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weight/esm-main/examples/README.md

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+# What's in this directory
+
+* The notebooks are introduced and summarized in `../README.md`
+* `data/some_proteins.fasta` and its smaller version, `data/few_proteins.fasta` are a random selection of UniRef50 sequences used in the second example of `../README.md`
+* `data/1a3a_1_A.a3m`, `data/1xcr_1_A.a3m`, `data/5ahw_1_A.a3m` are MSAs distributed with trRosetta, used in `contact_prediction.ipynb`
+* `data/P62593.fasta` is introduced and used in `sup_variant_prediction.ipynb`
+* Example MSAs genereated in the same way as the MSAs used for MSA Transformer pre-training:
+  - `data/UniRef50_E9K9Y4.a3m`, `data/UniRef50_UPI0003108055.a3m`, `data/UniRef50_UPI0003674933.a3m`, from the same sequences as trRosetta:
+  `data/hhblits_uniclust_2017_10_1a3a_1_A.a3m`, `data/hhblits_uniclust_2017_10_1xcr_1_A.a3m`, `data/hhblits_uniclust_2017_10_5ahw_1_A.a3m`.
+  - Generated with: `hhblits -i UniRef50_$id.fas -oa3m UniRef50_$id.a3m -n 3 -d /uniclust30_2017_10/uniclust30_2017_10`.
+* `esm2_infer_fairscale_fsdp_cpu_offloading.py` shows how to load the ESM-2 15B model with Fairscale's FSDP's CPU offloading capability on a single GPU

weight/esm-main/examples/data/some_proteins.fasta

@@ -0,0 +1,30 @@
+>UniRef50_A0A1E3NP16
+AVIYYRFRSQKPDHIATIKFDGTGLTVFELKRDIILANNLLHSTDVDIVLYSTEDIQDTKSWGYQNGGSSSAGERELDDDNEVVPRSTTVLVRRTMTPKKNKGNVQRYVAGKPRLQVSGTNSVNKSISLGNNVGGTMNFGDAATNGDEDDMIKKMFSVQDEQWSQQQDVMATATRVDNFRTNVNEPVPEYYICYKCGEKGKHHIKNCPKNNDPNWEGVRVRKTTGIPKSHLKAIENPEDTIRDSNSSGNTTYMVNDEGKYVVAVADTKAWEKYQKTKKGESGGYLNGDVDVDDGELKDPETGKLWKSPVRIPCCNKIFSRKIIEDKLIDSDFTCPSCGKEQIYLDTLVADEELQAKVDEYVKNLSENKNNDGNSPKRRQVNPAGATANTSQLPQIPMMPMPPINMQMPPMNIGMPPFMPFMPMPGMNP
+>UniRef50_UPI000836A30F
+MTLRTLLALSILALAAAATVQARPGAPPCSPLGLKYQPGACEKWKREHPDVNPDGVVQTVTIVNNSSSVLGGYVTFWHANNEHTDVDLPGVKPGETWTANGSWTVGQAPYYLLYSSFQDSYGDPVTFYAVPISKYPALAKKLPPEPDNCQSNHFRMVFGDGPQYVYEQHSGAVVTGGQTDKNTLCQILGCPTGGSTAGGLNTQNRTTSRSAPQPVYFRSCDP
+>UniRef50_A0A2G8L8Y3
+MAQKRYEENLSPYAELFRSKLIEDFHLLESFDEHGKSDAPKYYSKDFEDPARQDKMMLENPHGLVKFQVYSRKEPGEHFMLVLILSNSIALGLQAEVSESDDPKFAGLKLALDIFDYCSLFLFMVEIILKWIDNFWSFWSDNWNIFDFAVTVGSFVPEIINFFAGDIGGSMVRVIVRNLRVFRILRSLKMVSRFRQVRLIALAIGKAFSAITFIMLLLFTFLYIFAITGIIFFDTYTRSERQDLKYKDSFRSLPRAMITLFQLFTLDQWYKLLNDMWKVMDSMIPLGYIILWICIGSFIFRNVFVGIMVNNFQSIRNDLFEEVKEQEAARQIIQDTEKFNEELSRQEKKLNANRRGTLYQSPTVQPPKPNQPQPSQLAGLDNSETDEQSVSQEDESNTDGQTSLSGTDSYDLLGESSDSLFRRSSDGMIDKDKLSTNWEKTVHDNLTLLTSTPSETLWPRDTLFRYFQLMESLMENLQERQDLQDLAYHSLLQIFDSFDTSA
+>UniRef50_A0A1D5ZRM3
+MPCVAHECHPRLPAANHCRSLSCLGTPAAGWSSGDDDREEDELDTKQVILNEMRNREMRKRSSRCSVDSPTLSGAFAWSFTPLHPRSSIEKVSCTEEEKEAASDSDNESEAFFSVKSFFTRSTSRAATVASSTDMDPPATWEGLRGCEGWPFGLCP
+>UniRef50_UPI0003108055
+MPADAREYLESKHATRRFDRPAEVAGVVAFLLSDDTSFVIGAGYLVDGGYTALRAARGRLGPRRQAAPAVKLLPNTDSPR
+>UniRef50_A0A223SCH7
+MGPPRWWKGITGLAAVVHRADPEDKADLYAKMGLYLEYHPETRIVEARIKPRLHDVCESKVSEGGLEPPCP
+>UniRef50_A0A090SUK6
+MKTPEDRVFRATDEYSDFVMACRYKGNEREFVIASHDKLNEAQVETLTSYLSGEWFKKTYITGIMNDSDGVLSQHEEYGDEVFCQPLDELRVDRYIMMV
+>UniRef50_V4AGU2
+MHLGSIYLMVVLLIYFAYTDDRKERENVDVINPEENLVQDDEQYVGDSTENIEKSGSEEEEEDKEAIEEEEDEEEELNYRYIPEPAQDIVDNGKKYIQVHCSFKQDESLIRHPSNCSRYFVCSYGVVEEMPVCDDGEVFSIQVSECVKKGSENDDCDKLPFDSPPEITRGTQPSLIWHPRHKSPRSQFRQPTTLQMKVPHIELESFTCSATGKVLSHHSENCAWYYNCSAHPDAVMQTFYSGFIMECPYPQLFSTETKQCEDFEDVKCGDRYEPKSPCDYRANHCHETSHCIPCWVRYASCLELPDGLNPWSELEWKPFFVECYKERTVFQGVCDKSAVFSPLTRACETPYSIPRQHGGWRPVCDGRRDGIYADEYGRCDIYYVCKGYIFTGFFRCEKGEMFNPVISICQKPEAVPYPCGDLEMPNICESSLNGYHLDMFGRCTHYFECKDQQLEGISMCPSGIFNPELQICESSRDQPKPCGNLTNLCTHKNDGFHSDENDCTKVFQCERGLTMTSYDCSGSVRTECDVCNTPTECNDKPNGLYPNLKEGVGYYYDCVRSQIQNHYKCDKEKGGPIFNPVKQRCFYPEDLCKEVFSLKIAW
+>UniRef50_R5PKX9
+MQGGEQDVFEHRQVREKVIALKNHADAAAQGAAEFERLSFEQDVAALDGFETDQAAQKRGFAAARRPENHRDFFVVKREVDAVENHSVAELLYETARFQNNVIGHFYAFHFFSRALAASDTGQHARK
+>UniRef50_UPI0003674933
+MKKKEDILNLEHTLLPWMGRTMKVLDYFIGDFLNLKGIELTKVQWILLKKLNEQNGQPQQNLAFLTNRDKASLARLITTMEKKKLVERIPSKIDGRINHIFITKHGCEILQKSAPVIEKVVGCIQEGISPEEIETVIKVMQKVNNNINRASN
+>UniRef50_E9K9Y4
+MADNVLMAYHIVHDPDERAKHVLNTKKLYKWRITEKTKGTPVVGNVALVQTQFAKRTPVMIYATKEVANDLSDLQPVKVFTNNRDQETVNQTFDDLMR
+>UniRef50_A0A2C9LWN7
+MGKITGLLFFLFTSVRVTPSMKNTVNDIYRVRKDVLTKYRNTEEYDLGQRKGPLKQMDTRETRTPYGHFNSDTAFISPKNILKATRPLAFHLGNLKNQTSPCSTWNLTVDCSYKSLDHIESSWFPSNTTVLLLNNNKLVTLHNETFAQLTNLTRLDLSSNDIRRIDAGAFQGLHNLQELNLHMHCCNFTDHYSLESVFAPLRNLRILNAMHNSDVGVLTYSYTFLTRLPLLQSLSIDFDLDTLYCGPEFNDLKNLTFLQFSGQVMYIDDRSFQNVAQLKNLSMDHLSNINNISHNAFKPLSNLKVLTMYHVLLYVQEILSLLEPFQGRNMTEITLDTTTRTLTQVNPTRNGILTNHDTKYLMNICLESFTLIDNRIFYIKPDAVQNIYTWKKCLMHLYIASNPIQGNNFALIRLFTLDNLKSFTFINMFRACHEFQPFPQSSPPATRNVASSSISSQNNHYQKDTTSNQQQMIRHSPFSPYLDMIDENPYQLNIPNYIFISPSLQYMNFQRLVMSQSFEYHFILVGAQNVTSLDISDSGFYRFNGLMEGVSAIKTLIISGNDVSVLSVSFFDTFVSLENFAISSCKLDRDFISLNSRRIFQNHTRLQELDISSNSLNYLSQNTFSYNNRLMWLNMSGNQFKDIPFDLTNTPELQFLDIRFNSLTTIDETTAQQMDHLVTKSGKLEILLEGNVLSCSCSDLSFMRWMRMTLVTFDQNGNFTCMNTDGERKYTLDYSNLDSLWRECWGSFFLYFALIMLCLYCIGVFAVFITMRNKNFIVSFFLQLFGGFKLHSRRDYPVGVYIGYSDKDYQFPCKELRSFIESSLKLKTFLIDRDLIASVDKASGIIEALNASWRILLVCSKSFLKEDDWSMFTMRSAIYTQTPANPARVVVLVHKDCLPLLPPALLSSVNDENICAVSEWAMNYEMMQMLTTRLH
+>UniRef50_Q9REE6
+MSLRGRELLTSEERLELVRIPEDISEQELGRNFTLSNFDLELIKNRRRDYNRLGFAVQLCVLRFPGWSLNDAEPIPKKVLQHLARQLHVDPDCFSLYSSREA
+>UniRef50_A0A226D4M8
+MQKVINFPIWRRYYFSECGNCNDFRPDGVKKVHPPQEKSRTMDDEILAAPEVTIPFEPSDPSEVVVNLISSEEEDDDDVIQIVEEKSVDKAERQRRRQKKKDLWAARKLQRNKGQNVPLQTAWQRGPRPQETSSFPTPPQQSGGAQQKPLSPILISTAGSPNTSGAPTPANVSQQPTPTPSFVHTTASTSTHPEISLNINSDLALLIRLGPDGRPILTRVENVEQNNTSTSTPTTTRKLPPAPPPPKISFDTQTGESLLNGELITRPIIDITTDSPPSITHAATVSPQTSRTSGPPTLSPISPPPRTTQSNPAHPPPRYEPRKSRHPPRDPLASSSSSSSSSPSPPPTSRARHTSSANIPPPLEPLFLNTTQLIHLIKTCRKCEKSFPTRCDGVIHQKKEHNRKHCPVCFLTLSRHGNTYKDHLNMYHALEGDKEMVVCPFCAVEHSFDGLYNHIGRSHLIPVESKGESEHEVIFSVAPSPQSNGHQTRSVTQGNTPPKNDTNSPPNLEKRRPGPASKTRKTTNDPVPSTSRTGLICHKYVPDVETPPSKAGRNFESRAGRNFESRNVYPPATNRLNPPRNKSPPRNKNLPRNKSPPRNKSLPRNKTPPPSSSRSSSSRSVSNNLRRKNPTPPPPTQPPPKKVAKPDEAGINEKIQAAIKAVNARVHIERSVHHPNNRSDREIPSTSRTVTSRHKVPTSKTSGNTSVRKDPSPPPPLQTPPKTTNLELSKVQKARIVEDVRSIVRDVRITRVLDDGEVPSTSRAVEEEKKKEEKKNETRARLPRSSRVGERSSGYFQMAEGIDFSPENPTPRSKDLKAIHISKLNLIYQQLKCYPDTSVIANVAKECGVEIPVVAKWFTKKHMEYCQKTQQRKRKRKPPELR
+>UniRef50_UPI000B82D8F0
+MGGLHLIELRNVNIEFDKKLIEDGTIKIYDGKITAIIGESGSGKTSLLYLLGLISSNHRYLYSFDDVTLDLSNDFEMSRIRKQKIGYIFQDNNLVENLTIFENIRLSATIAGINITDKEIKSYLEFVELGYIDSNHYPRKLSGGERQRVAIACALAKQPELILADEPTSALDTVNSEIIMGIFKKIAHKDKKKIVIATHNDRIYNEADVIYEIKNNKIQLVKGESSNESSKKEPEDYDNNVKLTPRFYFDYAIKTSRKGRFAKNLMIVLSAIAIAFSSVMYNFGDTFVKEQEKLMDAISDKEIFVVNMTAPLNTILDIDENLSIQDKDAELLRNISYVDTIYPYFEFRSIGYPLINETEASEGYIVVSKGQKEEKYTFAESKDNPYDKYVIIPYYPEQNLERRLKEKLSDESSDKVYISSQLAQLLGIENLKESVSLRVLTYVPIAQHETQMTVRPEGIVYEIDIDLSKVVELDLKIEGILDESVRNRYSNSGNNAIYVPYHKMQMILTQTQNSATIDTNLEYIEWRPSAFVVFAKSYNDVGLVIERVSSINPNFRAVSEYQDIESMNAIVKNTREIGLVIVIVILIIIFLLMSIIHMNHILDRKYEISLLKANGLTKIELTKLVSVESLRHVFLVSLISSVISLVVTKVMNLLFEEIA

weight/esm-main/examples/esm2_infer_fairscale_fsdp_cpu_offloading.py

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+# Copyright (c) Meta Platforms, Inc. and affiliates.
+
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+from fairscale.nn.data_parallel import FullyShardedDataParallel as FSDP
+from fairscale.nn.wrap import enable_wrap, wrap
+
+import esm
+
+# init the distributed world with world_size 1
+url = "tcp://localhost:23456"
+torch.distributed.init_process_group(backend="nccl", init_method=url, world_size=1, rank=0)
+
+# download model data from the hub
+model_name = "esm2_t48_15B_UR50D"
+model_data, regression_data = esm.pretrained._download_model_and_regression_data(model_name)
+
+# initialize the model with FSDP wrapper
+fsdp_params = dict(
+    mixed_precision=True,
+    flatten_parameters=True,
+    state_dict_device=torch.device("cpu"),  # reduce GPU mem usage
+    cpu_offload=True,  # enable cpu offloading
+)
+with enable_wrap(wrapper_cls=FSDP, **fsdp_params):
+    model, vocab = esm.pretrained.load_model_and_alphabet_core(
+        model_name, model_data, regression_data
+    )
+    batch_converter = vocab.get_batch_converter()
+    model.eval()
+
+    # Wrap each layer in FSDP separately
+    for name, child in model.named_children():
+        if name == "layers":
+            for layer_name, layer in child.named_children():
+                wrapped_layer = wrap(layer)
+                setattr(child, layer_name, wrapped_layer)
+    model = wrap(model)
+
+data = [
+    ("protein1", "MKTVRQERLKSIVRILERSKEPVSGAQLAEELSVSRQVIVQDIAYLRSLGYNIVATPRGYVLAGG"),
+    ("protein2", "KALTARQQEVFDLIRDHISQTGMPPTRAEIAQRLGFRSPNAAEEHLKALARKGVIEIVSGASRGIRLLQEE"),
+    (
+        "protein2 with mask",
+        "KALTARQQEVFDLIRD<mask>ISQTGMPPTRAEIAQRLGFRSPNAAEEHLKALARKGVIEIVSGASRGIRLLQEE",
+    ),
+    ("protein3", "K A <mask> I S Q"),
+]
+
+batch_labels, batch_strs, batch_tokens = batch_converter(data)
+batch_tokens = batch_tokens.cuda()
+with torch.no_grad():
+    results = model(batch_tokens, repr_layers=[48], return_contacts=True)
+print(results)

weight/esm-main/examples/inverse_folding/README.md

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+# Inverse folding with ESM-IF1
+
+The ESM-IF1 inverse folding model is built for predicting protein sequences 
+from their backbone atom coordinates. We provide scripts here 1) to sample sequence 
+designs for a given structure and 2) to score sequences for a given structure. 
+
+Trained with 12M protein structures predicted by AlphaFold2, the ESM-IF1
+model consists of invariant geometric input processing layers followed by a
+sequence-to-sequence transformer, and achieves 51% native sequence recovery on
+structurally held-out backbones with 72% recovery for buried residues.
+The model is also trained with span masking to tolerate missing backbone
+coordinates and therefore can predict sequences for partially masked structures.
+
+More details in our bioRxiv [pre-print](https://doi.org/10.1101/2022.04.10.487779).
+
+![Illustration](illustration.png)
+
+## Recommended environment
+It is highly recommended to start a new conda environment from scratch due to
+potential CUDA compatability issues between pytorch and the pytorch-geometric
+package required for the inverse folding model.
+
+To set up a new conda environment with required packages,
+
+```
+conda create -n inverse python=3.9
+conda activate inverse
+conda install pytorch cudatoolkit=11.3 -c pytorch
+conda install pyg -c pyg -c conda-forge
+conda install pip
+pip install biotite
+pip install git+https://github.com/facebookresearch/esm.git
+```
+
+## Quickstart
+
+### Sample sequence designs for a given structure
+To sample sequences for a given structure in PDB or mmCIF format, use the
+`sample_sequences.py` script. The input file can have either `.pdb` or
+`.cif` as suffix.
+
+For example, to sample 3 sequence designs for the golgi casein kinase structure
+(PDB [5YH2](https://www.rcsb.org/structure/5yh2); [PDB Molecule of the Month
+from January 2022](https://pdb101.rcsb.org/motm/265)), we can run the following
+command from the `examples/inverse_folding` directory:
+```
+python sample_sequences.py data/5YH2.pdb \
+    --chain C --temperature 1 --num-samples 3 \
+    --outpath output/sampled_sequences.fasta
+```
+
+The sampled sequences will be saved in a fasta format to the specified output file.
+
+**By default, the script only loads the backbone of the specified target chain 
+as model input.** To instead use the entire complex backbone as model input for 
+conditioning, use the `--multichain-backbone` flag to load all chains. (In the
+example below, the encoder loads the backbone of all chains as input to the
+encoder, and the decoder samples sequences for chain C.)
+```
+python sample_sequences.py data/5YH2.pdb \
+    --chain C --temperature 1 --num-samples 3 \
+    --outpath output/sampled_sequences_multichain.fasta \
+    --multichain-backbone
+```
+
+The temperature parameter controls the sharpness of the probability
+distribution for sequence sampling. Higher sampling temperatures yield more
+diverse sequences but likely with lower native sequence recovery.
+The default sampling temperature is 1. To optimize for native sequence
+recovery, we recommend sampling with low temperature such as 1e-6.
+
+**We recommend trying both the single-chain and multi-chain design modes.** While in
+our paper we showed that conditioning on the entire multi-chain backbone often
+reduces perplexity and increases sequence recovery, on some proteins the
+single-chain performance is better.
+
+Sometimes, one failure mode in sampled sequences is a high number of repeated
+amino acids, e.g. `EEEEEEEE`. We recommend checking for that and filtering out
+sampled sequences with long repeats.
+
+### Scoring sequences
+To score the conditional log-likelihoods for sequences conditioned on a given
+structure, use the `score_log_likelihoods.py` script.
+
+For example, to score the sequences in `data/5YH2_mutated_seqs.fasta`
+according to the structure in `data/5YH2.pdb`, we can run
+the following command from the `examples/inverse_folding` directory:
+```
+python score_log_likelihoods.py data/5YH2.pdb \
+    data/5YH2_mutated_seqs.fasta --chain C \
+    --outpath output/5YH2_mutated_seqs_scores.csv
+```
+
+The conditional log-likelihoods are saved in a csv format in the specified output path. 
+The output values are the average log-likelihoods averaged over all amino acids in a sequence.
+
+**By default, the script only loads the backbone of the specified target chain 
+as model input.** To instead use the entire complex backbone as model input for 
+conditioning, use the `--multichain-backbone` flag to load all chains. (In the
+example below, the encoder loads the backbone of all chains as input to the
+encoder, and the decoder scores sequences for chain C.)
+```
+python score_log_likelihoods.py data/5YH2.pdb \
+    data/5YH2_mutated_seqs.fasta --chain C \
+    --outpath output/5YH2_mutated_seqs_scores.csv \
+    --multichain-backbone
+```
+
+We recommend trying both the single-chain and multi-chain design modes. While in
+our paper we showed that conditioning on the entire multi-chain backbone often
+reduces perplexity and increases sequence recovery, on some proteins the
+single-chain performance is better.
+
+## General usage
+
+### Load model
+The `esm_if1_gvp4_t16_142M_UR50` function loads the pretrained model and its
+corresponding alphabet. The alphabet represents the amino acids and the special
+tokens encoded by the model.
+
+**Update**: It is important to set the model in eval mode to avoid random
+dropout from training mode for best performance.
+
+```
+import esm.inverse_folding
+model, alphabet = esm.pretrained.esm_if1_gvp4_t16_142M_UR50()
+model = model.eval()
+```
+
+### Input format
+The input to the model is a list of backbone atom coordinates for the N, CA, C
+atoms in each amino acid. For each structure, the coordinate list `coords` would
+be of shape L x 3 x 3, where L is the number of amino acids in the structure. 
+`coords[i][0]` is the 3D coordinate for the N atom in amino acid `i`, 
+`coords[i][1]` is the 3D coordinate for the CA atom in amino acid `i`, and
+`coords[i][2]` is the 3D coordinate for the C atom in amino acid `i`. 
+
+### Load input data from PDB and mmCIF file formats
+To load a single chain from PDB and mmCIF file formats and extract the backbone
+coordinates of the N, CA, C atoms as model input,
+```
+import esm.inverse_folding
+structure = esm.inverse_folding.util.load_structure(fpath, chain_id)
+coords, seq = esm.inverse_folding.util.extract_coords_from_structure(structure)
+```
+Note this only loads the specified chain.
+
+To load multiple chains for the multichain complex use cases, list all chain ids
+when loading the structure, e.g. `chain_ids = ['A', 'B', 'C']`:
+```
+structure = esm.inverse_folding.util.load_structure(fpath, chain_ids)
+coords, native_seqs = esm.inverse_folding.multichain_util.extract_coords_from_complex(structure)
+```
+
+### Example Jupyter notebook
+See `examples/inverse_folding/notebook.ipynb` for examples of sampling sequences, 
+calculating conditional log-likelihoods, and extracting encoder output as
+structure representation (on a single chain).
+
+This notebook is also available on colab:
+  
+[<img src="https://colab.research.google.com/assets/colab-badge.svg">](https://colab.research.google.com/github/facebookresearch/esm/blob/master/examples/inverse_folding/notebook.ipynb)
+
+For multichain complexes, ESM-IF1 can design sequences for a specific chain in the complex,
+conditioned on the backbone structure of the entire multichain complex.
+
+See `examples/inverse_folding/notebook_multichain.ipynb` for sequence design and sequence scoring in multichain complexes, or find the notebook on colab:
+
+[<img src="https://colab.research.google.com/assets/colab-badge.svg">](https://colab.research.google.com/github/facebookresearch/esm/blob/master/examples/inverse_folding/notebook_multichain.ipynb)
+
+### Sample sequence designs
+To sample sequences for a given set of backbone coordinates for a single chain,
+```
+sampled_seq = model.sample(coords, temperature=T)
+```
+where `coords` is an array as described in the above section on input format.
+
+To sample sequences for a given chain in a multichain complex,
+```
+import esm.inverse_folding
+sampled_seq = esm.inverse_folding.multichain_util.sample_sequence_in_complex(
+    model, coords, target_chain_id, temperature=T
+)
+```
+where `coords` is a dictionary mapping chain ids to backbone coordinate arrays.
+
+The temperature parameter controls the ``sharpness`` of the probability
+distribution for sequence sampling. Higher sampling temperatures yield more
+diverse sequences but likely with lower native sequence recovery.
+The default sampling temperature is `T=1`. To optimize for native sequence
+recovery, we recommend sampling with low temperature such as `T=1e-6`.
+
+### Scoring sequences
+To score the conditional log-likelihoods for sequences conditioned on a given
+set of backbone coordinates for a single chain, use the `score_sequence` function,
+```
+ll_fullseq, ll_withcoord = esm.inverse_folding.util.score_sequence(model, alphabet, coords, seq)
+```
+
+The first returned value ``ll_fullseq`` is the average log-likelihood averaged
+over all amino acids in a sequence.
+The second return value ``ll_withcoord`` is averaged only over those amino acids
+with associated backbone coordinates in the input, i.e., excluding those with
+missing backbone coordinates.
+
+For multichain complexes,
+```
+ll_fullseq, ll_withcoord = esm.inverse_folding.multichain_util.score_sequence_in_complex(
+    model, alphabet, coords, target_chain_id, target_seq
+)
+```
+where `coords` is a dictionary mapping chain ids to backbone coordinate arrays. 
+
+### Partially masking backbone coordinates
+To mask a parts of the input backbone coordinates, simply set those coordinate
+values to `np.inf`. For example, to mask the backbone coordinates for the first
+ten amino acid in the structure,
+```
+coords[:10, :] = float('inf')
+```
+
+### Encoder output as structure representation
+To extract the encoder output as structure representation,
+```
+rep = esm.inverse_folding.util.get_encoder_output(model, alphabet, coords)
+```
+For a set of input coordinates with L amino acids, the encoder output will have
+shape L x 512.
+
+Or, for multichain complex,
+```
+rep = esm.inverse_folding.multichain_util.get_encoder_output_for_complex(
+    model, alphabet, coords, target_chain_id
+)
+```
+
+## Data split
+
+The CATH v4.3 data are available at the following links:
+- [Backbone coordinates and sequences](https://dl.fbaipublicfiles.com/fair-esm/data/cath4.3_topologysplit_202206/chain_set.jsonl)
+- [Split](https://dl.fbaipublicfiles.com/fair-esm/data/cath4.3_topologysplit_202206/splits.json)
+
+That's it for now, have fun!
+
+## Acknowledgements
+The invariant geometric input processing layers are from the [Geometric Vector
+Perceptron PyTorch repo](https://github.com/drorlab/gvp-pytorch) by Bowen Jing,
+Stephan Eismann, Pratham Soni, Patricia Suriana, Raphael Townshend, and Ron
+Dror.
+
+The input data pipeline is adapted from the [Geometric Vector Perceptron PyTorch
+repo](https://github.com/drorlab/gvp-pytorch) and the [Generative Models for
+Graph-Based Protein Design
+repo](https://github.com/jingraham/neurips19-graph-protein-design) by John
+Ingraham, Vikas Garg, Regina Barzilay, and Tommi Jaakkola.
+
+The Transformer implementation is adapted from
+[fairseq](https://github.com/pytorch/fairseq).

weight/esm-main/examples/inverse_folding/data/5YH2_mutated_seqs.fasta

@@ -0,0 +1,8 @@
+>mut_1
+SVLQSLFEHPLYRTVLPDLTEEDTLFNLNAEIRLYPKAASESYPNWLRFHIGINRYELYSRHNPVIAALLRDLLSQKISSVGMKSGGTQLKLIMSFQNYGQALFKPMKQTREQETPPDFFYFSDFERHNAEIAAFHLDRILDFRRVPPVAGRLVNMTREIRDVTRDKKLWRTFFVSPANNICFYGECSYYCSTEHALCGKPDQIEGSLAAFLPDLALAKRKTWRNPWRRSYHKRKKAEWEVDPDYCDEVKQTPPYDRGTRLLDIMDMTIFDFLMGNMDRHHYETFEKFGNDTFIIHLDNGRGFGKHSHDEMSILVPLTQCCRVKRSTYLRLQLLAKEEYKLSSLMEESLLQDRLVPVLIKPHLEALDRRLRLVLKVLSDCVEKDGFSAVVENDLD
+>mut_2
+DVLQSLFEHPLYRTVLPDLTEEDTLFNLNAEIRLYPKAASESYPNWLRFHIGINRYELYSRHNPVIAALLRDLLSQKISSVGMKSGGTQLKLIMSFQNYGQALFKPMKQTREQETPPDFFYFSDFERHNAEIAAFHLDRILDFRRVPPVAGRLVNMTREIRDVTRDKKLWRTFFVSPANNICFYGECSYYCSTEHALCGKPDQIEGSLAAFLPDLALAKRKTWRNPWRRSYHKRKKAEWEVDPDYCDEVKQTPPYDRGTRLLDIMDMTIFDFLMGNMDRHHYETFEKFGNDTFIIHLDNGRGFGKHSHDEMSILVPLTQCCRVKRSTYLRLQLLAKEEYKLSSLMEESLLQDRLVPVLIKPHLEALDRRLRLVLKVLSDCVEKDGFSAVVENDLD
+>mut_3
+EVLQSLFEHPLYRTVLPDLTEEDTLFNLNAEIRLYPKAASESYPNWLRFHIGINRYELYSRHNPVIAALLRDLLSQKISSVGMKSGGTQLKLIMSFQNYGQALFKPMKQTREQETPPDFFYFSDFERHNAEIAAFHLDRILDFRRVPPVAGRLVNMTREIRDVTRDKKLWRTFFVSPANNICFYGECSYYCSTEHALCGKPDQIEGSLAAFLPDLALAKRKTWRNPWRRSYHKRKKAEWEVDPDYCDEVKQTPPYDRGTRLLDIMDMTIFDFLMGNMDRHHYETFEKFGNDTFIIHLDNGRGFGKHSHDEMSILVPLTQCCRVKRSTYLRLQLLAKEEYKLSSLMEESLLQDRLVPVLIKPHLEALDRRLRLVLKVLSDCVEKDGFSAVVENDLD
+>mut_4
+VVLQSLFEHPLYRTVLPDLTEEDTLFNLNAEIRLYPKAASESYPNWLRFHIGINRYELYSRHNPVIAALLRDLLSQKISSVGMKSGGTQLKLIMSFQNYGQALFKPMKQTREQETPPDFFYFSDFERHNAEIAAFHLDRILDFRRVPPVAGRLVNMTREIRDVTRDKKLWRTFFVSPANNICFYGECSYYCSTEHALCGKPDQIEGSLAAFLPDLALAKRKTWRNPWRRSYHKRKKAEWEVDPDYCDEVKQTPPYDRGTRLLDIMDMTIFDFLMGNMDRHHYETFEKFGNDTFIIHLDNGRGFGKHSHDEMSILVPLTQCCRVKRSTYLRLQLLAKEEYKLSSLMEESLLQDRLVPVLIKPHLEALDRRLRLVLKVLSDCVEKDGFSAVVENDLD

weight/esm-main/examples/inverse_folding/output/5YH2_mutated_seqs_scores.csv

@@ -0,0 +1,5 @@
+seqid,log_likelihood
+mut_1,-1.611882209777832
+mut_2,-1.615426778793335
+mut_3,-1.614095687866211
+mut_4,-1.6152666807174683

weight/esm-main/examples/inverse_folding/output/sampled_sequences.fasta

@@ -0,0 +1,6 @@
+>sampled_seq_1
+AAVTRWWSTPVTSVQDPGRNNDDLLFDKNEMLKLLPSTDSSGYPNHIAFWEQINRYKLYDEVSPTIQNLDLEMRNLQVRTVSQLSNGRTLKLHFSFSDNGKALFKPMVSLTEDENPIDDATWEYFETSRAVIAAYKLDKLLNLRNVPPAAGRSVHIISQIRDVSKDQNLDNTFHETEDNKLCFWGKCHDRCDEASAICGNPNSVDGVIVAQLPDSIFAEQENHNSPWAHSCKTDVTTWQAVNPAFCNDIFKTKPLRHGDFKLALADNYLFAFLQGNCDDHSYMEFRSFGTDTRILMLDNGYGFGRYSYNNKDILQPLQQCAQVRQSSKVKLDLLNETIFDLRVLMEEVLSHDLSFPILYSPYLKRLNRNLQIVVEEFELSRQRVGVNNTYKMNHE
+>sampled_seq_2
+VKFDAWLSNPITTGKVPGLTETDFLFKPQEMEALYPTLEGEPLPDHIRFHVQINRYFMYDKDSPVIKRLLRRLEKAAIKSVGQLKGGDQLVLEFRFEDFGRALFKPKLNSLEEEVPPQFDWFERHDTCQAEVASYELDQILDLHNCPPTQARPIDFQKDLWNVSDDKELLSTANVTPWNQLCFYGKNKFQSSHETAICGTYDVIDGAVQAQLPHQIIAPRSDNHNPWARTKHQYIDSSWQTNPTYYDYIRTQPYHQTKYIVGTMTNEYCHYFLEGNADRHHYKTWTKFGEDQNLIALDTGRGFGRKNFMDLDILKPLSQGKWMSEQAYQRKKKLMSRDYSLAELMNESLSESALYPVLSEPYLIQLDTRLQIILQAMEFTIKEKGIDLFLRKEFD
+>sampled_seq_3
+CVARRWWAHPTTGEADPPQSAADLLFKTVDAYTMLPREARQELPHHLKFHRQIEKTRLYATKSPTVTALLQDLQSTKILRVSQFSGGRSLRLRFVFDDGGSSAFKPLVSELHAEVPPHWYNFQRSDVAQSLIASYHLNRVLDLRMTPPCAARLVDLVKELRDVSDDAELLSTFFVTPEHELAFFGTSYYRSGLETALVGRPRTVAGSHIAELPDETLSPRGSFDCPWAYSEKDRVSTYWSFDPKASARSDKSPQYRFGTILLNLANTHVFNFLMGNKDAHNFDTFTAFGKDQFAVQIDNGCGFGRYSHMDRDVLVPLTQSVVVRRRLYDRLRSLAQEEYGMRDLLAEVLAQDKCYPVLVQPFLDRLDETLNLVLDVMDQNRKLRGDDRVLLMESS

weight/esm-main/examples/inverse_folding/sample_sequences.py

@@ -0,0 +1,124 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+#
+# Sample sequences based on a given structure (multinomial sampling, no beam search).
+#
+# usage: sample_sequences.py [-h] [--chain CHAIN] [--temperature TEMPERATURE]
+# [--outpath OUTPATH] [--num-samples NUM_SAMPLES] pdbfile
+
+import argparse
+import numpy as np
+from pathlib import Path
+import torch
+
+import esm
+import esm.inverse_folding
+
+
+def sample_seq_singlechain(model, alphabet, args):
+    if torch.cuda.is_available() and not args.nogpu:
+        model = model.cuda()
+        print("Transferred model to GPU")
+    coords, native_seq = esm.inverse_folding.util.load_coords(args.pdbfile, args.chain)
+    print('Native sequence loaded from structure file:')
+    print(native_seq)
+
+    print(f'Saving sampled sequences to {args.outpath}.')
+
+    Path(args.outpath).parent.mkdir(parents=True, exist_ok=True)
+    with open(args.outpath, 'w') as f:
+        for i in range(args.num_samples):
+            print(f'\nSampling.. ({i+1} of {args.num_samples})')
+            sampled_seq = model.sample(coords, temperature=args.temperature, device=torch.device('cuda'))
+            print('Sampled sequence:')
+            print(sampled_seq)
+            f.write(f'>sampled_seq_{i+1}\n')
+            f.write(sampled_seq + '\n')
+
+            recovery = np.mean([(a==b) for a, b in zip(native_seq, sampled_seq)])
+            print('Sequence recovery:', recovery)
+
+
+def sample_seq_multichain(model, alphabet, args):
+    if torch.cuda.is_available() and not args.nogpu:
+        model = model.cuda()
+        print("Transferred model to GPU")
+    structure = esm.inverse_folding.util.load_structure(args.pdbfile)
+    coords, native_seqs = esm.inverse_folding.multichain_util.extract_coords_from_complex(structure)
+    target_chain_id = args.chain
+    native_seq = native_seqs[target_chain_id]
+    print('Native sequence loaded from structure file:')
+    print(native_seq)
+    print('\n')
+
+    print(f'Saving sampled sequences to {args.outpath}.')
+
+    Path(args.outpath).parent.mkdir(parents=True, exist_ok=True)
+    with open(args.outpath, 'w') as f:
+        for i in range(args.num_samples):
+            print(f'\nSampling.. ({i+1} of {args.num_samples})')
+            sampled_seq = esm.inverse_folding.multichain_util.sample_sequence_in_complex(
+                    model, coords, target_chain_id, temperature=args.temperature)
+            print('Sampled sequence:')
+            print(sampled_seq)
+            f.write(f'>sampled_seq_{i+1}\n')
+            f.write(sampled_seq + '\n')
+
+            recovery = np.mean([(a==b) for a, b in zip(native_seq, sampled_seq)])
+            print('Sequence recovery:', recovery)
+
+
+def main():
+    parser = argparse.ArgumentParser(
+            description='Sample sequences based on a given structure.'
+    )
+    parser.add_argument(
+            'pdbfile', type=str,
+            help='input filepath, either .pdb or .cif',
+    )
+    parser.add_argument(
+            '--chain', type=str,
+            help='chain id for the chain of interest', default=None,
+    )
+    parser.add_argument(
+            '--temperature', type=float,
+            help='temperature for sampling, higher for more diversity',
+            default=1.,
+    )
+    parser.add_argument(
+            '--outpath', type=str,
+            help='output filepath for saving sampled sequences',
+            default='output/sampled_seqs.fasta',
+    )
+    parser.add_argument(
+            '--num-samples', type=int,
+            help='number of sequences to sample',
+            default=1,
+    )
+    parser.set_defaults(multichain_backbone=False)
+    parser.add_argument(
+            '--multichain-backbone', action='store_true',
+            help='use the backbones of all chains in the input for conditioning'
+    )
+    parser.add_argument(
+            '--singlechain-backbone', dest='multichain_backbone',
+            action='store_false',
+            help='use the backbone of only target chain in the input for conditioning'
+    )
+    parser.add_argument("--nogpu", action="store_true", help="Do not use GPU even if available")
+  
+    args = parser.parse_args()
+
+    model, alphabet = esm.pretrained.esm_if1_gvp4_t16_142M_UR50()
+    model = model.eval()
+
+    if args.multichain_backbone:
+        sample_seq_multichain(model, alphabet, args)
+    else:
+        sample_seq_singlechain(model, alphabet, args)
+
+
+if __name__ == '__main__':
+    main()

weight/esm-main/examples/inverse_folding/score_log_likelihoods.py

@@ -0,0 +1,131 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+#
+# Scores sequences based on a given structure.
+#
+# usage:
+# score_log_likelihoods.py [-h] [--outpath OUTPATH] [--chain CHAIN] pdbfile seqfile
+
+import argparse
+from biotite.sequence.io.fasta import FastaFile, get_sequences
+import numpy as np
+from pathlib import Path
+import torch
+import torch.nn.functional as F
+from tqdm import tqdm
+
+import esm
+import esm.inverse_folding
+
+
+def score_singlechain_backbone(model, alphabet, args):
+    if torch.cuda.is_available() and not args.nogpu:
+        model = model.cuda()
+        print("Transferred model to GPU")
+    coords, native_seq = esm.inverse_folding.util.load_coords(args.pdbfile, args.chain)
+    print('Native sequence loaded from structure file:')
+    print(native_seq)
+    print('\n')
+
+    ll, _ = esm.inverse_folding.util.score_sequence(
+            model, alphabet, coords, native_seq) 
+    print('Native sequence')
+    print(f'Log likelihood: {ll:.2f}')
+    print(f'Perplexity: {np.exp(-ll):.2f}')
+
+    print('\nScoring variant sequences from sequence file..\n')
+    infile = FastaFile()
+    infile.read(args.seqfile)
+    seqs = get_sequences(infile)
+    Path(args.outpath).parent.mkdir(parents=True, exist_ok=True)
+    with open(args.outpath, 'w') as fout:
+        fout.write('seqid,log_likelihood\n')
+        for header, seq in tqdm(seqs.items()):
+            ll, _ = esm.inverse_folding.util.score_sequence(
+                    model, alphabet, coords, str(seq))
+            fout.write(header + ',' + str(ll) + '\n')
+    print(f'Results saved to {args.outpath}') 
+
+
+def score_multichain_backbone(model, alphabet, args):
+    if torch.cuda.is_available() and not args.nogpu:
+        model = model.cuda()
+        print("Transferred model to GPU")
+    structure = esm.inverse_folding.util.load_structure(args.pdbfile)
+    coords, native_seqs = esm.inverse_folding.multichain_util.extract_coords_from_complex(structure)
+    target_chain_id = args.chain
+    native_seq = native_seqs[target_chain_id]
+    print('Native sequence loaded from structure file:')
+    print(native_seq)
+    print('\n')
+
+    ll, _ = esm.inverse_folding.multichain_util.score_sequence_in_complex(
+            model, alphabet, coords, target_chain_id, native_seq) 
+    print('Native sequence')
+    print(f'Log likelihood: {ll:.2f}')
+    print(f'Perplexity: {np.exp(-ll):.2f}')
+
+    print('\nScoring variant sequences from sequence file..\n')
+    infile = FastaFile()
+    infile.read(args.seqfile)
+    seqs = get_sequences(infile)
+    Path(args.outpath).parent.mkdir(parents=True, exist_ok=True)
+    with open(args.outpath, 'w') as fout:
+        fout.write('seqid,log_likelihood\n')
+        for header, seq in tqdm(seqs.items()):
+            ll, _ = esm.inverse_folding.multichain_util.score_sequence_in_complex(
+                    model, alphabet, coords, target_chain_id, str(seq))
+            fout.write(header + ',' + str(ll) + '\n')
+    print(f'Results saved to {args.outpath}') 
+
+
+def main():
+    parser = argparse.ArgumentParser(
+            description='Score sequences based on a given structure.'
+    )
+    parser.add_argument(
+            'pdbfile', type=str,
+            help='input filepath, either .pdb or .cif',
+    )
+    parser.add_argument(
+            'seqfile', type=str,
+            help='input filepath for variant sequences in a .fasta file',
+    )
+    parser.add_argument(
+            '--outpath', type=str,
+            help='output filepath for scores of variant sequences',
+            default='output/sequence_scores.csv',
+    )
+    parser.add_argument(
+            '--chain', type=str,
+            help='chain id for the chain of interest', default='A',
+    )
+    parser.set_defaults(multichain_backbone=False)
+    parser.add_argument(
+            '--multichain-backbone', action='store_true',
+            help='use the backbones of all chains in the input for conditioning'
+    )
+    parser.add_argument(
+            '--singlechain-backbone', dest='multichain_backbone',
+            action='store_false',
+            help='use the backbone of only target chain in the input for conditioning'
+    )
+    
+    parser.add_argument("--nogpu", action="store_true", help="Do not use GPU even if available")
+    
+    args = parser.parse_args()
+
+    model, alphabet = esm.pretrained.esm_if1_gvp4_t16_142M_UR50()
+    model = model.eval()
+
+    if args.multichain_backbone:
+        score_multichain_backbone(model, alphabet, args)
+    else:
+        score_singlechain_backbone(model, alphabet, args)
+
+
+
+if __name__ == '__main__':
+    main()

weight/esm-main/examples/lm-design/conf/config.yaml

@@ -0,0 +1,62 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+#
+seed: 0
+num_seqs: 1
+test_mode: False
+allow_missing_residue_coords: True
+suppress_AA: 'C'
+disable_cuda: False
+cuda_device_idx:  # Set to numberic value to override default GPU device used.
+task: free_generation  # fixedbb or free_generation
+pdb_fn:  # set as empty string when using free_generation
+free_generation_length: 100
+
+tasks:
+  free_generation:
+      num_iter: 170000
+      resample_y_every: 3
+      resample_y_temp: 1
+      stage_fixedbb_args: ${tasks.fixedbb}
+
+
+  fixedbb:
+      num_iter: 170000
+
+      # Accept/Reject
+      accept_reject:
+          energy_cfg:
+              struct_w: 3
+              LM_w: 2
+              ngram_w: 1
+              ngram_orders: [1,2,3]
+          temperature:
+              scheduler: StepLR
+              step_size: 10000
+              gamma: 0.5
+              initial: 8
+
+
+
+# Hydra config
+hydra:
+  job_logging:
+    formatters:
+      colorlog:
+        datefmt: "%m-%d %H:%M:%S"
+    handlers:
+      file:
+        class: logging.FileHandler
+        mode: w
+        filename: logging.l
+      console:
+        class: logging.StreamHandler
+        stream: ext://sys.stdout
+
+  hydra_logging:
+    handlers:
+      console:
+        class: logging.StreamHandler
+        stream: ext://sys.stdout  

weight/esm-main/examples/lm-design/paper-data/README.md

@@ -0,0 +1,74 @@
+Here we provide the data associated with the paper
+["Language models generalize beyond natural proteins" (2022)](https://doi.org/10.1101/2022.12.21.521521) by 
+Robert Verkuil\*, Ori Kabeli\*, Yilun Du, Basile I. M. Wicky, Lukas F. Milles, Justas Dauparas, David Baker, Sergey Ovchinnikov, Tom Sercu, and Alexander Rives.
+
+## Free Generations (Section 3)
+Designs from the Free Generations section of the paper (Section 3) along with their statistics and pdb files can be found at [free_generations_full.db](https://dl.fbaipublicfiles.com/fair-esm/examples/lm_design/free_generations_full.db) and can be loaded using:
+```
+import pandas as pd; pd.read_sql('free_generations_full', 'sqlite:///free_generations_full.db')
+```
+
+
+## Designs with wetlab validation (Section 2 and 3)
+* [data.csv](./data.csv) - Load scalar data in `data.csv` with `pd.read_csv`.
+* [data.hdf5](https://dl.fbaipublicfiles.com/fair-esm/examples/lm_design/design_lm_data_2022_v1.hdf5)
+For long-form data, download `data.hdf5` from [this link](https://dl.fbaipublicfiles.com/fair-esm/examples/lm_design/design_lm_data_2022_v1.hdf5) and load with `pd.read_hdf`.
+```
+# Design information
+Design ID - {F,G}{0-267} unique identifier for each (LM or AlphaFold) design evaluated. 8 Nan values correspond to 8 ground truth sequences tested.
+Experiment Name - Label for the testing pool to which the design / ground-truth sequence belongs. See Supplement; Section 1.6 for a full description of submitted sequences. These pools (minus ground-truth sequences) have experimental results shown in fig. S11.
+Design Model - 228x LM, 20x AlphaFold, 20x AF+ngram, 8x Ground Truth.
+Target ID - PDB ID of de novo target for all fixed backbone designs, 'Generation' for all free generations.
+Sequence - Designed sequence
+
+# In Silico Evaluation
+*AlphaFold predicted PDB file - Structure prediction from AlphaFold (5x pTM models, select best by pLDDT -> Amber Relax).
+AlphaFold RMSD - (AlphaFold-predicted) RMSD to target backbone for fixed backbone designs, Nan for free generations
+AlphaFold pLDDT - (AlphaFold-predicted) Avg pLDDT for the predicted structure
+
+# Experimental Evaluation
+# Results from experimental testing.  Final classifications are in the booleans: {Soluble, Success, Success+Monodisperse}.
+Total Yield - Actual total soluble yield (in mg) from the 4x1mL prep. (Actual yield is closer to ~2x, we can only inject 1/2 of the total product onto the column.)
+yield_per_Leq - Total Yield, adjusted to 1 L of culture equivalent
+*Elution Volume (mL) - Array of x-values for plotting of the SEC trace.
+*Chromatographic Absorbance at 280nm - Array of y-values for plotting of the SEC trace.
+*Elution Volume (mL) (raw) - Raw version, data is not truncated, lengths may differ between rows.
+*Chromatographic Absorbance at 280nm (raw) - Raw version, data is not truncated, lengths may differ between rows.
+Soluble - Total Yield > 0.05 mg.
+Success - Soluble and SEC peak at the expected elution volume.
+Success+Monodisperse - SEC peak *only* at the expected elution volume.
+
+# Jackhmmer results
+# See Supplement, Section 1.5 for verbose details.
+# In short: Summary statistics of Jackhmmer searches (-n 1 --seed 0) of the designed sequence against UniRef90.  Hits that were removed from ESM2's train set were removed from consideration here.  See `.txt` files for ID's of these omitted sequences.
+min Jackhmmer E-value - Minimum (best-domain) E-value
+max Jackhmmer Seq-id (significant hits only) - Maximum Sequence identity over all significant (best domain E-value < 1) hits.
+max Jackhmmer TM-score (top-10 hits only) - Maximum TM-score of the ≈top-10 (by best-domain E-value) hits.  (Purging was applied after top-10, so the number considered may be slightly lower, counts were rarely reduced below 7).
+
+(* denotes long-form data only available in data.hdf5)
+```
+
+## `artificial_sequence_purge_ids.txt`
+ID's of sequences removed due to being annotateed "artificial sequence" by the UniProt website when `2021_04` was the latest release.
+
+## `uniref90_jackhmmer_purge_ids.txt`
+ID's of sequences removed by Jackhmmer search (`-n 1 --seed 0`) of UniRef90 when given the de novo target set as queries.
+
+## Minimal structure projection
+A small new model head was constructed on top of ESM2, which is [this linear projection layer](https://dl.fbaipublicfiles.com/fair-esm/examples/lm_design/linear_projection_model.pt).
+For a given sequence the projection measures the compatibility of the internal representations of the language model with a structure.
+The linear projection layer is automatically downloaded when running the `lm_design` code.
+
+## Reference
+
+If using this work, please cite:
+```bibtex
+@article{verkuil2022language,
+  author={Robert Verkuil\*, Ori Kabeli\*, Yilun Du, Basile I. M. Wicky, Lukas F. Milles, Justas Dauparas, David Baker, Sergey Ovchinnikov, Tom Sercu, and Alexander Rives},
+  title={Language models generalize beyond natural proteins},
+  year={2022},
+  journal={bioRxiv},
+  note={bioRxiv 2022.12.21.521521},
+  url={https://doi.org/10.1101/2022.12.21.521521},
+}
+```

weight/esm-main/examples/lm-design/paper-data/artificial_sequence_purge_ids.txt

@@ -0,0 +1,1027 @@
+F4ZNF8
+A0A2J8RBA3
+P62593
+G9F9U8
+P0A0C4
+A0A1D5R6J9
+Q8NFU1
+O35638
+P97793
+P03023
+P35125
+P08588
+A0A1Z1CGU0
+A3DJ78
+P03051
+C3TTS2
+A0A7S7YQ80
+A0A8A1QP70
+P00552
+C0IR82
+A0A6G8MV33
+A0A7G3QPU0
+C1IW73
+O21999
+A0A7S7YQ81
+B6ZLM4
+E9QNN1
+C3TTS5
+A0A8A1QPY2
+A0A6G8MVA3
+UPI000B4966E4
+UPI000CFA7FCD
+UPI000219022D
+UPI00027FCE9C
+UPI00025EFAFD
+UPI0012FE7F08
+UPI00080A7F37
+UPI0004F13E6B
+UPI0012F47792
+UPI00034F235D
+UPI0002B8CEC9
+UPI0002B7BDD8
+UPI00053CEE40
+UPI00142F3CED
+UPI00001103FE
+UPI0018E1D9AC
+UPI00021472E1
+UPI0008F7E60A
+UPI00097159CA
+UPI000C173B10
+UPI0008FBBFA1
+UPI0012494AED
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weight/esm-main/examples/lm-design/paper-data/data.csv

@@ -0,0 +1,277 @@
+,Design ID,Experiment Name,Design Model,Target ID,Sequence,Total Yield,yield_per_Leq,Soluble,Success,Success+Monodisperse,AlphaFold RMSD,AlphaFold pLDDT,min Jackhmmer E-value,max Jackhmmer Seq-id (significant hits only),max Jackhmmer TM-score (top-10 hits only)
+0,F000,Fixed_Rd1_48,LM,1QYS,MVKVTLTYTKDGKPVTETVEVSSLEELEPKVKEIVEKLKKEGATSASITVTAESEDEAKRAENVAKAVLDAAGVTGYSTSVSGKTVTITATF,0.4957277507294888,123.93193768237221,True,True,True,1.8563631772994995,89.5325332981491,0.95,0.2717391304347826,0.5336
+1,F001,Fixed_Rd2_Distant_24,LM,6MRS,MSEAARAVAEEIEKRVKAAGGSATVKETSSGLEVTVTGVPEPVLKIVENIAKDVGSKYGKTVSVSREGDTLKVTITW,0.3176838320219042,79.42095800547605,True,True,True,1.2924525737762451,87.73543773267119,1.8,,0.79568
+2,F002,Fixed_Rd2_Distant_24,LM,6MRS,MDPRVRAALEEIEKAVKEAGGSVERKVEGNKVTLTVTGLSPEVASKISSKASEVGSKYGVPVSAELKDGKLVITFTV,0.4228188625945215,105.70471564863038,True,True,False,1.1545886993408203,90.09167939406858,2.7,,0.79038
+3,F003,Fixed_Rd2_Distant_24,LM,6MRS,MSKAAEEIVSRLEKELKDAGVTFTKSLSGDTLTITAENVPPSAVERVKSVIESVASEYGKKPEVKVEGNKVTATVKL,0.20452150131449953,51.130375328624886,True,True,True,1.9942351579666135,89.64661571494226,1.6,,0.76915
+4,F004,Fixed_Rd2_Distant_24,LM,6MRS,MSEAVNKIVERLKEEASKLGKTVSVREEGGKVVVEITGLTPSDAPKVKELVEKVAGEYGVKAEVSLEGSTLRVTITA,0.4075973400219396,101.8993350054849,True,True,False,1.2864220142364502,88.70031155357758,0.69,0.2597402597402597,0.48702
+5,F005,Fixed_Rd2_Distant_24,LM,6MRS,MSDAARAVASEISSRVSSLGGSATVREEGGKLVVTITGLPPSVLEEVKKAAEEAGRKAGKPVKVTLSGSTLTVEVSW,0.22237320363197127,55.59330090799282,True,True,True,1.1808888912200928,89.63327697501046,0.78,0.23376623376623376,0.8134
+6,F006,Fixed_Rd2_Distant_24,LM,6MRS,MDPRVSAAVNKVLEELRKLGKTPSISESGGSVTVTITGLSSDEASKVKEIAEKAGREAGVPVEVSLSGSTLTVRFRV,0.037857109780889554,9.464277445222388,False,False,False,1.01856791973114,90.08561428560375,1.8,,0.7999
+7,F007,Fixed_Rd2_Distant_24,LM,6MRS,MPPEVEKAVEEIKKLVSEAGGSAEVSVSGKTVTVKVTGLSSDVASKIEAKAREVGEKYGVPVNVELKDGTLTITFRV,0.24805508578155241,62.0137714453881,True,True,False,1.0749236345291138,90.28870944917344,0.87,0.24675324675324675,0.61168
+8,F008,Fixed_Rd2_compare_LM_24,LM,6NUK,MPTYTLTGTITVPSEEAAKKLVEDLKKAGEEISKETGAKVSVSAELKDGKVTVTATVENAPPEVIEKIKERVKPIVEKYGGSLEVKG,0.28329050639414466,70.82262659853616,True,True,True,2.563059091567993,81.73945005931526,0.39,0.3103448275862069,0.56181
+9,F009,Fixed_Rd2_compare_AlphaFold_20,AlphaFold,5L33,DEKKEVAYRILRALELGSPEAMKEAITDDTHMHVGGMHFQGDEVVKFVEWIAENGMRWRLEHMELIGDHYVAWVHVERGGQEQNVIISINVVDGKVHHIHIYGR,0.277442302966893,69.36057574172325,True,False,False,0.4343377649784088,95.76276113248424,1.0,,0.85007
+10,F010,Fixed_Rd2_compare_AlphaFold_20,AlphaFold,5L33,DEEHQVALRFIEALQTGDPELAAAIVPEDTRMYVGGKRYHGPGIVEFVKELAEAEIKVQLLEYKWVGDKWHFLIRQTHDGQEKRMVVSIRVENGKLGRIYIFGP,0.07283634440694234,18.209086101735586,True,True,False,0.4950173199176788,96.55776679074548,0.14,0.22115384615384615,0.83396
+11,F011,Fixed_Rd2_compare_AlphaFold_20,AlphaFold,5L33,NERYELAHRFIEALELGSPELLARIVSPSTTLWVGGLHFRGEGIYLFVLFIAIFGIKVKYQEHEQVGDMYVYNVEVSHGGQIWDVHIWIHVKDGELSHVYIFGP,0.03633134604045652,9.08283651011413,False,False,False,0.4901691079139709,93.3927575683868,0.085,0.20192307692307693,0.81263
+12,F012,Fixed_Rd2_compare_AlphaFold_20,AlphaFold,5L33,SERLERALAFVEALQTGSVEEARNVISDDTRMWNNGRHYHGEGIVDFVKGMAQGGIKTELIEHELVGDHYVFHIKVSHGGREWPVHMQIRVVDGELRDIWIWGR,0.013853445263341107,3.4633613158352765,False,False,False,0.5045303702354431,96.67489866328364,1.5,,0.8514
+13,F013,Fixed_Rd2_compare_AlphaFold_20,AlphaFold,5L33,NERFMRAWLFVQALQYGDPEMFRNVITPSTRMVVDGHHYEGEGIVDFVRHIAETGMRVQPLSHHLEGDQWVFHMQVTHNGHEKLVTIKIKVVGGEIVHVTISGR,0.026416916364346666,6.604229091086666,False,False,False,0.5356542468070984,93.92580078353362,2.1,,0.84179
+14,F014,Fixed_Rd2_compare_AlphaFold_20,AlphaFold,6D0T,AIDLAEYLPGHWEMLLVSETGKHFTGTMHMKPVDPTVIQMDFWGHDNDGTPFWGYGYFMDTGEDEVHFGMVMSNGYQFEGAYEVVDDDTIVIVADSNKGERYVGVLRRLDP,0.03608557123999195,9.021392809997987,False,False,False,1.2565512657165527,92.54382781055942,1.6,,0.49717
+15,F015,Fixed_Rd2_compare_AlphaFold_20,AlphaFold,6D0T,RPDLKDHVPGHYTVILVSQGGHYITGTVTMETVSDKKVLIEHTGTTGDGTPVTGWGYIDDRSGSPYHFALFLSNGWYFHGNIRFVSDNHILIQMTSSHGLNWVGVLYKTPA,0.013190157779185595,3.297539444796399,False,False,False,0.7335907220840454,93.8959715867114,0.01,0.17117117117117117,0.89692
+16,F016,Fixed_Rd2_compare_AlphaFold_20,AlphaFold,6D0T,DWDLTETLPGYYHFVFVSNDGFAIYGTVHMEVVNPDVVRMTYVGWDSNGVPGSGWGYFHRDPDDKVHFYFEFSNGIKMDGWVKQLDDDTIAITALTSDGRFMHGYLYKMDP,0.008445903520939553,2.111475880234888,False,False,False,0.8426432013511658,93.4402470645872,0.25,0.1981981981981982,0.89593
+17,F017,Fixed_Rd2_compare_AlphaFold_20,AlphaFold,6D0T,APDLDEHFAGKWTWVFVSQDGVSYTGTSTMRAVDGNVIQIDYTGHDSDGTPVTGGGYIHNNHPGRLHFVMAFSNGLVLEGWLTVVGDDTILVHMTSNTGRTYVGVFTRTDA,0.015506616586872708,3.876654146718177,False,False,False,0.9828828573226928,91.99622006352418,0.094,0.21621621621621623,0.90737
+18,F018,Fixed_Rd2_compare_AlphaFold_20,AlphaFold,6D0T,RPDLSTHLPGHYEWHFVSNDGYAFTGTVTMEVVDPNVIWMHYTGWDNNGTPLSGWGYFRDHHPEYYEFGMWMSNGYKFHGWVEFVDDNRIEVVARSNKDRIYSAVFTKHDP,0.004148435344812339,1.0371088362030847,False,False,False,0.6710172891616821,93.87439772382255,0.43,0.16216216216216217,0.44506
+19,F019,Fixed_Rd2_compare_AlphaFold_20,AlphaFold,6MRS,NSPAVMKVIEAVIQAVEGWPSTIHIIRWPHYHLIHIPGLTREQQAELLAVVAWASEAYGVPTWVHLHGNTVSILIFD,0.0067303889674713464,1.6825972418678365,False,False,False,0.4508427381515503,96.6509884001364,4.6,,0.58873
+20,F020,Fixed_Rd2_Distant_24,LM,6MRS,MSEKVLKALEEAENAVKEAGGSLSRSVSGDKVTLTITGLPSDVASKIEGKLREVGEKYGVPVEVSISGSTVTATFRV,0.20536458406256974,51.34114601564244,True,True,True,1.2931123971939087,90.93261409285294,0.58,0.3246753246753247,0.74641
+21,F021,Fixed_Rd2_Distant_24,LM,6D0T,MAKLSELLVGEYEITGKNAAGEPVTGTLKIEAVSDTEVKVSGTIKTKDGKEVPQSGSATVAADGTVTAERKLEDGRVVKSTSKPEGDKTLTTTSVLDDGSEIVEKGVRKAS,0.24740261117966605,61.85065279491651,True,False,False,2.7274112701416016,82.86246046516828,0.61,0.22522522522522523,0.78995
+22,F022,Fixed_Rd2_Distant_24,LM,6D0T,MSSVLDKVAGEYTVEGKDPQGNPITGSLKIEKKDEKTASISGKLKTPEGKEVPVSGEATVTSENKVEVKVKAEDGSEITLTGTLESDDTIKLSGTTKDGKPVELTAKRPKS,0.34486560568121266,86.21640142030317,True,True,False,2.518651247024536,86.09766403439173,0.24,0.35135135135135137,0.90266
+23,F023,Fixed_Rd2_Distant_24,LM,6CZJ,MTFVERLAGKYEGEAKNAAGEPVKVVLEITKEGDNKASVKLTITTKDGKTEELSGSATLESETKATVSLKTSDGQTVTLTVEKPSDSEIKVTGTAPDGSKIEGTLKKKS,0.2537102178201451,63.42755445503627,True,True,True,1.669878602027893,86.19851952337629,0.94,0.2018348623853211,0.38273
+24,F024,Fixed_Rd2_Motif_11,LM,6W3W,AGDEAAVKDVVTKLIEAENKRDAAGLKALVAPGAKIVEEGKTITGVDAFVKELEKEPKPTYTLGEVKVSGDTATVPVTETEDGKSEKGTVTLKKDGGKWVISEIKEE,0.28501657145748693,71.25414286437173,True,False,False,2.4170925617218018,89.54169269821487,2.3e-06,0.37383177570093457,0.7676
+25,F025,Fixed_Rd2_Motif_11,LM,6CZJ,MTLEEALAGTYKIEGKDAAGNPISGTSTITKEGEGKYSVVSEIKTPDGKVIKSSGSATVEKDGTVKAELTAENGQKLTVTLKVNADGSRDVTVTTSDGKTFTGKETRVK,0.24468233970059516,61.17058492514879,True,True,False,1.6262468099594116,87.99247793965945,0.002,0.28440366972477066,0.84723
+26,F026,Fixed_Rd2_Motif_11,LM,6CZJ,MTLEEKLVGEYSVSGKTPEGAEYTGKAKIEKAGENKYKVTWTTKNAEGKEVTSSGEATLKDDKTVELTITNPDGSKSTGTAVVKDADTREITFKGSDGQEIKETWKRVK,0.3977466139210023,99.43665348025057,True,True,False,1.337502360343933,88.70211181997347,0.0016,0.25688073394495414,0.84406
+27,F027,Fixed_Rd2_Motif_11,LM,6CZJ,MTVLDKLAGKYTLSGTGPDGKPVSGELTITKEGENKVSVERKITSPEGKETTEKGTATLKSDTEADVTLTAEDGSTVSATVKLESDSEISFTGKTKDGQEVKVTAKKAS,0.40438764010168293,101.09691002542073,True,True,False,1.4750990867614746,88.14754882966898,0.9,0.21100917431192662,0.81634
+28,F028,Fixed_Rd2_Motif_11,LM,6CZJ,MSLADKLVGEYTVSGKDAEGNEYSGTAKISKAGENKYTIEWTSTDKEGKPITLKGEATVKDDSTVEIKVTTEDGKEITGVQKVVSDTEREVTLTLPDGSVAVEKWTKKS,0.3733776066897868,93.34440167244671,True,True,True,1.5804373025894165,89.3412295564101,0.0025,0.23853211009174313,0.84063
+29,F029,Fixed_Rd2_Motif_11,LM,6CZJ,MSVLDALAGKYTVKGKNPDGSEYTGTAEITKKDDTTAAVSLTVTGADGKPVTLSGEAKLEGENKISSTLKTSDGREVKVVEELVSATERKVTITPPGGSPIVETWTKAS,0.2803780751152961,70.09451877882402,True,True,False,1.886478304862976,85.56625102495296,0.00015,0.29357798165137616,0.79837
+30,F030,Fixed_Rd2_Motif_11,LM,6D0T,MSSVLDKVAGEYEVSGKTPDGKPYKGTATITRVSDTEAKISWTSTPPEGKEIKLEGTAKESEPGKVTVTLKGEDGQEVTGTYTVNADGSLSATLTTKDGVKAEETWKKKAS,0.3798131326932491,94.95328317331229,True,True,False,2.5999600887298584,87.12063850277771,0.0004,0.3063063063063063,0.83537
+31,F031,Fixed_Rd2_Distant_24,LM,1QYS,MFRVKVTGTKDGKPVTEEYTASKPEDVVKAVGEVIKKLKEKGVTSATVEIEAESEKDLENAKRIVEGLLRAAGAKNVSVSLSGRKLTITATL,0.3427875797108305,85.69689492770763,True,True,True,1.807220816612244,89.89167015658074,2.2,,0.53858
+32,F032,Fixed_Rd2_Distant_24,LM,1QYS,MVKVVIKGTKDGKPVEKTLEASSLRDVGKLVRETVKELKDAGVSSVTVEVTAPSEKAAKIAENVAVNALKKAGAKDVTSKVEGNKVTITATL,0.9202675931650974,230.06689829127436,True,True,False,2.0360801219940186,92.28481875444518,5.9,,0.52215
+33,F033,Fixed_Rd2_compare_AlphaFold_20,AlphaFold,6MRS,RSPQVMRVIEEIAQAVQGWPVELHMIWGPDRVIVIIPHLTWPQQMELLAVVARASWELGVPTKVYIGGSTVIIIVDD,0.006731173414179127,1.6827933535447817,False,False,False,0.4216504096984863,97.22116813959614,1.7,,0.94281
+34,F034,Fixed_Rd2_Distant_24,LM,1QYS,MVRVKVTGTKDGKPVTIEGEASKPEDLKDVVAKAVEKLKEAGVTSATVEITAENEAQLSEVKKIVEEELRKAGAKNVSVSLEGKTLKVSGSF,0.33944966291032735,84.86241572758183,True,True,True,1.9140664339065552,89.98501265829073,0.9,0.22826086956521738,0.4941
+35,F035,Fixed_Rd2_Distant_24,LM,1QYS,MVKVTLTYTKDGKTVTETIEESDPSKAGEVIKKVVEKLKELKPTSVTVEITASSEDEAKNAENIVKAVLTGLGAKDVSVSREGSTVKISATF,0.4037276032258898,100.93190080647246,True,True,False,2.09683895111084,90.84441433223994,2.0,,0.6046
+36,F036,Fixed_Rd2_Distant_24,LM,1QYS,MVKLTITGTKDGKPVTKTVEVSSLEGLPSEIEKAVKELKDAGVTSVTAEVEAPDSATANVVARVLEATLRAAGAKDVQVSVSGSKVTVKASF,0.395968365097821,98.99209127445525,True,True,True,1.81305992603302,90.34497043231632,0.96,0.22826086956521738,0.48044
+37,F037,Fixed_Rd2_Distant_24,LM,1QYS,MVKLTLTYTTPEGTKTETVEVSSLEEAKEKIKAKVEELKSKGVTSVTVVIEAPSEDAVRVAENVARLVLSAAGAKDVKVSREGNKVTITATF,0.052453902269729735,13.113475567432435,True,False,False,1.5934504270553589,90.3361436031438,2.4,,0.55363
+38,F038,Fixed_Rd2_Distant_24,LM,1QYS,MVKYTLTATKDGKTVTKSGEASSLSEVPSEISKLLEELKKEGVTSATVVIEAPDEKTAENAKRVAEAVLKAAGAKDVSVKVEGNKVTITGTF,0.3282799016302957,82.06997540757392,True,True,True,1.724876880645752,90.52690170049704,0.78,0.2717391304347826,0.8638
+39,F039,Fixed_Rd2_Distant_24,LM,6CZJ,MTVLEKLAGKYEISGKSPEGEPVTGTSEITKEGDNKAKIVSTIKTKDGKEVKLSGTATVESDTKVSAPLTLEDGRKVTAEYTFVDADTVKVVITLDDGSKIEGTEKRVK,0.36725811471625625,91.81452867906407,True,True,True,1.2565298080444336,88.28953625778355,0.84,0.22935779816513763,0.76003
+40,F040,Fixed_Rd2_Distant_24,LM,6CZJ,MTVAEKLAGEYTVTGKNAAGQAVSGSSKITKVDATNYKVESTLTTPEGKPVTLSGTAKLESDTSAPIELKTEDGKTVKGTIKFEGADKRVVEVPGPDGGVLKETWTRKK,0.2979780984488259,74.49452461220648,True,True,True,1.509373664855957,86.41524649023813,0.81,0.1834862385321101,0.55789
+41,F041,Fixed_Rd2_Distant_24,LM,6CZJ,MSLEEKIAGKYEVSAPGPPGVTVTGTLEIKKEGENKYSVKGELVTPEGQKVPVEGSATLESETKATVKLKTSDGREITLTIEFKSDDEAVVSGTTPDGKPISGTAKRVK,0.07818634822978235,19.54658705744559,True,True,False,1.5739067792892456,86.4768208724314,0.0099,0.3211009174311927,0.74571
+42,F042,Fixed_Rd2_Distant_24,LM,6CZJ,MSDLDKIAGEYEVSGKSPEGEEIKGTLKVTKKDDKTVAVELELPGPDGKPVKASGTGTYDGGSKATVTVKDEEGNEVTLTITFKDADTAEIEGKDSEGQPIKLTAKRKK,0.43600475224699825,109.00118806174956,True,True,False,1.936157822608948,86.32997514021847,0.4,0.21100917431192662,0.82276
+43,F043,Fixed_Rd2_Distant_24,LM,6CZJ,MSFVEKLAGKYKVEVPGPPGLTISGTWEIKAEGENKASITSTTKDPTGKEVKVSGVLEKKSDTEAVGKITLPDGSSADVTVKLEGDDKLSVTAKNAAGQELKGTATRVK,0.1588073964687471,39.70184911718677,True,True,True,2.2209596633911133,85.67952096772032,0.83,0.25688073394495414,0.74244
+44,F044,Fixed_Rd2_Distant_24,LM,1QYS,MPTVKITGTKDGKPVTEEYTTSDPSEIKSIVEKKAKELKDSGVSSVTVEVTASSEGELREAQRVAENALRDAGLKNVSTRLEGNKLVISGSA,0.0994513461543207,24.862836538580176,True,True,False,2.0107052326202397,91.17856337372622,2.0,,0.82719
+45,F045,Fixed_Rd2_Motif_11,LM,6D0T,MKSLSEKLAGEYTLEGEAPGGAKITGKTVIKAVGENKFEVTSSVTDPSGKPVESKGTAELKGDKEIVATLTASDGSTATVTETVVSADERKVTITTKDGQKIEVVEKRVKK,0.13598994945441087,33.997487363602716,True,True,False,3.051065683364868,88.52513366461596,0.36,0.24324324324324326,0.8139
+46,F046,Fixed_Rd2_compare_AlphaFold_20,AlphaFold,6MRS,GSPKVWQVIWAILQATQGMGVTVHIIAHPHHYLIIIPGLTAEQQWILLHVVAQAAQLFGVPTWVHLEGGTVSILVFD,0.0055851279719760704,1.3962819929940176,False,False,False,0.3663604259490967,97.1495514547789,0.0045,0.2727272727272727,0.48785
+47,F047,Fixed_Rd2_compare_AlphaFold_20,AlphaFold,6MRS,VNPKVWKMIYSVIQATKGMGHHIHIHWYPHFFIIHIPGLTDAQYERIRQAVAQSAEQNGVETWLYWSGNTVTIIIFE,0.003687218919149328,0.9218047297873321,False,False,False,0.4571395814418793,95.11999800945894,2.7,,0.55457
+48,F048,Fixed_Rd2_compare_AF_ngram_20,AlphaFold+ngram,6NUK,SYEFRVSVSVTVSDVPGILAWLEGLSATLKELEAGTPARIRETVRIDGGKVDLTIVAENAEPAVVSEIIGLLLPFVERYGGSLIIEY,0.397509175990901,99.37729399772526,True,False,False,0.6839452385902405,93.80271215881568,5.0,,0.49771
+49,F049,Fixed_Rd2_compare_LM_24,LM,5L33,MKPEEVVKKYIEALNKGDANAVKELLSSDVELTDVGQTLKGEKAAEFVAGLAKSGAKAEIKDLKVEGDKVTATVTITAGGKTTTSKDTFTVKDGKITKLEVSAS,0.39212291496487056,98.03072874121764,True,True,False,2.490266799926758,91.68561308298789,5.8e-08,0.40384615384615385,0.83014
+50,F050,Fixed_Rd2_compare_LM_24,LM,5L33,MSAKEVVEKYVAALNSGKPEDLKPLLADDVTLTDVGQELKGSDAVKFVEGLSKAGAKIELGEVSESGDTVTAPVTITAGGKTTKATDTFTVKDGKITKLEIKAS,0.06935968584639321,17.3399214615983,True,True,False,2.330277919769287,89.5547881916068,3.8e-06,0.3942307692307692,0.84532
+51,F051,Fixed_Rd2_compare_LM_24,LM,5L33,MSAKDVVNKYVEALNSGDPEKAASLYAENATVTDAGKTLTGKEIGEYLKGVAAGGAKLEVGEVKEEGDKVTAPVTITLPGQTIKAVDTFTVKDGKIVSSEFKAE,0.38402089919353116,96.00522479838278,True,True,False,1.8524878025054927,89.30061218432351,9.4e-10,0.40384615384615385,0.92595
+52,F052,Fixed_Rd2_compare_LM_24,LM,5L33,MSAKEVVEAYVKALNEGKPEDLKKVLADDVELTDVGQTLKGSDAVKFAEGLSKAGAKLEIKDISESGDTVTATVTITAGGKTTEAKDTFTVKDGKITKLVIEPK,0.6398325282794153,159.95813206985383,True,True,False,2.2361340522766118,90.44281855923052,2.4e-05,0.38461538461538464,
+53,F053,Fixed_Rd2_compare_LM_24,LM,5L33,MSAKEVVTKYIEALNKGDAKAAGELLSPEVELTDVGQTLKGSDAVKFVEGLAKSGAKAELVSLEEKDGKVVATIKVTAGGKTVTSTDTFTVKDGKITKLEISSK,0.35495440722572474,88.73860180643119,True,True,False,1.9567445516586304,91.38719989078253,4.7e-06,0.36538461538461536,0.85161
+54,F054,Fixed_Rd2_compare_LM_24,LM,6D0T,MLKPEDLAGKTFEGKLTTKDGKEITGTVTIEKKGENEYSVKISGKDAEGKPVEVSGTAKLESETKAVVTLKDDKGNEVTATVERPEPGKIKVTVKASDGSEASGELTEKKS,0.3765251534916582,94.13128837291455,True,True,False,2.6787517070770264,84.5044350504976,0.064,0.32432432432432434,0.70066
+55,F055,Fixed_Rd2_compare_LM_24,LM,6D0T,MASLLEKIAGEYEISGKTPEGEEVSGTVTIKAKDDKTAEIEGKLKTKDGKEVPVKGSATVESENKVKVTLTTEDGQKVELEGEVKDENTIVLKGKTSDGKPVEATLKKKSS,0.5348707582648717,133.71768956621793,True,True,False,2.441730260848999,87.08570465817874,0.17,0.1891891891891892,0.9073
+56,F056,Fixed_Rd2_compare_LM_24,LM,6D0T,MASLAEKLVGEYTLSGQTPEGKPVSGTLTIKAAGDGKYEISGSAKDSEGKEVPVTGTAELKSDTEAVVTLTTKDGKTVTATVKVVDENKIEITGKDDAGNEVKLTGERKKS,0.1467424148581473,36.685603714536825,True,True,False,2.2758829593658447,85.9973229866832,0.01,0.3063063063063063,0.64224
+57,F057,Fixed_Rd2_compare_LM_24,LM,6D0T,MASLLDSVAGKYTFKVTTPEGVEITGELTITKKDDTTADVTGTAKTPDGKEVPVKGTVKLESETKATVELTLEDGSKITATIEKVDENTLKVSGKDAEGNEISGEAKRVSS,0.3781642452545014,94.54106131362535,True,True,False,2.1835381984710693,87.53634282677913,0.34,0.2882882882882883,0.73003
+58,F058,Fixed_Rd2_compare_LM_24,LM,6D0T,MASVLDKVAGEYTVKGKDPQGNEISGTVTIKKESDSKATVSAKLKTPEGKEIEAEGTAEDAGSGKYKVTLTTKDGQKITGEVEVTSENKAVFKGTTPDGKPVELTLEKKSS,0.2722799409457035,68.06998523642588,True,False,False,2.5545766353607178,86.77399146550304,0.043,0.25225225225225223,0.83274
+59,F059,Fixed_Rd2_compare_LM_24,LM,6MRS,MSEAVNKALEEIEKRVKELGGSISKSVSGDTVTLTITGLSPSDAEKLKPIVEEVGKKYGVPVTVKVEGNSVTATFRV,0.4146882077613797,103.67205194034493,True,True,True,1.2394684553146362,90.22297520345086,0.5,0.36363636363636365,0.56873
+60,F060,Fixed_Rd2_compare_LM_24,LM,6MRS,MDPRVQKVLDAVRAEVSKLGGSLSESVSGDTVTLTITGLSPSDVEKVKEAAKKAGEEAGVPVEVTVEGSTVKVTFRV,0.42795601432893304,106.98900358223327,True,True,True,1.0627379417419434,88.78233635603895,0.0086,0.33766233766233766,0.94273
+61,F061,Fixed_Rd2_compare_LM_24,LM,6MRS,MDPRVKAVLDAVRAEVEKLGGSLSESVSGDKVTLTITGLTEENAKKLKEILEAKGKELGVPVEVKVEGTTVTATFTV,0.06768315402819766,16.920788507049416,True,True,False,1.1077884435653689,88.68789417835325,1.4,,0.65477
+62,F062,Fixed_Rd2_compare_LM_24,LM,6MRS,MSEAVNKALEEIEKRVRELGGSLSRSVSGDKVTLTVTGLPPSAAEKLKDVVKEVGEKYGVPVEVSISGTTITATFRV,0.32934146863803615,82.33536715950903,True,True,True,1.1360328197479248,89.26651574507316,0.36,0.2597402597402597,0.78406
+63,F063,Fixed_Rd2_compare_LM_24,LM,6MRS,MSEAVNKALEEIEKRVKELGGSLSRSVSGDTVTLTITGLTPEVAEKLKPIVEEVGKKYGVPVTVKVEGNSITATFRV,0.34711303125744997,86.77825781436249,True,True,True,1.2481086254119873,89.66695531546407,0.38,0.33766233766233766,0.5774
+64,F064,Fixed_Rd2_compare_LM_24,LM,6NUK,MPTYTITVTGKPPSREAAEKLKAELEKRLEEIKKETGGSASLSISVSGDTVTATLTVSDPKPEVVNKVKEIVERVAGEYGLKVEVKG,0.38448925298207326,96.12231324551831,True,True,True,1.672995924949646,85.72934394680213,0.95,0.2413793103448276,0.38076
+65,F065,Fixed_Rd2_compare_LM_24,LM,6NUK,MPTYTITGTIKVPSREAAEKAVSEISSKLEELKKEAGGEVSVSASLSGDTLTVTATVKDAPPEVVNKVKEILKPIVEKYGGSFTVSG,0.27670878440403274,69.17719610100818,True,True,True,1.4940338134765625,86.68075073787664,1.1,,0.45283
+66,F066,Fixed_Rd2_compare_LM_24,LM,6NUK,MPTVTLKGTVKVPSREAAEKLVSELKSKAEEIAKETGGRVSVSASLSGDTLTITATVTDAPPEVVEKIKNAVKPIVEKYGGSLEVSG,0.3782618229632644,94.56545574081609,True,True,True,1.965891003608704,87.01459559497974,1.5,,0.69193
+67,F067,Fixed_Rd2_compare_AF_ngram_20,AlphaFold+ngram,6NUK,SNEVTITLRFVTKEPAALDKRISGLIFGLRNLAAVTGPPIRVRVIKDGGRYEITVTVSDAPEYVVETILSWLRPLVEDEGGELTVNR,0.025522988182149688,6.380747045537422,False,False,False,0.6856326460838318,93.01342464445024,3.4,,0.72334
+68,F068,Fixed_Rd2_compare_AF_ngram_20,AlphaFold+ngram,6NUK,SPTTTISVSFRVSAPDAVLRRLAWLKAELARIAAEGEYTIRLIIVYDGGRVRLTVTVSDPPGEVVEEIIGLLLPLVETSGGSLEIRP,0.014717887286131444,3.679471821532861,False,False,False,0.617400586605072,93.0615892190848,0.51,0.28735632183908044,0.59931
+69,F069,Fixed_Rd2_compare_AF_ngram_20,AlphaFold+ngram,6NUK,SPKVTISGSFRSTEGEALDLQLERLKENLKEIEKGTGVRITTKIVKDGGRYGITITAVNSEPPVVAEILAMLRPLKETQGGSLRVSA,0.05544771673805695,13.861929184514237,True,False,False,0.7212327718734741,89.46671457519403,1.0,,0.717
+70,F070,Fixed_Rd2_compare_AF_ngram_20,AlphaFold+ngram,6NUK,SPKTTIRISWKTSDLAATIGRLKNILKVLAELEASTDVKITGYVTINGGNVRFRVTVENPPESVVEYIIEILRPIVDSDGGSLEIEK,0.029373659480558863,7.343414870139716,False,False,False,0.6144019365310669,93.7152693065673,2.6,,0.72923
+71,F071,Fixed_Rd2_compare_AlphaFold_20,AlphaFold,6NUK,SRSFIIGVHVRTSELEGMKRWIQNLQANLQYLEAHSGHKIHMHVTMEDNDLSIIIIIHDWDAELVHRVIDMLVPLVQHHGGLLVIIP,0.0028960967350633005,0.7240241837658251,False,False,False,0.468681275844574,96.12428272083872,3.8,,0.58877
+72,F072,Fixed_Rd2_compare_AlphaFold_20,AlphaFold,6NUK,ENVVAIYIKYKTTHPMETLMRLQWLKGHLAWLAAHSDAPIYMYVHIKGNEVEILVIVRNWTPWLVMEIIWHLLPWVEEEGGELYIFL,0.0028035211157925325,0.7008802789481331,False,False,False,0.5145629644393921,95.31983353633112,3.0,,0.51107
+73,F073,Fixed_Rd2_compare_AlphaFold_20,AlphaFold,6NUK,SPKFIIVMSFKTDRPEETKLRLAWLKGWLKWLEAKTPAKINMYVKINDGNVFIMVTLINPQPEMIERVIWMLLPWKEEYGGSLYIFP,0.0015853388134483997,0.3963347033620999,False,False,False,0.4841421246528625,95.45712654746409,3.2,,0.50826
+74,F074,Fixed_Rd2_compare_AlphaFold_20,AlphaFold,6NUK,VPVVKISVAFWAEEPDAIRAWLRQFYNNLRALEANTDSTIHMYVVERRGWYAIWVEVYGYDEKLKEQIISMLLPIVERYGGSMQIED,0.006123649016661879,1.5309122541654696,False,False,False,0.4769626557826996,97.1155665975422,1.6,,0.50591
+75,F075,Fixed_Rd2_compare_AlphaFold_20,AlphaFold,6NUK,SKQVQISLAIRTTEPAPMARRIANFRADLERLKANSPMEAHLTVITNGGNIAMTVVLVDWDEEIVQRVIGMLLPWVNHYGGYMEYRP,0.012608951268734995,3.1522378171837486,False,False,False,0.4407394826412201,95.49964385694892,0.041,0.21839080459770116,0.52575
+76,F076,Fixed_Rd2_compare_AF_ngram_20,AlphaFold+ngram,5L33,SEKLEVAYKFVRALEEGSPEELREVVPPKTTLVVGGKRYTGDGIGEYVENLARSGVEIKAESVREDGGPYVFDLRVSAGGNEFRVTLQITVKDGKLDTVVITGR,0.03433580815522718,8.583952038806796,False,False,False,0.6159579753875732,92.56226007687252,0.66,0.22115384615384615,0.50001
+77,F077,Fixed_Rd2_compare_AF_ngram_20,AlphaFold+ngram,5L33,GKPLEIAEAFVKVLQAGDAEELKNVVSAPARMTVGGRKYEGKEIVSFVERLAENKVTVQKLSARREGGKYVVGLRVTKDGSTGLVLVTISVKNGELSEVIIETS,0.0195163450991353,4.879086274783825,False,False,False,0.6291195750236511,89.29584350313368,1.3,,0.82768
+78,F078,Fixed_Rd2_compare_AF_ngram_20,AlphaFold+ngram,5L33,SPPEPKARAFVRALETGSPEEMKSILSPSTTLVVGGETYTGDQGVGLVERLKEARIKVKLLSWTKEGGAPIFRVRVSAGGSEIDYTISVEVTDGKVVNVVITGP,0.045617220559802565,11.404305139950642,False,False,False,1.501671552658081,93.55763168023591,0.13,0.18269230769230768,0.75454
+79,F079,Fixed_Rd2_compare_AF_ngram_20,AlphaFold+ngram,5L33,SPEGEVAGRFVEALERGDPELLENVVSEGTTLVDGGRQYTGSSIVEYVKEIRESGTKVTRLSSYREGGAYVVPLKWTKDGAEFRVKATITVTDGKLTNVVIERP,0.01781002567547135,4.452506418867837,False,False,False,0.7932162880897522,94.82330797515212,0.034,0.28846153846153844,0.79219
+80,F080,Fixed_Rd2_compare_AlphaFold_20,AlphaFold,6MRS,SSPAKHKVIEAIAQAVQGMNVRLQIYEAPGYSIIYIPGLTFEQQLRLLAVVAQASHAFGVPTSVHLWGNTVMITVLD,0.01253226726272454,3.133066815681135,False,False,False,0.5121968984603882,95.97648053702224,1.4,,0.46478
+81,F081,Fixed_Rd2_compare_AF_ngram_20,AlphaFold+ngram,5L33,SPKEAVVEDFVKALETGDASSLAGVVSEPVTGTVDGRTFKGKEWVRFAEELERRKITVQKLSWYKDGGSYVVPLRVTAEGKTIEATLRVNVIGGKLKEVTISGK,0.018462287127385252,4.615571781846313,False,False,False,0.6195029616355896,91.81086284819986,0.5,0.2403846153846154,0.81175
+82,F082,Fixed_Rd2_compare_AF_ngram_20,AlphaFold+ngram,6D0T,VPDLKKVLPGKYSYTLEAAGPPVITGDATFKPVDDGVVRVTSEGTKSGGEPISGEYTITRADGGALEYRETLSDGYEFRGTPTFVSEDLIEITAENSEGLTVTGSLTRLPD,0.0400671570350624,10.0167892587656,False,False,False,1.0885957479476929,89.13234307107493,0.0022,0.25225225225225223,0.88272
+83,F083,Fixed_Rd2_compare_AF_ngram_20,AlphaFold+ngram,6D0T,SLDLTKELQGRVSFTLTAPGPPTITGVANIRPVDGEKVLIEHKAVNSAGEPISGIGYIEKDGGSKPKVSLVFSDGQVLTGYYTFVSEDEILIKGRYSDGDIVEGTWTRLPD,0.07543364116939073,18.858410292347685,True,False,False,1.1696112155914309,89.83817957215413,5.7,,0.49535
+84,F084,Fixed_Rd2_compare_AF_ngram_20,AlphaFold+ngram,6D0T,SLDLASDLKGEYSFYLENAAGRSITGYITVRPVDPSVVEIRTKAVNSDGVPVYGTGYVTRDGGSKPKVTLVLGPPLIIEGSLRKESEGEFRVKATLSAGEEVTGRFTKIPD,0.030757727520846254,7.689431880211563,False,False,False,0.856549084186554,91.328890772023,2.6,,0.9077
+85,F085,Fixed_Rd2_compare_AF_ngram_20,AlphaFold+ngram,6D0T,RPPLEEAYVGWYRVTLTSREGLTITGLVTVTPVSPEVLNIEVTAVNSAGTPLKGIGYISEGPPGPPVFSLVLDGGYEFRGDITVVGEDEIRVSATSSDGLDVTGTFTKLPE,0.029582706827141456,7.395676706785364,False,False,False,0.8497048616409302,92.4071064287583,0.29,0.25225225225225223,0.376
+86,F086,Fixed_Rd2_compare_AF_ngram_20,AlphaFold+ngram,6MRS,LDPAVFRVIEAIIRAVEGIGSPVYIFLSPDSVLIEITGLTEPEIARLTKVVAESAERYGVPTSLRIDGGTVSILVQA,0.04638047838827145,11.595119597067864,False,False,False,0.4881626069545746,95.27932442103456,15.0,,0.51141
+87,F087,Fixed_Rd2_compare_AF_ngram_20,AlphaFold+ngram,6MRS,SPPKVNALIEAILEATRDSGFRVTIRESDGGSTIRITGLTPPDIARLLRVVAEASAELGVPSTVLVAGRTLIVTVSW,0.038991302018317235,9.747825504579309,False,False,False,0.9341968297958374,93.33669438860584,2.4,,0.70646
+88,F088,Fixed_Rd2_compare_AF_ngram_20,AlphaFold+ngram,6MRS,FSEKTLKVIAEIVEAVKGLGSPLYLFVTPDKVTGYIAGLTPPEIERILEVVARASAEYGIETTTTVDGGSLRVTITD,0.08912437224937385,22.281093062343462,True,False,False,0.5166324973106384,94.78244900295392,0.34,0.3116883116883117,0.57541
+89,F089,Fixed_Rd2_compare_AF_ngram_20,AlphaFold+ngram,6MRS,RDEGTYELINAILKAVEGSPARPRIYEGGGSAVIVITGLTPPEIERLDRVVKEAGRKTGVPFSLRVKDGTLTVTVSW,0.0237274715701571,5.931867892539275,False,False,False,0.7228794693946838,92.31426257702373,2.2,,0.55537
+90,F090,Fixed_Rd2_compare_AF_ngram_20,AlphaFold+ngram,6MRS,SNAAAEYLLNVIREAVKGLPSPITERRDGGSTLVEIENLTPEDIARLKAVVKTAGEETGVPYRVFVGGPTVTILISW,0.018787930568922893,4.696982642230723,False,False,False,0.9679087996482848,94.93896683420776,5.1,,0.94399
+91,F091,Fixed_Rd2_compare_AF_ngram_20,AlphaFold+ngram,6D0T,SLNLKEDVVGKYRFLLVSEEGASIEGIATVKAVNPEVVEIEYTGRTSAGKPISGQGYISRDPPGQLKLTLLFDIGLVLEGTITKVSENEFRVNATSDGGSKITGYFVKVPD,0.19157571455457184,47.89392863864296,True,False,False,0.8795504570007324,94.01044187538032,5.9,,0.60468
+92,F092,Fixed_Rd2_Motif_11,LM,6D0T,MASLADVLAGTYTVTGKSPAGQEYKGEVVIEKVSDTKLKVKRTITTPDGKPVTDEGEATVSAENVVTFTAKNKDGVEISGKYTLEGKDKIKAELTAADGSKASEEWTKKTS,0.4294321330847011,107.35803327117527,True,True,False,2.5218183994293213,87.65193392239404,0.18,0.27927927927927926,0.83247
+93,F093,Fixed_Rd2_compare_LM_24,LM,6NUK,MPTVTLSAEIPVKDRETAEKIAKELEAKASEISSKTGGSVRVSTSVSGDTVKVTATITDAPPEVVKAVEEVLKPIVEKYGGKLTVSR,0.342255516940417,85.56387923510425,True,True,True,1.4239349365234375,86.81879193111219,0.33,0.27586206896551724,0.5938
+94,F094,Fixed_Rd2_Motif_11,LM,1QYS,MVKVTYTITKDGKTFKGEREVSDPSEIKPVVEKLIEDAKNAGATSVSVEVTAESKEKAEEVKKAVEEALKSAGLTNVTVTLEGTTLKVSGSL,0.4990069812474674,124.75174531186686,True,True,True,2.132812976837158,89.96011297143937,0.077,0.2608695652173913,0.49928
+95,F095,Fixed_Rd1_48,LM,6W3W,ADDKAAITDVVNKLIKAVNEKNAEALKELTAPGVEIVIEGQTVSKPEDLVKGLSEAKIDSATVGEVKVEGDKASVPATLKAGEETTPGTFELEKKDGKWLVTKVTLQ,0.4305501431758556,107.6375357939639,True,True,False,1.870003581047058,86.81428747758584,1e-05,0.308411214953271,0.85199
+96,F096,Fixed_Rd1_48,LM,6W3W,AGDEAAVRAVVKSLVDAENAKDTTKLKALLSSSATVEEDGKTVSGRDAVLAEIAKEKVTGYTLGEVTVKGSEASVPVTITADGKPEPATFTLKKEGGKWVISKIVEK,0.34157332772079496,85.39333193019874,True,False,False,1.675412893295288,89.52935934106502,8e-05,0.3644859813084112,0.78333
+97,F097,Fixed_Rd1_48,LM,6W3W,SPEEAAVKDVVNKFVELIKAGKYDEAKNLLSSSATIVVDGKTVTGRDAVLEKLKSEPPTSVEITKVSVSGDTATAEGTVTKDGEKKPVKVTLVKEGGSWKISKIEVQ,0.19536385993824054,48.84096498456014,True,True,False,1.7062160968780518,95.06005609985051,9.2e-05,0.34579439252336447,0.89644
+98,F098,Fixed_Rd1_48,LM,6W3W,MSAENKIKEVLEKLKSAAGSPSESDLSSVVASNVTLTIDGKTVSGKDELVKALKEAKVTDVKVDSVEEKNGEYTATGTVTKDGQSKPVKFTVKEEGGKYVITSIEVK,0.23774272150668382,59.43568037667096,True,True,True,2.2278475761413574,86.84431080836934,0.06,0.2803738317757009,0.8568
+99,F099,Fixed_Rd1_48,LM,6W3W,MSAEDKVKEVLTSLVNAAKEKKSSDVEKLLAENVTLEIEGKTVTGKDAVVKALSEAGVTDLKVDSVTEKDGEYTVSGTATKDGKPTPVSATVKDDGGSFKITKLTIK,0.23592128304088208,58.98032076022052,True,True,False,1.926544189453125,90.262273711329,0.15,0.19626168224299065,0.83195
+100,F100,Fixed_Rd1_48,LM,6W3W,MSAEAKVREVLDKVVAAVNKKDEAGLKALLAPGAKIVEDGQTVTGVDAVVKELKDSGITKLEVGEVKAEGGKYTVPATLTDGGSTSKVTFTVEEKDGKPVITEIKSE,0.4043727545699978,101.09318864249946,True,True,True,1.897280216217041,87.15427816645891,0.00077,0.3177570093457944,0.75264
+101,F101,Fixed_Rd1_48,LM,6W3W,AGDEAAVRAVVQSVIDAENAKDSAKLKPLLSSTVTLEEEGKKVTGLDAVLAEVAKEPVTKAEITDVKVSGDKASATVKETEDGKTETATFELVKEGGSWKISKIVEK,0.37507375671603954,93.76843917900989,True,False,False,2.0780415534973145,88.30627886885424,2.9e-06,0.3925233644859813,0.74489
+102,F102,Fixed_Rd1_48,LM,6W3W,MSDEAAVRDVVNKYVAALEKKDSATLKTLTASNVTIVEEGKKVTGLDAFVKEAEKEKPSKITVGEVSVSGDKATVPVTEVEDGKTTKSTFKLVKDGGKWLISEITEQ,0.27505544121764547,68.76386030441137,True,False,False,1.9738805294036863,87.71122586251461,1.3e-06,0.38317757009345793,0.80058
+103,F103,Fixed_Rd1_48,LM,6W3W,AGDEAAVKEVVNKLIEASKAKNAEGLKPLVAENVKIVSGGQTVTGRDALLKSLSDSGVTEYKVGEVKVEGDKATVPVTATKDGKTSTTTFTLEKKDGKWVITEIKSE,0.32059223185798696,80.14805796449674,True,True,False,1.9781895875930784,87.12275126881077,1.7e-06,0.3364485981308411,0.87358
+104,F104,Fixed_Rd1_48,LM,6WVS,MEYIVEVDTPERVSEIVKTLKESGASNIALVSSKPDLVIEAAKAGIEKVILTPGSLEDARETVKKLKEVGVKDIGLITSEPSLVLEGLRAGVGEYVVELDSAENAAEVVKRLKDGGAKNVAFVTTKPELAVEAVKAGVEKVVLSPATLEEAKETVRKLREAGAKDVAIISTEPPLILEALKS,0.3807309294882399,95.18273237205997,True,False,False,1.6833454370498655,83.07913801333038,0.036,0.3076923076923077,0.58145
+105,F105,Fixed_Rd1_48,LM,6WVS,MIISVRAENKDELTSTVKELKDKGIENILVVTKDLDSAVAASKGGSEYVVVEVSEPGKAGETVERLRKEGVKRVAVISSDPKVIEKALEEGTPIVSVKAKNKEELSSTIKKLKEKGVENVLAVTEDLESAITASEGGAEYIVVKVSKPEEAGKTVEKLREKDVKRIAVVSSDPEVIKKVLEK,0.06826551071176487,17.06637767794122,True,False,False,1.4189119338989258,85.69943641442232,0.044,0.18681318681318682,0.42681
+106,F106,Fixed_Rd1_48,LM,6WVS,MITVIEGKDPATIKDSVTKLRAENVSEIAFTSTKPGLIVEAAKAGVSLAVLDVKDGTAVNESVKKLKEEGVEKVAVVSDKPEYIVDAARAGASLTVVEGRDSATVKESVTRLKAENVGEISFVSSKPELIIEAAEAGVPLAILEVKDETTVRESIKTLKEKGVKRVALLSDRPDLVLEALKS,0.04873738323755056,12.18434580938764,False,False,False,1.4892292022705078,86.73085885026522,0.0056,0.2087912087912088,0.41517
+107,F107,Fixed_Rd1_48,LM,6WVS,MLPLIRVNSLDRAVEIVKEFRDKGVEYIGVVSSRPDVVEKALEAGTTLTIVEAENLEDAIKTVKELREKGAKYTGLVTSDPKVAEEGLKAGVTLVLIEADDLSTATETVERLRKEGAEYVGVISSKPEVVKKSLESGATLALVEAKDLDSAVKTVRELKDGGSKYVGIITSEPEILEKVLKL,0.08324956966583831,20.812392416459577,True,False,False,1.1505229473114014,89.9793096191984,0.099,0.2802197802197802,0.73144
+108,F108,Fixed_Rd1_48,LM,6WVS,MLPLIVSKDPSSVTETLRELKNAGVEKFGVVTSEPRVLIEAVRAGLDLAIVVAEDENSLTKTLEELRKEGVGKYGVLTDRPEVLVKAVKSGVDVAFVTARDGGSVSETVKKLKDAGASKIGVISDKPEILVEAAKAGIDLAVVIAKDGESLSATLKRLKEEGVERYGIITNEPKVLLEALKG,0.11101001510221765,27.752503775554413,True,False,False,1.3169331550598145,88.29019890735734,0.0047,0.25824175824175827,0.76291
+109,F109,Fixed_Rd1_48,LM,6WVS,MIPTLTSKDEGKILETVKRLKELGVPRVSVVTKNLDVAVRAGEVGAEIATVVADSEDELIENAKKLKDAGIERISFVTDKPETAVKAAEAGSEIVTITSSDPSKLVENVKALKEKGVSRVSAVTKDLDTAIKAGEAGADIATIVAESEEELLENVRKLREAGVERVSIVSDRPEVIEKVLSL,0.062017321415891655,15.504330353972914,True,False,False,1.0949746370315552,90.00291529801315,0.11,0.23626373626373626,0.75715
+110,F110,Fixed_Rd1_48,LM,6WVS,MKPVIISSSLEEAVKSVRVLKEKGVKDIVGISDSPEVASRLAEEGTEYVLVSAKDEREAGETIEKLKKAGVENVTGLTDKPETAVELSKRDVEKVVITSSTLEDAVNSIKILKENKVKNIVAISGSPEIASELAKEGAEYVVVSAENEKEARETVKELRKAGVENITGLTDDPKTALKLLEG,0.0769709084937476,19.2427271234369,True,False,False,1.5414249897003174,80.88010187607468,0.045,0.25274725274725274,0.43877
+111,F111,Fixed_Rd1_48,LM,6WVS,MKVLIEVSEPSKAVETIRKLKEAGVENIAVVTSKPDLAISSASEGAEYVVLEVKDGETATKTIKALRDSGLKRVGVLTDKPEVAKESAKAGAEYVILKVSDEDTAVKTVRELKDAGLSRVAVITNKPELAKKSAEGGAEFVVLDVKDEETAVNTVKELRAAGLERIGIVTDRPEIVEKLLKG,0.1740522297376026,43.51305743440065,True,True,False,1.2983237504959106,88.48472686298933,0.043,0.23076923076923078,0.85539
+112,F112,Fixed_Rd1_48,LM,6WVS,MRVLLTVEDEETGKRILEKLKERGVDKVGLITSSPDVALKASDAGTDLVIVTAKDGGSAAETIRKLKEKGIKEVSVVSSEPKVVVEASKAGANLAIVTVKDEDSGKKTLEELKKEGVNKIGLVTSTPEVAVKASEAGADLAVVTAENEESATKTVRELREKGVKEISIVSSKPEIIEKVLSL,0.18326693405062214,45.81673351265553,True,False,False,1.3447251319885254,86.39240099374861,0.015,0.2692307692307692,0.86934
+113,F113,Fixed_Rd1_48,LM,6WVS,MRLLIEASNEKEALDIVEKLKKEGIERISAVTDKPEVARKLADGGVGEVVVRAEDEGTAVNVIKELRDAGLTRVAAVTTKPDVAKRLAEAGASRVLVEAPDEETATKTVKDLKDSGLQRVSAITSKPEIAKNLAKAGVSEVIVKAENKEEAVKVVKELKSTGLEKISSVTSDPSVALEILKG,0.30177932691580905,75.44483172895227,True,False,False,1.3554872274398804,84.05546735970879,0.023,0.25824175824175827,0.71213
+114,F114,Fixed_Rd1_48,LM,6W3W,PAGEAAVKDVVTKLVDAVNKKDSAALEPLLADSVKIVEDGKTTTGKAEVLKELKDGGAKDLKVSKVEKSGSKYTVTGTATIEGETSPITFVVEEKGGKPVISEITEK,0.2547350336464487,63.68375841161218,True,True,True,1.8296480178833008,88.3476609536108,0.0055,0.35514018691588783,0.8636
+115,F115,Fixed_Rd1_48,LM,6W3W,MSDADAVKAVVEKVNAAAEKKDEAGLKELLAENVKITVAGKTIEGKDKVLEALKSAPPSKREVGEVSVKDGTATVPVTVTAGGSTTKLTYTLTKSGDKWLISEIKAQ,0.4039984347925847,100.99960869814618,True,True,False,2.014538526535034,88.72275449950267,0.0077,0.32710280373831774,0.81728
+116,F116,Fixed_Rd1_48,LM,6WVS,MRVLVSAKDENSAVETVRQLKNAGIEKVGAVTSKPSVALRLASEGLDYVIVEARDVNEAAEVVKKLKESGVSKVGSISSDPSTAVSLAKAGLEYVVVDAPDLETATKTVKELKDAGVEKIGAITSRPEVAVKLAKEGLSYVLVEVKDVEEGKKTIKELREAGVERIGTVTDKPEIALEILKG,0.20136769663932136,50.34192415983034,True,False,False,1.80736780166626,85.90858703824641,0.018,0.21428571428571427,0.89501
+117,F117,Fixed_Rd1_48,LM,6CZJ,MTVAEKLAGKYTGTAKDDKGNEVTITLEIKKESDTEYTVSSTSKTKDGKEVKASGKAVLKDATTADVTLTGEDGKPVVGTYKIEGENKVSVTFKSPEGVELKGDLTRVK,0.40187536250134254,100.46884062533563,True,True,True,1.0732301473617554,90.77154708387503,0.33,0.22018348623853212,0.87436
+118,F118,Fixed_Rd1_48,LM,6CZJ,MSVLDKVAGTYVVEGKTPDGKPISGELKIEKKGENAVKVSGKLTSPEGKEEPLEGEATVKSETEVSATLKTKDGQEIKVTIKFKDADTAEYTVTLSDGSTITGTAKRKK,0.3269384803644804,81.7346200911201,True,True,False,1.342326283454895,86.96862546869347,1.7,,0.8631
+119,F119,Fixed_Rd1_48,LM,1QYS,MVKVTVTGTKDGKPVTKTFEVSSLDELEKKVKEEVEKLKSEGVTSVTLTVEAPDASTASKAKEIAENALRKAGYTGLSISVSGNKITITATR,0.23823675466455083,59.55918866613771,True,True,False,1.480959415435791,92.34708158697816,0.73,0.21739130434782608,0.8881
+120,F120,Fixed_Rd1_48,LM,1QYS,MVKVTITFTKDGKPVTKTEEVSDPSKLEDVVKKAVEEAKSAGATSVTVEITADTPEKASEAENVARKVLESLGYTGLTVKREGNKITVTATL,0.29713331758258826,74.28332939564707,True,True,False,1.650942087173462,92.96180670085404,0.82,0.30434782608695654,0.86817
+121,F121,Fixed_Rd1_48,LM,1QYS,MPKVTITGTKDGKPVTKEIEVSSRAELESKIKEEVEKLKSEGVTSVTVEVTAPDEASKKEAENIARDVLTKAGAKNVQVSVEGSTVKVSATF,0.283977524363175,70.99438109079375,True,True,True,1.661885142326355,90.57610364778702,0.79,0.2717391304347826,0.51044
+122,F122,Fixed_Rd1_48,LM,1QYS,MPKITITGTKDGKPVTKEVEVSDPSKLEEAVRAELEKLKSEGVTSATVTVKAPDESTASKVKEIVEKVLKELGAKDVSVSGSGGEYTIKATF,0.4962038371617647,124.05095929044117,True,True,True,1.7360239028930664,89.62163197601475,0.085,0.2391304347826087,0.59121
+123,F123,Fixed_Rd1_48,LM,1QYS,MVTVKITGTKDGKPVEKTLEVSKPSEIPGAISKLTEELKSAGVTSVTIEVTASSKEDAENAARVAEAVLKGLGYTDVSVKVEGNKATITAKK,0.2742051074536023,68.55127686340059,True,True,True,1.7395225763320925,89.79718220084987,1.5,,0.46493
+124,F124,Fixed_Rd1_48,LM,1QYS,MVEITVTGTKDGKPVTKTFTVEKPEDVPKVIEKVLEELKKEGVSEVKVTVKASSKDEAENVARVVEATLKAAGAKEVSVSGSGGEYTITAKL,0.35067152355882203,87.6678808897055,True,True,False,1.992031574249268,91.4011528958854,0.72,0.2608695652173913,0.79074
+125,F125,Fixed_Rd1_48,LM,1QYS,MVEIKITGTKDGKPVTETLTVSSPSDIDSAVNKVLEKLKSEGVTSATVTVKASSKEEAENVAKEVEKRLKDAGFKDVKVSGSGGEYTITATR,0.454025108550495,113.50627713762375,True,True,True,1.5798276662826538,89.53139849225488,3.0,,0.54812
+126,F126,Fixed_Rd1_48,LM,1QYS,MPKITITGTKDGKPVTETVEVSSVDEAVKKLEEKIKELKDAGVTSVTLTIEAPDESTAKDLASKAENALKSAGFKDVKVSVEGNKVTVTATL,0.3255327815620091,81.38319539050228,True,True,True,1.53373122215271,89.86168090861035,0.11,0.2608695652173913,0.85828
+127,F127,Fixed_Rd1_48,LM,1QYS,MVKLTITGTKDGKPVTKTVEVSSPEDAVNKVKEILEELKKEGVTSATVVIEAPDEKTAENAKRVAEALLKAAGAKDVKVSVEGSKITVTATL,0.47971685757209886,119.92921439302472,True,True,True,1.7418758869171145,88.89922296487072,1.5,,0.83836
+128,F128,Fixed_Rd2_Motif_11,LM,6CZJ,MTVLDKLAGEYEVSGKDAAGNEYKGTVKIEKEGENKAKVTRVITGADGKPVTSSGTATLESDTKLSVELTTEDGQKVSAVETIKSDDEREIVVTLPDGSKLTETWKRKK,0.42573097655642544,106.43274413910636,True,True,False,1.3811442852020264,89.96638561423804,0.061,0.22935779816513763,0.85718
+129,F129,Fixed_Rd1_48,LM,6CZJ,MTVLEKLAGKYTITGKTPEGEEISGTVEYKKEGDDKVSVTRVIKTKDGKEVKQTGTATLKSDTELSIELTAEDGSKVTGVETLVSENERKVVLTTADGQKIEAVEKRAS,0.1969993130688227,49.24982826720568,True,True,True,1.1307755708694458,92.23752939601708,0.16,0.21100917431192662,0.87003
+130,F130,Fixed_Rd1_48,LM,6CZJ,MSLAEKLAGKYTVSGKTPEGQEISGEYEFTKAGENEYKVVAKITGPDGKPVELSGTATLKSDTEAVVTAKDPSGREVTVTLTVKDADTIEGVGKAPDGSEIKVTGKRVK,0.400563139199727,100.14078479993175,True,True,True,1.1647963523864746,88.77604506515777,0.019,0.30275229357798167,0.89158
+131,F131,Fixed_Rd1_48,LM,6CZJ,MTVLDKLAGKYTIEGKTPEGEPISGTTEVTKKSDSEVALKSVIKTKDGKEITLEGVGKPVSDTSVKVELKAADGSTATAEYTFKGDDAVDVKVTLSDGTVITGTEKRAK,0.305597507227619,76.39937680690475,True,True,True,1.219199776649475,87.58026920980092,0.91,0.23853211009174313,0.80478
+132,F132,Fixed_Rd1_48,LM,6CZJ,MSVLDKLAGTYEGTAKSPEGKELKVTVTITKDGENKAKVVIEAPGPDGKPIKAEGEATLESDTSASFKVKTEDGQEVTGTVKLVSENEIDVEYTLSDGSKISGKLTRKK,0.4585908282289566,114.64770705723915,True,True,False,1.7507332563400269,88.66633659573229,0.047,0.27522935779816515,0.74559
+133,F133,Fixed_Rd1_48,LM,6CZJ,MSLADKIAGEYTVTGQTPDGKPISGKLTITKEGENKYSVKGTLKDPEGKEEPVEGTATLESETSAKVSLKTKDGKELSGTVEVVSDTEIKVTLTDAEGNKIEVTAKKAS,0.386734824659598,96.6837061648995,True,True,False,1.0890276432037354,87.8132647979346,0.19,0.26605504587155965,0.94502
+134,F134,Fixed_Rd1_48,LM,6CZJ,MSVLDKLAGKYEISGKDAAGNPVTGTSEVTKVSDTKASIKSTVTTADGKTIELSGEATEVKPGVVSAELTTPEGKKAKVEITVKDENTRDVVITLEDGSKITAVEKRVK,0.29136398284163545,72.84099571040886,True,True,True,1.4823777675628662,87.96171146295072,1.3,,0.88796
+135,F135,Fixed_Rd1_48,LM,6CZJ,MSVLDKIAGTYEGEAKDKEGKPVKAKLVITKDGGSKVNVELTVTTPDGKEKKLSGTGEPVSETSVKVTLKDEEGNEVTATIEFKDADTAVVSGKTPEGVEIKGTVKRVK,0.356295626261776,89.073906565444,True,True,True,1.7677292823791504,86.80593495491695,1.8,,0.83906
+136,F136,Fixed_Rd1_48,LM,6CZJ,MSPLDAFVGEYTGTLKTKDGKEVPVTVKIEKAGENEVKVSVSGKTPEGKEIKASGTAKLESETSAKVTLTLEDGSTVEATITKPSDDKLTVTGKNAEGQEISGELTRKK,0.43271550606435516,108.17887651608879,True,True,False,1.358462929725647,89.08745904116874,0.3,0.21100917431192662,0.68446
+137,F137,Fixed_Rd1_48,LM,6CZJ,MSVLDKLAGEYDVELKSADGKPIKGSLKIEKTSETEGKVTGTITTPDGKEEPVEGTVKASSDTEATVTAKTKDGQEVTLTVKVVDENKLEVSGKDAEGNEISGTATRKK,0.4135837874338196,103.3959468584549,True,True,True,1.2818390130996704,89.9499049610178,0.49,0.29357798165137616,0.69754
+138,F138,Fixed_Rd1_48,LM,1QYS,MVKVTVTYKVGDKEISETKEVSKPEDVPKIVEELVKKLKEKGVSSVTLTIEAPDKTSAENAARVAKAVLEGLGAKNVEVKVEGNKATITATF,0.1913173988144506,47.82934970361265,True,True,False,1.7961807250976562,91.92466852220964,1.3,,0.83248
+139,G139,Generation_Rd2_Distant_57,LM,N/A,MKAGAEEIKTSATSLKDSVSSGTVDPIVTEAGKVLDAAKAVKEKVSEADKPKVEPVVTKLETAATDLKTKGEAKDVTGLTTAVNEVITASDELVKLLPAS,0.20507356097996407,51.26839024499102,True,False,False,,90.45868949690686,0.076,0.28,0.39181
+140,G140,Generation_Rd2_Distant_57,LM,N/A,MAADVPPEVKAGLDSVNKAVEVSGSDATVEQVTGPLKEALEKYKVAAEKVTKPDGKTKLEGVIKDIEGAITAGEAKDLPKVKELAGKAKTSSEEIVKLLS,0.3328677079393693,83.21692698484233,True,True,True,,84.99285539872803,0.22,0.32,0.83491
+141,G141,Generation_Rd2_Distant_57,LM,N/A,MSPAARAELTETRNDVTKLIKTVKDGGSKSTIAKEASEISGDLDKAITLTAGKPKAVAELRETSADLKKLVATVKAGGSKSVIVKEAGEVSSDIDKVLAL,0.1551972832114287,38.799320802857174,True,False,False,,93.5872682057466,3.8,,0.49823
+142,G142,Generation_Rd2_Distant_57,LM,N/A,MSEIETVITGLTTARDSVVAGSKDGGKSLVEVGKTLVELAKAEGVSDDVKASIKDAGKPIVEAGKLLKEGEPPEDKVTEAVTKIDEAITALNAALESAQG,0.2756433114334177,68.91082785835442,True,False,False,,86.87348946971616,0.82,0.31,0.48043
+143,G143,Generation_Rd2_Distant_57,LM,N/A,MARVISKTITVSATGESDPIEVPEDEGKYIVAVNVTSLSGSPSVTVEILKDGDSTPEYTLTASAGETKSEPVKIKAKKLKVKVTAAGGSSGTATVTLVLE,0.027529949935088758,6.88248748377219,False,False,False,,83.71902057521575,0.5,0.23,0.48523
+144,G144,Generation_Rd2_Distant_57,LM,N/A,MVSEEDIKKAVENKAKEKLQEELSKYGVSATLTVTDVKVTEPPKITKDGKVIVVAEGSGTATIEAGPLKGKTASFRVRAEVDPSTGEVKSVEILEVTVSS,0.38875352995405293,97.18838248851323,True,True,True,,88.74604251379841,9.5,,0.44431
+145,G145,Generation_Rd2_Distant_57,LM,N/A,MATKDDVTKLVSSVTGLLDKVGSASTVPEIQAVAKELEGTIEPLEKKAKDGGAESEVEAVEKPAEALEDAAKAGSDKSAIDTAVTNLKTSLTTLAGKLSG,0.222212473773112,55.553118443278,True,False,False,,88.46027073589488,0.18,0.29,0.3727
+146,G146,Generation_Rd2_Distant_57,LM,N/A,MTVEEIKKASDTLVSSGTELKTAADAGNLDKVKELSATIVTSGETLQKGLEGKPEVLEAVNAVVTAGKDLKAAAEAKDVEKIKALTPVIVEAGTKLKSLL,0.41143414845372267,102.85853711343067,True,True,True,,90.46682388380894,0.75,0.19,0.78525
+147,G147,Generation_Rd2_Distant_57,LM,N/A,MNAALEKAKTATDVPSAVTAVKEAITVAGSKPEDAEPVVKAGVELVKVLKDGGSLDAAENVAKEVTTISGTPGVSDSVKTSASEGLTEITKLIEEKKSGK,0.091279198227127,22.81979955678175,True,False,False,,86.53972629608363,3.3,,0.60999
+148,G148,Generation_Rd2_Distant_57,LM,N/A,MSSLSDIKSTVASITDVSELEPVVEPLTEIIEGGGSDADKVEAVNVIGEVGKKLGEAGEYDVLESVLPPLEELAEDSSVSAEVKEAATKASETISAVTAE,0.18933746095612405,47.334365239031015,True,False,False,,88.14748754664437,1.5,,0.50912
+149,G149,Generation_Rd2_Distant_57,LM,N/A,MKALENAADEIKKSDSRITDVTASVSGTTVSARVTLAEGVTEDQAKPVLEEVVPKISEAATKGLKDAGEPSTLTIVAVSSKDGKELVRATSLGGSVKVEK,0.3332914704062595,83.32286760156488,True,False,False,,88.37161919753758,0.79,0.18,0.41947
+150,G150,Generation_Rd2_Distant_57,LM,N/A,MKITLTVPPGKTVKSDPIKAAGRVSASVKVTAKDSSKPAKVRVLAVPEGGSEPVTVVEKEVKAGETATLEPLDTTGYTEYIVEAENVGDEEATVTIELSG,0.336444292189329,84.11107304733224,True,True,True,,87.98001209003614,1.6,,0.53459
+151,G151,Generation_Rd2_Distant_57,LM,N/A,MAELDTIKSGLTDLKTKVDGVVADPSSVTPETVTGLSDSAKTLGESLEGSPVQEVKDAGKELSEASKAAGEETEPAKIVEKLTTVSTAVGKVITAAENAA,0.26587976795727936,66.46994198931984,True,False,False,,89.96433851537665,1.6,,0.41494
+152,G152,Generation_Rd2_Distant_57,LM,N/A,MATVKEVLTEISTKVTAAGEAKDVPTLTTLSGEITGLIEKLKPLVEGKPELTPVVDGLTAVSEASKKVGEDGSAENVAAFTKAVTDLKTSLDAGIKALEG,0.4991924215797286,124.79810539493215,True,False,False,,87.48442474556842,1.5,,0.47016
+153,G153,Generation_Rd2_Distant_57,LM,N/A,MPKYTVTLTVKLSGKPEPSKISEIENAAKSAAESVAGEGVSVKVTKVEVKDDGTATITLEVTASGSEDEAKKKAEELKNKIKEAVEKAGGSVVDVTVSTS,0.42333046694985677,105.83261673746419,True,True,True,,88.78546222717601,0.14,0.26,0.46378
+154,G154,Generation_Rd2_Distant_57,LM,N/A,MTEKITADVTKALEEAGVTPGKVEVSGSTVKVTLTVPEGTDEATAKTTAENAVNAAVKGLKDAGESVSSVRVIVVGKDGKELASATSLGGSITIKPAPSE,0.4531835261231916,113.29588153079791,True,False,False,,87.75473846202736,0.1,0.27,0.55879
+155,G155,Generation_Rd2_Distant_57,LM,N/A,MSDVKITKNEDGTVTATGTAVLTPEDIKAAGGSEEKAKELAEKRAKDSARESLRLLVKAENPDLKDEEIDKLIENIKVVSSKPVEGGVEVTVEASVKVGK,0.37388402065682946,93.47100516420737,True,False,False,,88.89236488239041,0.43,0.23,0.54614
+156,G156,Generation_Rd2_Distant_57,LM,N/A,MSDISATVTGLSESVTALGTAVSSGDVEGVKTAGEDLKAKLKELETSAEEAGAKDLVKEIKDAGKAVVKVGKDGGSPEEVTPPLTTLQEKLTELSTKLAG,0.33130961826060645,82.82740456515161,True,False,False,,87.8328885327107,0.068,0.31,0.87952
+157,G157,Generation_Rd2_Distant_57,LM,N/A,MKDVSDKAEAELKKIDGVEDASVSLQVTPPSFSGSITVKEGTDEATAKEIAKNAGKVAARVLEEEKLTEYTLTISVTGEPADGGSSKPVGSATFTKDDLK,0.3887420302984773,97.18550757461932,True,False,False,,85.66335758481124,3.3,,0.48156
+158,G158,Generation_Rd2_Distant_57,LM,N/A,MSEISTAATEVETAVNKLVSDIEGGSEPSFDSVSADLTKAGENLEKASAEAGSDEKVTSAVTTIKEGITLAKDGVSGKDVAKVKEGAEKIKTGLDGLKAL,0.24084138782798684,60.21034695699671,True,True,True,,88.22556618126865,0.58,0.2,0.46527
+159,G159,Generation_Rd2_Distant_57,LM,N/A,MPKITVENKSGVDATITLEAGSFKDSKTVAKDGGTAEFEVKEKPSEPLEIKAKADVPGKGSAESDPVKVELKEGETAKVTVTLTVGEDGKLSVSSTTTKE,0.2227111388280504,55.677784707012606,True,True,False,,84.9238082614045,0.67,0.33,0.49835
+160,G160,Generation_Rd2_Distant_57,LM,N/A,MEFRIKTSKPPEEVLKAVKEALSSDPKYKDVQIVSESPSEIVAKGRAGTATVRVSVKDGEVTISGSAEGGLITKSLVEKKLRELAENAARKVPGAEVVTG,0.24038289936562846,60.09572484140711,True,True,False,,88.59548342136148,1.5,,0.48746
+161,G161,Generation_Rd2_Distant_57,LM,N/A,MSDEVKSVVADLKAAVTSLKDAGSSAENLKPVGEKLKEIGTKLSGLTVPEDKKAKVTELAGKIKDGGEALVKAGESGKPDDVKTAITTIETAVSELEGLL,0.42792015185193416,106.98003796298354,True,True,True,,89.05788763849755,1.2,,0.7236
+162,G162,Generation_Rd1_48,LM,N/A,MSSEEITKATEDLTTAKDSGDTDKIVSAVETLVSVTGSPSDVPPEVQLASIEALGYVVENAEGGVADKVKEAGAKERLEELKDAGEPVGEAAGKVLEKLS,0.21005651574537504,52.51412893634376,True,False,False,,85.10657686493096,2.4,,0.43952
+163,G163,Generation_Rd1_48,LM,N/A,MAEVEISVEKKGENEYVVSVKVSGKPGTPPSEPVTATSLDDAVNKAKEKIKEALEKAGVTELKDSDTITIKVTVKEGEEGGSKTVTKTLTVGELRKLLEG,0.1357011772016441,33.925294300411025,True,True,False,,85.69054945839417,0.77,0.18,0.42333
+164,G164,Generation_Rd1_48,LM,N/A,MKTTVTLTADKPSEPVAVNAAENKASKITVKVTKAPEGGSATLKVVGKKDGKDVTVLEAKEVKAGESVTSDPIEGLTEYTVSLSGVPAGSTAEAEITVEK,0.364473906674991,91.11847666874776,True,True,False,,86.88541224847904,1.2,,0.52102
+165,G165,Generation_Rd1_48,LM,N/A,MSELENKIKEALSSIEGVEVKVGEPKVEEKDGKKVVTSVPVTITKDGKTYTLTVSKPEGSDKFRATVTAPDGTVVASAEGDTPEDAVNKAVEKLKEILGG,0.323444357381488,80.86108934537201,True,True,True,,86.51952648831619,0.52,0.23,0.54128
+166,G166,Generation_Rd1_48,LM,N/A,MATAKDVKAVVEKVEKGEPVTAEDLKPLIEGTKDSSFTVRVNSITGIGKLVEAGKVTPEVAKEALPVLEELRKDKTSVSGGQTVGGAAGSAISKIKKASK,0.07451008573836172,18.62752143459043,True,False,False,,83.85604869710298,1.2,,0.50281
+167,G167,Generation_Rd1_48,LM,N/A,MAGKLTVKVTGLSASDVAKVRVTATVTPSGGSPVELKPEEKEIKAGETSVTFEVSDIKEGDKVSVKAEALKADGTVEYTGSKDNVVPGTNEVTITLEKKS,0.2552751535613065,63.81878839032662,True,True,True,,85.82734665883672,0.21,0.22,0.76777
+168,G168,Generation_Rd1_48,LM,N/A,MSAKEVAEAFVKAVNAKDAAGVEKLSDGGSLTAENVQKVLDLIEKAGEYTVTDVGEPEKRETSSGTITVVPVTLKGSKDGKPLEFRVSVKDDKVTGLYIK,0.22973311193269697,57.43327798317424,True,True,True,,83.80963016732258,0.12,0.35,0.76997
+169,G169,Generation_Rd2_Distant_57,LM,N/A,MSVQEEITAKTTEISEALSSGDADKVVTGLTDLVTLIEKAGEPPGPVLENAATVLGTIAENKDVVKGKPEVVEKLKELAGKVEGGSDEVKEAVGRVSASL,0.3692888793736263,92.32221984340657,True,False,False,,83.33180243087995,1.7,,0.48083
+170,G170,Generation_Rd1_48,LM,N/A,MSPEDQKKLEELKPKIEKAAKDSASSSGADVKAEVKVEGKTAVVTLTVTVPDGGSKDDAKSTAENVAGSAVSEIKKEAPEITSVRVQVKSGDTTLVDKTS,0.07272598231771801,18.1814955794295,True,False,False,,86.38503262080307,0.62,0.2,0.54976
+171,G171,Generation_Rd1_48,LM,N/A,GKLEFTVKFSEPVDPSTIKAENIVVKVGDKEITLKPEDVTKVSDDGKEYKVTVTLTEEQLKAIKEAGGKVSVSIPEGAVKDAAGNENKASEIKDNTVEVK,0.353917321190868,88.479330297717,True,False,False,,88.99491429469761,0.11,0.3,0.64077
+172,G172,Generation_Rd1_48,LM,N/A,MAEYTATLTTSLPPSKVLEVTKSTLESEGLTITSSSDKEIKASGKGVSATVKVEAKDGGSVVTVTAKAGGLIGKKRAEELLNKIVEKLKSAPEVKDVKTS,0.08443556471481162,21.108891178702905,True,True,False,,85.9582238766821,0.16,0.2,0.69889
+173,G173,Generation_Rd1_48,LM,N/A,MSVSEEVTKASDAVNAAVTATDGSKSADEISTAVGEAKTTLEGLKDKVTGDAKTLVEEAITSLGEVETKVKDGGSPEDIKAALTPVAEKLKEAAGKLPSS,0.35514819735560493,88.78704933890123,True,True,True,,90.5134869098054,0.34,0.25,0.63993
+174,G174,Generation_Rd1_48,LM,N/A,KVTLDKAEIDPKDGSAKITGKVENVKEGEVSVVIKDSEGKEVAKGTATVKDGKIETTIEGLKLEKAGKYTVEASFKPTEGQEPVTSEKKELTVPVKVTLD,0.49288849535230767,123.22212383807691,True,False,False,,85.49357017737616,0.014,0.41,0.54795
+175,G175,Generation_Rd1_48,LM,N/A,MSDEVKAKIEEAIKSSGLPVTDVKVEGTTATVTVSDPSLVPAVLTIVAGAVKGVEGVSEITVKVVGSDGKELASATLKDGEYTVNGQKVTKPEDLEKLLK,0.43620360927076324,109.05090231769081,True,True,True,,87.11474597462664,0.99,0.18,0.5706
+176,G176,Generation_Rd2_Distant_57,LM,N/A,MKASEILAKLSGTYKDDEAGLTVEIKDGKVTVSAEGESAVLENVTSVDEKAGTATVKGKDEEGADVTLKITFSDAKNPSKVSVVEVTEDGETSEPVELTK,0.3844477984588167,96.11194961470417,True,False,False,,86.20851780777167,0.034,0.2,0.66728
+177,G177,Generation_Rd2_Distant_57,LM,N/A,MAVNKEETSEPITVPEGKKVKSVSGVATNKAGDGGSATLTVTIEGAGEPVTKTVELKDADTPFEVTGLDVPAGSTVKVTAKLSSDKPGTAEIKDLRVVLE,0.2654726003622822,66.36815009057055,True,False,False,,86.41534731482963,1.2,,0.58678
+178,G178,Generation_Rd2_Distant_57,LM,N/A,MATVQEIKDAVNAVLDSVEKKDLEGAKTSAETLKTKAEELAGSSDIPADVKEKVTTAATDAGTAAENLVTAVEGGSGDVSEPVTKLKEALTGLLSVIEGK,0.425398676935431,106.34966923385775,True,False,False,,87.68901877999998,1.3,,0.64483
+179,G179,Generation_Rd1_48,LM,N/A,MSEELRKAVENAAKEAAEKRVKEYLEKNPDLKDVTISDVKVVSVEEVPGEPPKKYKVTLTVSGTKDGKPVTATVTDIVEITEGGSPPTVKLLESKIETSS,0.29900936277246376,74.75234069311594,True,True,False,,86.02743373232883,1.3,,0.57502
+180,G180,Generation_Rd1_48,LM,N/A,MKDGEIKEVVKVLVEKLREGAENGSVTTISNAATGLGKVAERLEDDEIRDTVKEAVRVLIEKLKEGVENSSVSTITNTATSLSKVAGRLKDSEIKDAVKE,0.3213427087290815,80.33567718227037,True,False,False,,86.20820028410907,0.23,0.23,0.66443
+181,G181,Generation_Rd2_Distant_57,LM,N/A,MPKYRVVATVSVSAGSKEEAEKKASELVEKIKAKLEEKGLKDVKVTGTSLTEVSGGYTLTVTITAESDKPPSEIENAVREALKEAGVGEPVISSVTKVEG,0.25384446588434895,63.46111647108724,True,True,True,,88.54541706281088,2.7,,0.50116
+182,G182,Generation_Rd2_Distant_57,LM,N/A,MATLQEGVTTITGLVERLGSATSFDEVKTVAGEISDALKPIVEKAKESGEPSEEIKGPVTDLENAVKELKDGAEAEDSSKVTAAVGTVSTKLTELAGKLS,0.3724929920808958,93.12324802022395,True,True,False,,87.92727221288574,0.045,0.28,0.59423
+183,G183,Generation_Rd1_48,LM,N/A,MKVTRAENALRVVSSAVGQYTLEKGEPPKSLDDLVKAGYLKEDEAKELIEEVKVEGADAGTATITGKVKEGDKPVTVTLTVAKDGGSVTKTTTVDGKPAS,0.3468407943058762,86.71019857646905,True,False,False,,85.3267939260983,0.00012,0.26,0.54095
+184,G184,Generation_Rd1_48,LM,N/A,MAEITSGSKTVSGSETKLTVKAGETATVTLSADVTEIVVEKDATLTIEAAEGVKVGDKPITVEAGGKLVNNGTVTGNVTVKDGGSLEGKEVKGEAKIEKK,0.023438156178967277,5.859539044741819,False,False,False,,86.76730231087329,0.47,0.25,0.7231
+185,G185,Generation_Rd1_48,LM,N/A,MPVKVTITAPPGTKPSDVKAKIESEVIPKVLEEVGKPELAEKLKSATLTVTENPDGSVTAVLDVSGLSKEEAEKVAEITRKKLVEAGVPEDAIKVEVVEG,0.20753040061384626,51.88260015346157,True,True,True,,87.18989491808568,0.35,0.34,0.57824
+186,G186,Generation_Rd1_48,LM,N/A,MLVEITTSSGTKLVNAENVVSVTENSDGSATISFVGGSTLTVTGETVADVKAKLKPVIEGKIKEAEDKVAQLEKDGGAGSPEVTAAKAELERLKAVLASL,0.4051587468926507,101.28968672316267,True,False,False,,91.88900529299924,0.36,0.24,0.53372
+187,G187,Generation_Rd1_48,LM,N/A,MDLSEIKALVAAGKYEWDEGNVEKIAKHGVSPEEVEQVLENKPLIEPVPSKVKGETRFRVTGLTDGGRLLTVVFTVRGDDVIRIISARDATKKERKEYEG,0.16448825393008118,41.122063482520296,True,False,False,,86.01142421786548,9e-20,0.51,0.845
+188,G188,Generation_Rd1_48,LM,N/A,MSDSAVKEGKELLEKAKSTSDPLDKVSLLTDAATKLKDGGAPESEIKPVLVEAGKTGLSIVEKGGSSVPPSEKREILEDVAENLETAGEPDLAKEVRSKI,0.28479652189612137,71.19913047403034,True,False,False,,87.2741586468708,0.89,0.36,0.51479
+189,G189,Generation_Rd1_48,LM,N/A,MATFTKTVTLEAGKPSEPITGLSGRVKKITASISGGSAENVSAKLVVEKEDGTTETIELKAGESSVTKDLDPPVDVKSVKVEVTGAKEGEKATLTVTVEE,0.13369887066922734,33.42471766730684,True,True,False,,87.77665895300834,1.4,,0.49889
+190,G190,Generation_Rd1_48,LM,N/A,MKLVLTSSADVEYTISVNGGEPVTGTLKAGETKEIEVPAPEEGKPITVTVKVTAKNVKDGGSVSAKLIKVDKDGKETVVAEDSKSGSEATVELSKTFTVQ,0.14553392066270446,36.38348016567611,True,True,False,,85.23398740416752,0.49,0.22,0.55118
+191,G191,Generation_Rd1_48,LM,N/A,MARFRVTLTVTVKDADPSKVEEAKNAVKEALSSIEGVKVLDVRAGPVGGSYTITAIVEVPEGQEDKAKELAEKAKSEAENKLKEKGLEVTSVSASATPLG,0.07282996354878321,18.207490887195803,True,True,False,,87.53061091129207,0.91,0.2,0.60413
+192,G192,Generation_Rd1_48,LM,N/A,MSEITSLTTATLSSPSKTPVIVTASGSDITVRVLKDGKLEEAGKYSTGLVGKINSVSAKETGKDSFTVVAAGDGGAVELKVTVKEGEDGKPEVEKAEVVK,0.26659004459356195,66.64751114839049,True,False,False,,89.59009024552489,0.25,0.29,0.40602
+193,G193,Generation_Rd2_Manual_24,LM,N/A,MAVSSDVTVKAGKTLTLEKDSEVTGTITVEDGGSLILEEGAKVTATSITVKSGGSVTLKKDAELTAPSADIVVEAGGKLVLESGSKVSGKITNAGEIIKE,0.22727875498455913,56.81968874613978,True,True,True,,92.68699567297864,0.00042,0.31,0.8495
+194,G194,Generation_Rd2_Manual_24,LM,N/A,MAEFRVTGTATIDGESVPVVATVSDSEVSLAPEDGGDLPEITVPLSSISGLEKDGSTVTLTVLDEEGEPTEYTVELESADDAGEFVKAVRKALVEAKNAA,0.13058535749075587,32.64633937268896,True,False,False,,89.80239142712125,1.4,,0.72477
+195,G195,Generation_Rd2_Manual_24,LM,N/A,MKSLEGKKVLIVGGLDRLRPEYEKKLKELGAEVTSFSGRESRKTVEKITKEIKDGKYDLVIVLTNYVSHKVTTAVNKAAKEAGVPVVKGSPKRILEEISK,0.2438046900032924,60.951172500823105,True,False,False,,84.283443401976,3.6e-07,0.37,0.73891
+196,G196,Generation_Rd2_Manual_24,LM,N/A,MADISGKYEGKVSGEEEGDAVLDLKQDGSKVTGTITVPSENIKADVKGSVDGDKLTLEIEVPEAGETAKAELTVKDGKLSGTVTAEGESAPVELEKKTSS,0.03521260291153069,8.803150727882672,False,False,False,,88.46053178719052,1.8e-07,0.39,0.88997
+197,G197,Generation_Rd2_Manual_24,LM,N/A,MTKEELAAKLKGKTFVGEAEGVKVTLEFKDDGTAVITVDVPGQEPETGTYEVSEDGSTVTVKAGDEVVATGKVNDAGEIELTVEVEGEPVTIKLTEKPAS,0.31272403972533674,78.18100993133419,True,False,False,,90.26839772316754,0.01,0.28,0.70725
+198,G198,Generation_Rd2_Manual_24,LM,N/A,MATLEEVIAKAESDGKVSLAGEGLTDADVPKVVELLKSGKITSLDLSENKLTEASVDAIVAAVKEGGSKVTEVNLKGNEIKDSAPIEKLKAAGVTVTGVE,0.33709740965070456,84.27435241267614,True,False,False,,86.36919238098604,0.00021,0.35,0.85039
+199,G199,Generation_Rd1_48,LM,N/A,MAEYTSEPISLEGKKIKAVKVTLKEAPSSGTVTVELLDENDNVLATFTGLSVSAGETKTLTPPSPPTIPAGTKKVKVRVTASGSDGGAKDVVVEVIVEEE,0.10049578289951612,25.12394572487903,True,True,False,,86.52507613680642,0.39,0.29,0.64192
+200,G200,Generation_Rd1_48,LM,N/A,MSPEEILKELESKVSSGEISADEALSLAEKAVEGGSVDVLVKAAEVSAKAGKPEVTVKIVEKLIEKGVKDKRVIEAAINAARDAGDTETASKLEELLKTL,0.40972010352290444,102.4300258807261,True,True,True,,88.52770732744686,0.077,0.35,0.61949
+201,G201,Generation_Rd1_48,LM,N/A,MSEEVNKIKELVEKASGQYKDGKFDEGKTTLGEASTAVTGLEEKVKAAGGSAEITGPVTELKTSIETATKDVEAKSADAGTKLEDVSKKLTDLAGKLPAA,0.30720398423712897,76.80099605928224,True,False,False,,90.41036662944973,0.13,0.27,0.65927
+202,G202,Generation_Rd1_48,LM,N/A,MKLKDGAKLVIPPEIVEKIKEKHGVSEGEVLEALKNAAENSVTIEPSKTKPGRFRVTGLSGSRILTVVVEELDDGTVKVITARDASKKERREYEKAVGKK,0.0940098747249764,23.5024686812441,True,False,False,,82.49738499306618,4.1e-08,0.36,0.79272
+203,G203,Generation_Rd1_48,LM,N/A,MTIVRVFDSREKAENAVNELKSAGISSDAINLVSGEEGAKKLTEAGVPEDEARVYAEGVKRGGTVVTADVPDDKVSEVESILEKYGGSKPEPPSETPPAS,0.17221999174676836,43.05499793669209,True,False,False,,84.4356925716466,1.7e-09,0.37,0.81654
+204,G204,Generation_Rd1_48,LM,N/A,MEITISGKDLSAENAAKLKEAVTTAVNDGGSVKVTLTVSVPEGESDPKAAGQKLADALEAELKTVAGDKASSITKGEPVVDTSKPAGTPGVTATVEVTKS,0.12751274187278755,31.878185468196886,True,False,False,,87.70044338976987,1.0,,0.43692
+205,G205,Generation_Rd1_48,LM,N/A,MTPEEALERIRAAVAAGRVTVTKHVEKRLKERGLTTRDAVVSAVLAKAGTAELIEDDGGSKKYKVTGEVPGKDGKPVKLVVIVSLSDPSEPPRVITAYPA,0.20831051512177579,52.077628780443945,True,True,False,,84.10098394565233,0.071,0.32,0.77711
+206,G206,Generation_Rd1_48,LM,N/A,MTLTTATVDDAGKVTEISVTGLPGGSDPKAAGEKTAENVAALLEAVGAPEDGRKEVLDKLKEGSASLSEPVKVTKDGVEYTVTSSTVSGTPSFTITAKPA,0.32079452603050557,80.19863150762639,True,False,False,,84.62503673562736,1.4,,0.59321
+207,G207,Generation_Rd1_48,LM,N/A,MTIKSATLTATVTPPADAPEGSEITVSAQLVPVGEDGKPGDPIATSDSVTVKAGEEPKEVTLKLEGKDVDVTKGRVAVVITASGTEPVEVKVSNVKLTVE,0.32624960566135874,81.56240141533969,True,False,False,,87.13537223500181,1.2,,0.482
+208,G208,Generation_Rd1_48,LM,N/A,MLTEEQKTAIENAAKEAAEAKIKEELEKAGYTVSNVAVNKDSIVVKEITDTSATVEVLVDADVTKDGETKTLKGVKVSVPVTISEDGKTVTVGTPTVTEG,0.41753954616702316,104.38488654175579,True,False,False,,86.20522534717749,0.18,0.31,0.33752
+209,G209,Generation_Rd1_48,LM,N/A,MKSLAENVAKEIEKRLKDKGYTVTSVSAGETSATITASKPGKPDLKIIVEVSGSTVKVRVEEGGKVVGEGTATVPPGTPPSEAVNKAVDEAIKKAEEKLK,0.31402878229989095,78.50719557497274,True,True,True,,87.296913956626,0.9,0.2,0.52248
+210,G210,Generation_Rd1_48,LM,N/A,MAELSASVSGKTLTATAKVTSTEDLKGAVLVITFVKADGSLAGVGGSEPVDIKAGESKDVTVTAENVPEGTTKVVAAVGQVTDLENLKPISDPVEVTITG,0.10715110741757516,26.78777685439379,True,False,False,,86.36463523027102,0.68,0.25,0.75625
+211,G211,Generation_Rd1_48,LM,N/A,MRISEAVRVATLKVLAENPEGLTVKELVEKVPKIVEEITGKPIKDVTSLRNSVSAALSRLSSKPPSPVVKAGEVTKDGRKYTVYKLTEEGKKLLEELEKR,0.15429707413853394,38.57426853463348,True,True,False,,88.44467056034608,0.9,0.22,0.71473
+212,G212,Generation_Rd2_Manual_24,LM,N/A,MAVAGTYKITSDIPGVGEATLTLKEDGSSVTGTLEVPAAGGSAEIKDGKVEGDKVSFTYTVNVPGQEPIEVKAELTVKDGKLSGTASTPFGDAPVSGEKA,0.13229364622976908,33.07341155744227,True,True,True,,86.99826238600645,3.2e-11,0.43,0.86711
+213,G213,Generation_Rd1_48,LM,N/A,METATEFVEAVIAGDYEKALELSAEQLQEGLTVEKLEEAKASLDAAGEYDSVGEPTTREDGGYTIVDVPVTLKDGSEVVLTVSIENESGKVAGLFVKPKE,0.27084891141880163,67.7122278547004,True,False,False,,86.5225409631302,7.2e-07,0.36,0.6981
+214,G214,Generation_Rd1_48,LM,N/A,MKVEISNVTVSGEGLDRNAVVSAVNSKKSSIEKAAKDAGVTSGTATVKLVIGSDGKVKSATASASGVSSSVASKIASEVKGLSFPSTGKETTVTIPFTLK,0.03139902999505464,7.849757498763659,False,False,False,,88.22988155893422,2.8e-05,0.34,0.88246
+215,G215,Generation_Rd1_48,LM,N/A,MTVAEKTLKNAAGQKVKAVLEASEDGSTGYVVITKAKDGKELDRVEISGEGLTDPEVSVSDADGGPVVTVTGTTAEGEPATFTVDLSKGKKPKVTETSAE,0.32056675575624544,80.14168893906135,True,False,False,,82.37853019011793,0.53,0.28,0.64831
+216,G216,Generation_Rd1_48,LM,N/A,MTLQEYIDKLKPSLEEGAKNAGSPIEKVEVKASDDGKSITATVTVKDGTPEDVIKGLVEGIGGSVLDSAKEAVPDAENVTLTVVGVSSSGKELARVTKTF,0.41491778749891783,103.72944687472946,True,True,True,,86.08404559468715,0.89,0.21,0.54895
+217,G217,Generation_Rd1_48,LM,N/A,MSITEEITKAVNEATSLSGSDPSKALEILKPVVEKAEKEGAPPLTVTRVKFVYATVASSVDKELAGKVLDEVIEDGKKLGESEPEVKDLVSAAENLKSTL,0.3071582954114238,76.78957385285595,True,True,True,,91.0878333852068,0.83,0.24,0.82118
+218,G218,Generation_Rd1_48,LM,N/A,MTQEELDRIEKELPPKVKAVPEVEDASVSLSISGGSTSLYTTVTAKEGLEGEALEKLAENVAGVVKDEITKSDIKDKYDSVTVSATTKDGKSGSKTIDLK,0.3570257326165188,89.25643315412971,True,False,False,,83.7127830370605,3.1,,0.57739
+219,G219,Generation_Rd1_48,LM,N/A,MSEETVKELTEISEGLSSGEEDVTATLEKLLPVIEDSSASDEAKVKALEITGDILENSADDVPPEVVSKIKSVVEPLKDGGSDAVKNAAGEVLDTIAEYQ,0.2836327323448656,70.9081830862164,True,False,False,,84.70923580434633,0.18,0.29,0.43856
+220,G220,Generation_Rd1_48,LM,N/A,MEITKEQLKEIIREAVKEVLEENKDIKAPEVSAKVEGDKVVVTVKPPSDADSVTLTVTPDGGSPITGEKKSDGTYEFTLSEPLKDGQKVTATATKDGKTS,0.3061561526382395,76.53903815955987,True,False,False,,86.83595052652292,0.17,0.33,0.50191
+221,G221,Generation_Rd1_48,LM,N/A,MATITKTATVTLDSPIKAGETVEKDVEVTGLPENAAGVSAVVASLSEDLKGKLTVSVLEAEISGTTAKFRVRITNVSGSDITPPAGSTSVTVKVTAIVEE,0.28516372438257664,71.29093109564415,True,True,True,,87.93084613994105,1.7,,0.55774
+222,G222,Generation_Rd1_48,LM,N/A,MPEFRVKLPPEKAAEIVERVLKEKYKDVTITSKSVSGGKAVIEGTKKGLLSSTTVKVTVEVSSDGTATVRAEPVGSLVSASKILEDIKNALREAGAEEVK,0.06549880772623917,16.374701931559795,True,True,False,,85.3883913690933,3.1,,0.77698
+223,G223,Generation_Rd2_Manual_24,LM,N/A,TPPAPPKASDFAGTYVGTDEEGEKVTVTLTVDAAGNVSGTAKDTEDSSEPVPLKGKVNADGKLEIVEDGEVVATGTLSADKKTLVIEAEGESEKITLTKQ,0.09947732072583444,24.86933018145861,True,False,False,,87.09331399353077,0.05,0.28,0.73171
+224,G224,Generation_Rd2_Manual_24,LM,N/A,KPEIKVSLDKDSVEIEAGKSAEVTVKVENVPEGTKVTLSVSGEGVSASLEPSEITPPGEAKLTITAGDKEGEYTVTVTATSSDGKTTGSKELKITVKKAE,0.28115350681597817,70.28837670399454,True,True,True,,90.74575985194588,8.2e-08,0.44,0.87605
+225,G225,Generation_Rd2_Manual_24,LM,N/A,MATVVRAEGEIDLDTAPELSEALEKAAGGSDGRPVVLDVSGVTFADSSFLNVLLRTHERVGKLVLAGTPPQVARLLEITGADKVLTVKDSVEEAVAAVSA,0.13122382806108723,32.80595701527181,True,True,False,,76.47276497468523,1.1e-19,0.55,0.83766
+226,G226,Generation_Rd2_Distant_57,LM,N/A,MVTATITVSASDPSKAEEVAKNVVTTKLTEKGYTVKDVRVSGDGKVTAVIEVTGGSEDEIKKAAENAAREAGEEAKKELEKEGLTVTSVEVKVEPPKPSS,0.18208726366160208,45.521815915400516,True,True,True,,87.55560444750809,10.0,,0.54279
+227,G227,Generation_Rd2_Distant_57,LM,N/A,MTPEEITKAVNDGLTEGLKGSPITDVKAEVTGEPPSFTVTATLTVSDSVSADEAKTILEGVAGGAAQALKDAGVTSGTVKVVGVRASDGKELASVEKPLS,0.20885437685969077,52.21359421492269,True,False,False,,85.7801244236998,0.21,0.23,0.44718
+228,G228,Generation_Rd2_Distant_57,LM,N/A,MSELLEKLKASLGSTVSVKVTREAKNAAGEPVKEVTEYTGKLSAVNEADGTITLTDVTVTKTTPPGPDGKPVVEKKETVEIPISSIESVTFTKDGQTVTA,0.3398507202768766,84.96268006921915,True,True,False,,86.87667869887674,1.0,,0.4184
+229,G229,Generation_Rd2_Distant_57,LM,N/A,MEKKTVTVSAAGESDPIVVPPGRPARVRATLTSSGTADVTLVSVPVKDGKPDDSKVLATITGLKAGETKELEIEAPEEGVAVKAKVTGGSASLTVEVEYE,0.25227839975840133,63.06959993960033,True,True,False,,87.79723699857843,0.067,0.23,0.46773
+230,G230,Generation_Rd2_Distant_57,LM,N/A,MVKDSATTLSTSVTDLTTAVGEGKSLEELKPLAETIKTSADTIVEKSKEIEGSEPVVEAANKLVEVSKVIADGGSLDDVKAKAGEVKTASDALVTAAGGK,0.2338792115122648,58.4698028780662,True,True,False,,90.34305763417996,0.39,0.25,0.70741
+231,G231,Generation_Rd2_Distant_57,LM,N/A,MDKKEFGAKVGEYGKLTVPVVKEPSAENVTKAGESVTGLVEAVNAADVSEEVKTSITTAAETLKSTIEGGGSKDEIKTAVIALRDATVAAGKELGVELPK,0.25967684498058474,64.91921124514619,True,True,False,,86.18377510050942,1.1,,0.52956
+232,G232,Generation_Rd2_Distant_57,LM,N/A,MAKIKVKVTGLPKGTKEVTVTAEYTPVDDEGNEGEPEVVAEAEKKNVKAGKSATFTLKDVPDGGSVTITASAVLADGTELSDSEEVEVSGKTVKLTLEEA,0.16627183065390522,41.56795766347631,True,False,False,,87.07187992662959,0.76,0.22,0.60576
+233,G233,Generation_Rd2_Distant_57,LM,N/A,MTKIEITGPITAENVQAVKDAIAKASGDVEVVISGTELDDGGADAVGEAIVEALEKGSKPVKVTVAAGSEPATILKEVLEEELKDLEGKKLSDRVSVSAA,0.22449890530402983,56.124726326007455,True,True,False,,85.88362975688092,0.87,0.22,0.64094
+234,G234,Generation_Rd2_Distant_57,LM,N/A,MSEISSKITELEGKDLSSAENVKAVGEYVSGTVVPKLEELAGKAPEGEVKTLVEKVASDAKALGEAGSAGDTAKVTEIVTKLKEDVTSLTTKVNELKPAA,0.31250271876593766,78.12567969148442,True,True,False,,85.75613746041445,0.13,0.21,0.69023
+235,G235,Generation_Rd2_Distant_57,LM,N/A,MSDPVKDAVSKIETAVTDLGTAIESGDKEKITSAGTTLTEASTSLKEAAGKLEGEKKTVVEGLSEKVTKAGELAKAEPVNVAELKPVFDEIKADIAKLKG,0.37075495020892035,92.6887375522301,True,True,True,,89.51803412698082,0.088,0.25,0.44611
+236,G236,Generation_Rd2_Distant_57,LM,N/A,MPEYIVTVDVAGGSREAVNKAAEKAKEIVEKVLSSKPGASLKDFRVSEPVEVSGVKVPSATLELVVEGEDAGKLAEEIKKTIESETGLTVISVTARPLKG,0.24397498074815135,60.99374518703784,True,True,True,,85.51463768596741,5.6,,0.62557
+237,G237,Generation_Rd2_Distant_57,LM,N/A,MATVSEIKTLLETAENKVEEAITAAGTDGGSDSGVKPILTEAKDAVTKAEGEATSPEVKELLEKAESKIEEAISAAGSDGGAKPVLTEAKDLVTQAEGKL,0.27265198104308813,68.16299526077204,True,True,False,,87.58504732555899,1.3,,0.49624
+238,G238,Generation_Rd2_Distant_57,LM,N/A,MEAENAKLTELEKKLPPEVKAVSADVTDVRVTATLKDGKYTGLIVTTTVKEGSDGGALGERIAGEITKVVKDSGVASEVGTPSVQVLDAAGKPLGSKTFG,0.1961497751760811,49.03744379402027,True,True,False,,86.26746860832306,0.79,0.23,0.45217
+239,G239,Generation_Rd2_Distant_57,LM,N/A,KAEEERKAKEAEEKRLAEIKKITDSVKSSVSGVTVGTPTEKDGKLVVPVTVSSASNAESTAKTVLTKLKDAGYTGEVIVEYKGSKVATSTLSGGSFTISK,0.15768906005559827,39.42226501389957,True,True,True,,85.66091734445378,0.75,0.26,0.44084
+240,G240,Generation_Rd2_Distant_57,LM,N/A,MSSSEITGLVTTLTESVKSLDGGSDLETATNAADTIKIIAETPENVGPLKDAGAVEAVKAVVTKLEELSGKEGVSDEDKTSAGEVLTVAKEALEVIEKAS,0.2445097638285768,61.1274409571442,True,True,False,,88.87406036196427,1.5,,0.46387
+241,G241,Generation_Rd2_Distant_57,LM,N/A,MSDDEIVEKLTAELKKSAENAAKDGGSPISGLITDVKVDRAGKTVTATVSESVPPEVSTAVGEVLAGVAGPVIKASDPSLEGYRVVVTGADGKELGSKDL,0.2825679200185735,70.64198000464337,True,True,True,,84.74036519803893,0.7,0.29,0.57643
+242,G242,Generation_Rd2_Distant_57,LM,N/A,MSESEIISSITSATSTLKSSSPSDADLVTALTTLEDTLVIAVNSGKPEFVKAAKDGGAVEAVKGVKERAEKAGEEGGSKEVVDRAGKVLELLEPPPAPSA,0.2989346108710877,74.73365271777192,True,True,True,,87.48056595556326,0.56,0.29,0.41786
+243,G243,Generation_Rd2_Distant_57,LM,N/A,MSITVTVKDGKVVISGDGGSKEVEGLDKLTETLKSEIEKAVKEGKPVKVTIEADSSVSEDDVNKIKTAAENAIKELKDKGELPSDAKVPENVEVKVEKKS,0.3876533576037763,96.91333940094407,True,True,True,,88.55976311534685,0.27,0.24,0.37726
+244,G244,Generation_Rd2_Distant_57,LM,N/A,MATVKEGVTSARDAVNKTLESLKAEPVDVEKVKADLTTAKDSVAKAKETAEKDGKPEVVTGLTELDASITKAGEAKTVEELRAALEPVSKSLSDLAGKLG,0.35559134729502334,88.89783682375584,True,True,True,,90.20816445050598,0.14,0.22,0.53916
+245,G245,Generation_Rd2_Distant_57,LM,N/A,MSIKSDIEAIVTKLNAATTPEEKKTVAGELSTKAGELKTSLEGKEVPENVKPVVTDLVEKVTALRDAAVKAKDGGADVSAEITAVQTSVTEISGKLEPLK,0.10007647042899974,25.019117607249935,True,False,False,,85.16136920836401,3.2,,0.76591
+246,G246,Generation_Rd2_Distant_57,LM,N/A,MSISEQVTEIKGLVTAIEGATSAEEVKTKAGELSTKIKELKDSVKDKVDGDLLTAVSDAGKALKEAGSKPDASLDDVKAAVTTATEKLNEVVAKLEPPAA,0.3820963191385863,95.52407978464659,True,True,True,,87.18511383337285,0.11,0.28,0.4967
+247,G247,Generation_Rd2_Distant_57,LM,N/A,MSEVISDLEKGKTLVTEAVNAGSPEEIKSKATEGLSVVESAIEKAKDVPGKETAVSLLEDGKKLITDAVAAGEAGNVDEVKRLASEGLTKVEEGIAALKG,0.3756393555782181,93.90983889455453,True,True,True,,93.67002727367768,2.5,,0.84527
+248,G248,Generation_Rd2_Manual_24,LM,N/A,MSEEIKKTITDLTEKVSGEPSAENVKAALPALKEGLENEDAGVRYRSVTAVGNIAEKAKDVPEVVEAVKELKPVLEKLSESDPDSTVRLRAKWALGKIEG,0.36140554984456064,90.35138746114016,True,True,True,,89.57056780347637,0.0084,0.37,0.77568
+249,G249,Generation_Rd2_Manual_24,LM,N/A,MEREVLRILKEAGFRVRRAAGSKGYDIVAEKDGKKYLIQVTSGKSPIKPEKVKELVEKAKAENAVPVIVTKTGRVSLPSEIEGVKVLTLEDLKKLLEESS,0.03994438270154638,9.986095675386593,False,False,False,,81.39408555233771,1e-05,0.35,0.73786
+250,G250,Generation_Rd2_Manual_24,LM,N/A,KVEASADKTTLKAGESTKLTVKVTPEGGKKVTITGLTAEGVKIEPSSVELPEVEPGKPVEVTFTVSAENATEGTKEITVTAKAEDGSEGSAKVSVTVEKK,0.2885688997753328,72.1422249438332,True,True,False,,89.35600792244621,0.02,0.33,0.85551
+251,G251,Generation_Rd2_Manual_24,LM,N/A,MANILVIGRVETITSTVKGLLEKEGYTVTTAVTDEEGIAKFKEAGKQDLVLISAGVSDPAEVERLTGEIKKVSSVPVVVHYDGGSKPEDIVADVKAALAK,0.3280431605407768,82.0107901351942,True,True,True,,84.67222933604307,1.2e-07,0.41,0.86648
+252,G252,Generation_Rd2_Manual_24,LM,N/A,MTVNSADDVKAAIEKAKSEGKTELVIGEGVTDEVLEEIAKETTITKVTLPSTVSVAGVEKLAENATGLTSITVPSSLSDEDKQKIKDAVTAKNGGSVTVG,0.2631119641538814,65.77799103847035,True,True,False,,88.24861387282598,0.025,0.27,0.58312
+253,G253,Generation_Rd2_Manual_24,LM,N/A,PDFELSAPPEVTVEKGKSAETTITVTAKDGFSDTVKLEASVSPSSGLTATLEPSEVKPGGSSKLTISTSSSTPVGEYTVTVTGTSGSLTKTKEIKVKVTE,0.339728702528489,84.93217563212225,True,True,True,,87.97385013811632,6.8e-14,0.53,0.86313
+254,G254,Generation_Rd2_Manual_24,LM,N/A,MTVTEKLSSDSVSERREAAEELAETAESDPESVTPEDVPGLIEALDDGSPVVRANAASALGEIGEPAKDAGAVERLTELKENDENSLVRVKAGKALEKIE,0.19252380707578953,48.130951768947384,True,False,False,,88.1057325229771,1.5e-06,0.4,0.7204
+255,G255,Generation_Rd2_Manual_24,LM,N/A,MSYTATVTITLSGVPSDKAGEAANKAKEIAENVAREKGLSASVRVSSISGGSVTVTLTVSAPSKEEAVKTAEEKVKPEIEKKLKDELGVEVTSVSVSEPS,0.2160377889856688,54.0094472464172,True,True,False,,88.04311436876847,0.043,0.25,0.74055
+256,G256,Generation_Rd2_Distant_57,LM,N/A,MPKFRVTVTLEVSAPEGTTPPSPSEIRDKVVEKLREAGASDVSVSTGYVGGKYTITATLTVEGSKEDAEKKAKETAENAVKEAVPGAEVKDVKVTKVEEL,0.3583463973557906,89.58659933894765,True,True,False,,88.59396840807321,1.3,,0.65086
+257,G257,Generation_Rd2_Manual_24,LM,N/A,MVKDGVTGLKEGVAKLKSEADSGGSVDTIKTTATDLKGKWDEIEPKVKEKSPEEYTKVETSIDELVKAAEAKDAATVKTKAGEVSTSLDAVITKLESASK,0.2574744851283073,64.36862128207682,True,False,False,,91.88966367944136,0.0001,0.34,0.92678
+258,G258,Generation_Rd2_Manual_24,LM,N/A,MKVAVEEGLTSVKKALEEAGFEVVSLDEVGEDVDAVVVRAGSAPPERVKEIIEKYKGKPVIVNASGRLAEIAKEYGVTAITGGSPSETVEKIKELLEKLK,0.27630556250521865,69.07639062630466,True,True,False,,86.07982206494033,0.00066,0.33,0.63824
+259,G259,Generation_Rd2_Manual_24,LM,N/A,MEELIKELESSDVEKRIEAVTKLSEIGPPAKDAVPSLVKLLKDEDSGVRAGTATVLGEIAETSPEVVKSAIPDLEKVSKTDENEYVRVAAENALKKITGK,0.36805364210366004,92.013410525915,True,True,True,,93.45533278402938,6e-09,0.47,0.89765
+260,G260,Generation_Rd2_Manual_24,LM,N/A,MKVKVTASGSVSSAKLRVTITPEDGGSPIVKEEEVSLSAGQTVEKEITLSEDDAKKLEGKKAKIKVEALDKDGNVVATGESDPVELKPGETKEVTVTLTK,0.4037847386394065,100.94618465985162,True,True,False,,90.01503428912336,1.8,,0.62789
+261,G261,Generation_Rd2_Distant_57,LM,N/A,MRIEEVVKLPLDEAFRVAKEAAENAGLKDVKVTSSTPTTITATGKSLVGSASVTVTLERVSDSETKVIVEGKPSGGLIGEKKARDAVNKVLEEIKRRAGG,0.14921872002077485,37.30468000519372,True,True,True,,87.86129988150013,0.44,0.22,0.73807
+262,G262,Generation_Rd2_Distant_57,LM,N/A,MAGVEAIKADVEKLKTAKPEEIVPLATSLKEKVDALVKEVEGLTVSAENKAKVTEAVTLIKSGADLVIEGTTSKDSAKISEGVTKVLEGITKILEVVAGK,0.4043522690967771,101.08806727419427,True,True,False,,91.76721563412217,0.048,0.26,0.77123
+263,G263,Generation_Rd2_Distant_57,LM,N/A,MSIKENIVKASESVSTAVTEAGKEPVNVETAKTSLTDAVTVLEGLVPELKADGKDDVATKLEEGVTKAKEGIEKVGSKDEVSAGLTTLSEAITAAGEALK,0.1432357725590353,35.808943139758824,True,False,False,,88.44608194385249,0.33,0.26,0.46556
+264,G264,Generation_Rd2_Distant_57,LM,N/A,MSEVETVKASIQEGIDAANKVSADGGSVAEIKTSLEPVVTKLTEAKGKSSDPKVTTLLDEAITAVNAVKPEEGADVATIKAGLTPVIEKLKEALALVPAA,0.35828339273400983,89.57084818350246,True,True,True,,86.91840993536144,0.33,0.3,0.74084
+265,G265,Generation_Rd2_Distant_57,LM,N/A,MSEIAGKVKEVVTKVEGVLSDSSKTPEEIKAAAGEVSTLVGEAVTAVSGKPGAESVATDLKNAADALKSAAEGGSKDGLTTAKTSLETAITKLTELANSL,0.32759017206237123,81.8975430155928,True,True,True,,92.24300254982056,2.9,,0.71623
+266,G266,Generation_Rd2_Manual_24,LM,N/A,PKIEEVSVSPSELKPGEKTTITAKISDPEGGSGIDTSSIKVEINGEDVTSKAKVEKVDENSATLTVEYTPPEPPLPPGKYTVTIRVKDKAGNEATETKEF,0.35890439604736646,89.72609901184161,True,True,True,,88.44787984007462,2.7e-05,0.41,0.75919
+267,G267,Generation_Rd2_Manual_24,LM,N/A,MTGAKVEPATAKPGDEVKVTATVNAAEEGEYTVSASLSGEGIGGSKSEPVKLEKGENEVTITLTVPEDAEAGKADVTSVSVTDKDGKRVASDSAPSFTVE,0.24711120672212794,61.77780168053199,True,False,False,,89.24986482953167,0.0055,0.38,0.70965
+268,,Fixed_Rd1_48,ground truth,1QYS,DIQVQVNIDDNGKNFDYTYTVTTESELQKVLNELMDYIKKQGAKRVRISITARTKKEAEKFAAILIKVFAELGYNDINVTFDGDTVTVEGQL,0.41197595017913285,102.99398754478321,True,False,False,0.8288429379463196,89.17377945545974,14.0,,
+269,,Fixed_Rd1_48,ground truth,6CZJ,SALAQQLPGTWKMDVTSEDGVRTTGQMHIQPKTPTTMDVTLTGTHADGKPFTGQGKITVKTPTTVDITVTYEDGSTATGQLTVDSPTQFKFDMTASDGTRFTGTVQRQS,0.39017888440813564,97.5447211020339,True,True,True,1.0148541927337646,91.31992370254028,7.1,,
+270,,Fixed_Rd1_48,ground truth,6W3W,DEDREWIERFNRILIESLTTGDEHTLKELIDPNARLVINGRDIHGREEFVRLLSEMGVKHFHVHDVKVVGNKAVTRGILYFNGREYDVDVFTRKIDGRWLYESLEVK,0.04445695864286616,11.11423966071654,False,False,False,1.4773865938186646,85.96058814924257,8.2,,
+271,,Fixed_Rd1_48,ground truth,6WVS,DILIVNATDVDEMLKQVEILRRLGAKQIAVVSDDWRILQEALKKGGDILIVNATDVDEMLKQVEILRRLGAKQIAVVSDDWRILQEALKKGGDILIVNATDVDEMLKQVEILRRLGAKQIAVVSDDWRILQEALKKGGDILIVNATDVDEMLKQVEILRRLGAKQIAVVSDDWRILQEALKK,0.28575451397545437,71.43862849386359,True,True,True,0.8460836410522461,90.9020930370598,,,
+272,,Fixed_Rd2_compare_LM_24,ground truth,5L33,PEEEKAARLFIEALEKGDPELMRKVISPDTRMEDNGREFTGDEVVEYVKEIQKRGEQWHLRRYTKEGNSWRFEVQVDNNGQTEQWEVQIEVRNGRIKRVTITHV,0.4303842742590548,107.5960685647637,True,True,True,,92.43271477764092,4.8,,
+273,,Fixed_Rd2_compare_LM_24,ground truth,6D0T,MVDAAQYFPGTWEFRFRSSDGKEYRGTVEMQPRTPTEIEIRFKGQSSDGRPVEGRGSIEVRSPYEYRFEMQSSDGARWEGTLQVRSPDSVEVRFKSSDGREYSGEFRRQEG,0.401660220780138,100.41505519503451,True,False,False,,89.34978999176117,13.0,,
+274,,Fixed_Rd2_compare_LM_24,ground truth,6MRS,GSGRQEKVLKSIEETVRKMGVTMETHRSGNEVKVVIKGLHESQQEQLKKDVEETSKKQGVETRIEFHGDTVTIVVRE,0.19924157322399905,49.81039330599976,True,False,False,,92.79344239462696,43.0,,
+275,,Fixed_Rd2_compare_LM_24,ground truth,6NUK,GSTVRIEIRFTNMRREEVQKELEKFKERLKELEKRTGSEIRIEIEERDGEVRVEVEIRNSHEEEVRQIIEEIERWVRKMGGELRVEK,0.4075636222846284,101.89090557115709,True,True,True,,89.86144181933138,4.7,,0.38491

weight/esm-main/examples/lm-design/utils/constants.py

@@ -0,0 +1,13 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+from typing import Optional, NamedTuple
+from enum import Enum
+
+
+# Note ordering is important, correlates with Designer.py::coords[:, angle-index]
+COORDS_ANGLE_NAMES = ['omega', 'theta', 'phi']
+COORDS4D_NAMES = ['dist'] + COORDS_ANGLE_NAMES
+COORDS6D_NAMES = COORDS4D_NAMES + ['torsion_phi', 'torsion_psi']

weight/esm-main/examples/lm-design/utils/linear_projection.py

@@ -0,0 +1,138 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+#
+import logging
+from collections import defaultdict
+from functools import partial
+from pathlib import Path
+import torch
+import torch.nn.functional as F
+import torch.nn as nn
+from collections import namedtuple
+
+import os
+import sys
+import torch
+from pathlib import Path
+from argparse import Namespace
+from omegaconf import DictConfig, OmegaConf
+from typing import Dict, Optional, List, Callable, Union
+import math
+
+BinningScheme = namedtuple(
+    'BinningScheme',
+    [
+        'N_BINS',
+        'CUTOFF_BIN',
+        'MIN_DIST',
+        'MAX_DIST',
+        'CONTACT_DIST',
+        'THETA_BINS',
+        'PHI_BINS',
+        'OMEGA_BINS',
+        'TORSION_BINS',
+    ],
+)
+LinearProjectionDist_1A = BinningScheme(
+    N_BINS=18,
+    CUTOFF_BIN=5,  # conservative cutoff for 8 angstroms exact ((20-2.5)/16*5 + 2.5 = ~7.9)
+    MIN_DIST=2.5,
+    MAX_DIST=20,
+    CONTACT_DIST=8,
+    THETA_BINS=18,
+    PHI_BINS=8,
+    OMEGA_BINS=18,
+    TORSION_BINS=50,
+)
+BIN_FLAG_TO_ENUM = {
+    'LinearProjectionDist_1A': LinearProjectionDist_1A,
+}
+
+logger = logging.getLogger(__name__)
+
+
+def extract_features(
+    model,
+    inp: torch.Tensor,
+    has_cls: bool = True,
+    has_eos: bool = True,
+    need_head_weights: Union[bool, Dict] = False,
+):
+    """
+    Inflexible way of dealing with the mess of inputs with cls tokens,
+    need embedding without, and LSTM doesnt want cls token in the first place.
+    """
+    
+    out_start_idx = 1 if has_cls else 0
+    out_end_idx = -1 if has_eos else None
+    inp = inp.argmax(dim=-1) # output [B, T]
+    result = model(inp, need_head_weights=need_head_weights)
+    attentions = result["attentions"]
+
+    batch, layer, head, seqlen, seqlen2 = attentions.size()
+    assert seqlen == seqlen2
+    attentions = attentions.reshape(
+        batch, layer * head, seqlen, seqlen
+    )
+    emb = result["logits"]
+
+    return emb[:, out_start_idx:out_end_idx], \
+        attentions[:, :, out_start_idx:out_end_idx, out_start_idx:out_end_idx]
+
+    
+class LinearProjectionDistogramModel(nn.Module):
+    """
+    This model regresses angles and distances from the attention maps of the LM
+    """
+
+    def __init__(self):
+        super().__init__()
+        self.base_model = None  # To be initialized later
+        self.num_heads = 20 * 33
+        self.bin_enum = BIN_FLAG_TO_ENUM['LinearProjectionDist_1A']
+        self._s1 = self.bin_enum.N_BINS
+        self._s2 = self._s1 + self.bin_enum.THETA_BINS
+        self._s3 = self._s2 + self.bin_enum.PHI_BINS
+        self._s4 = self._s3 + self.bin_enum.OMEGA_BINS
+        
+        conv_in_channels = self.num_heads
+
+        self.conv1 = torch.nn.Conv2d(
+            in_channels=conv_in_channels,
+            out_channels=self.bin_enum.N_BINS + self.bin_enum.OMEGA_BINS,
+            kernel_size=1,
+            stride=1,
+            padding=0,
+        )
+
+        self.conv2 = torch.nn.Conv2d(
+            in_channels=conv_in_channels,
+            out_channels=self.bin_enum.THETA_BINS + self.bin_enum.PHI_BINS,
+            kernel_size=1,
+            stride=1,
+            padding=0,
+        )
+
+
+    def forward(self, src_tokens, **kwargs):
+        _, attentions_asym = extract_features(self.base_model, src_tokens, need_head_weights=True)
+            
+        def symmetrize(contacts, scale=1.0):
+            return scale * (contacts + contacts.transpose(-1, -2))
+             
+        attentions_sym = symmetrize(attentions_asym)
+        
+        # [B, C, N, N] -> [B, output_channels, N, N]
+        out1 = self.conv1(attentions_sym)
+        out2 = self.conv2(attentions_asym)
+        return {
+            'logits':       out1[:, :self.bin_enum.N_BINS:,    :, :],
+            'omega_logits': out1[:, self.bin_enum.N_BINS:,     :, :],
+            'theta_logits': out2[:, :self.bin_enum.THETA_BINS, :, :],
+            'phi_logits':   out2[:, self.bin_enum.THETA_BINS:, :, :],
+        }
+
+
+

weight/esm-main/examples/lm-design/utils/lm.py

@@ -0,0 +1,102 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import logging
+import math
+logger = logging.getLogger(__name__)
+from typing import Optional, Dict, List, Any, Union
+
+import torch
+from torch import nn
+import torch.nn.functional as F
+from abc import ABC, abstractmethod
+
+from .tensor import is_1hot_tensor, add_eos_bos
+from .masking import apply_mask, assert_valid_mask
+
+
+def lm_marginal(mlm, x, mask):
+    """ 
+    Utility to extract logprobs from an MLM, given an input tensor and a mask.
+    NOTE:
+     - **Mask each set bit in mask's L-dim separately.** - (as opposed to all at once.)
+     - Therefore, performs B*n forward passes.
+     - In the future, we might try just using a single forward pass per sequence = B passes.
+        - We had some success with something akin to this in the ESM-1V paper.
+    Args:
+        x: [B, L, K]
+        mask: [B, L, 1] # n masks in L dim
+            if None, use a mask of all ones.
+    Returns:
+        logits: [B, n, K]
+    """
+    B, L, K = x.shape
+    if mask is None:
+        mask = torch.ones(B, L, 1, dtype=torch.bool, device=x.device)
+    n = assert_valid_mask(mask, x=x) # this is gross.
+
+    # Get coords of set bits. [B*n]
+    b_coords, l_coords, _ = mask.nonzero(as_tuple=True) # [B*n], [B*n]
+    # Double-check of mask assumptions.
+    assert torch.equal(b_coords, torch.repeat_interleave(torch.arange(B).to(x.device), n).to(x.device))
+
+    # naming: _m1 = mask1.  (B*n leading dim.)
+    x_m1 = x[b_coords]  # [B*n, L, K]
+    mask_m1 = F.one_hot(l_coords, L).unsqueeze(-1).bool() # [B*n, L, 1]
+
+    # Apply mask, mlm forward, select logits.
+    x_masked_m1 = apply_mask(x_m1, mask_m1, mlm.vocab.mask_idx) # [B*n, L, K]
+    lm_logits_m1 = mlm(x_masked_m1)['logits']
+    lm_logits_select = lm_logits_m1.masked_select(mask_m1).reshape(B, n, K) # [B, n, K]
+
+    # Mlm outputs 'logits' = not logprobabilities, but pre-softmax values.
+    # We must log-softmax here to convert from pre-softmax logits -> logprobs.
+    lm_logprobs_select = torch.log_softmax(lm_logits_select, axis=-1)
+
+    return lm_logprobs_select # [B, n, K]
+
+
+class WrapLM(nn.Module, ABC):
+    def __init__(self, LM, vocab):
+        super().__init__()
+        self.model = LM
+        self.vocab = vocab
+
+    @abstractmethod
+    def forward(self, seq1h, **kwargs):
+        raise NotImplementedError
+
+
+class WrapLmEsm(WrapLM):
+    def forward(self, seq1h):
+        B, L, K = seq1h.shape
+        seq1h, seq_start_idx, seq_end_idx = self._prepare_seq(seq1h)
+        seq = seq1h.argmax(-1)
+        
+        out = self.model(seq)
+
+        return {
+            'logits': out['logits'][:, seq_start_idx:seq_end_idx, :K],
+        }
+    
+    def _prepare_seq(self, seq1h):
+        assert is_1hot_tensor(seq1h)
+        B, L, K = seq1h.shape
+        # Prepend bos/cls and append eos.
+        seq1h = add_eos_bos(seq1h, bos_idx=self.vocab.cls_idx, eos_idx=self.vocab.eos_idx)
+        seq_start_idx = 1
+        seq_end_idx = L + 1
+
+        # In some cases, a vocab padded to 64 positions
+        # with dummy character was used.
+        # As a workaround, pad K dimension, then remove this portion after
+        embed_dim = self.model.embed_tokens.weight.size(0)
+        if embed_dim != K:
+            seq1h = torch.cat([
+                seq1h,
+                torch.zeros(B, L+2, embed_dim - K).to(seq1h)
+            ], -1)
+
+        return seq1h, seq_start_idx, seq_end_idx

weight/esm-main/examples/lm-design/utils/loss.py

@@ -0,0 +1,29 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+import torch.nn.functional as F
+
+
+def get_cce_loss(probs, labels, mask=None, eps=1e-8,):
+    """
+    Calculates the categorical cross entropy and averages result.
+    Using optional mask to control which labels are included in the loss.
+    Args:
+      probs (torch.float32): [B, L, L, num_categories]
+      labels (torch.int32): [B, L, L]
+      mask (torch.float32): [B, L, L]
+    Returns:
+      average_cce (torch.float32): [B]
+    """
+    if mask is None:
+        B, L = probs.shape[:2]
+        mask = torch.ones(B, L, L).to(probs.device)
+
+    num_categories = probs.shape[-1]
+    labels_onehot = F.one_hot(labels, num_categories)
+    cce_ij = -torch.sum(labels_onehot * torch.log(probs+eps), axis=-1)
+    average_cce = torch.sum(mask * cce_ij, axis=(1, 2)) / torch.sum(mask, axis=(1, 2))
+    return average_cce

weight/esm-main/examples/protein-programming-language/language/__init__.py

@@ -0,0 +1,22 @@
+from language.energy import (
+    MaximizeGlobularity,
+    MaximizePLDDT,
+    MaximizePTM,
+    MaximizeSurfaceExposure,
+    MinimizeCRmsd,
+    MinimizeDRmsd,
+    MatchSecondaryStructure,
+    MinimizeSurfaceExposure,
+    MinimizeSurfaceHydrophobics,
+    SymmetryRing,
+)
+from language.folding_callbacks import EsmFoldv1, FoldingCallback, FoldingResult
+from language.optimize import run_simulated_annealing
+from language.program import ProgramNode
+from language.sequence import (
+    ConstantSequenceSegment,
+    FixedLengthSequenceSegment,
+    VariableLengthSequenceSegment,
+    sequence_from_atomarray,
+)
+from language.utilities import get_atomarray_in_residue_range, pdb_file_to_atomarray

weight/esm-main/examples/protein-programming-language/language/energy.py

@@ -0,0 +1,317 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+from abc import ABC, abstractmethod
+from typing import List, Optional
+
+import numpy as np
+from biotite.structure import annotate_sse, AtomArray, rmsd, sasa, superimpose
+
+from language.folding_callbacks import FoldingResult
+from language.utilities import get_atomarray_in_residue_range
+
+
+class EnergyTerm(ABC):
+    def __init__(self) -> None:
+        pass
+
+    @abstractmethod
+    def compute(self, node, folding_result: FoldingResult) -> float:
+        pass
+
+
+class MaximizePTM(EnergyTerm):
+    def __init__(self) -> None:
+        super().__init__()
+
+    def compute(self, node, folding_result: FoldingResult) -> float:
+        del node
+        return 1.0 - folding_result.ptm
+
+
+class MaximizePLDDT(EnergyTerm):
+    def __init__(self) -> None:
+        super().__init__()
+
+    def compute(self, node, folding_result: FoldingResult) -> float:
+        del node
+        return 1.0 - folding_result.plddt
+
+
+class SymmetryRing(EnergyTerm):
+    def __init__(self, all_to_all_protomer_symmetry: bool = False) -> None:
+        super().__init__()
+        self.all_to_all_protomer_symmetry: bool = all_to_all_protomer_symmetry
+
+    def compute(self, node, folding_result: FoldingResult) -> float:
+        protomer_nodes = node.get_children()
+        protomer_residue_ranges = [
+            protomer_node.get_residue_index_range() for protomer_node in protomer_nodes
+        ]
+
+        centers_of_mass = []
+        for start, end in protomer_residue_ranges:
+            backbone_coordinates = get_backbone_atoms(
+                folding_result.atoms[
+                    np.logical_and(
+                        folding_result.atoms.res_id >= start,
+                        folding_result.atoms.res_id < end,
+                    )
+                ]
+            ).coord
+            centers_of_mass.append(get_center_of_mass(backbone_coordinates))
+        centers_of_mass = np.vstack(centers_of_mass)
+
+        return (
+            float(np.std(pairwise_distances(centers_of_mass)))
+            if self.all_to_all_protomer_symmetry
+            else float(np.std(adjacent_distances(centers_of_mass)))
+        )
+
+
+def get_backbone_atoms(atoms: AtomArray) -> AtomArray:
+    return atoms[
+        (atoms.atom_name == "CA") | (atoms.atom_name == "N") | (atoms.atom_name == "C")
+    ]
+
+
+def _is_Nx3(array: np.ndarray) -> bool:
+    return len(array.shape) == 2 and array.shape[1] == 3
+
+
+def get_center_of_mass(coordinates: np.ndarray) -> np.ndarray:
+    assert _is_Nx3(coordinates), "Coordinates must be Nx3."
+    return coordinates.mean(axis=0).reshape(1, 3)
+
+
+def pairwise_distances(coordinates: np.ndarray) -> np.ndarray:
+    assert _is_Nx3(coordinates), "Coordinates must be Nx3."
+    m = coordinates[:, np.newaxis, :] - coordinates[np.newaxis, :, :]
+    distance_matrix = np.linalg.norm(m, axis=-1)
+    return distance_matrix[np.triu_indices(distance_matrix.shape[0], k=1)]
+
+
+def adjacent_distances(coordinates: np.ndarray) -> np.ndarray:
+    assert _is_Nx3(coordinates), "Coordinates must be Nx3."
+    m = coordinates - np.roll(coordinates, shift=1, axis=0)
+    return np.linalg.norm(m, axis=-1)
+
+
+class MinimizeSurfaceHydrophobics(EnergyTerm):
+    def __init__(self) -> None:
+        super().__init__()
+
+    def compute(self, node, folding_result: FoldingResult) -> float:
+        start, end = node.get_residue_index_range()
+
+        return hydrophobic_score(folding_result.atoms, start, end)
+
+
+_HYDROPHOBICS = {"VAL", "ILE", "LEU", "PHE", "MET", "TRP"}
+
+
+def hydrophobic_score(
+    atom_array: AtomArray,
+    start_residue_index: Optional[int] = None,
+    end_residue_index: Optional[int] = None,
+) -> float:
+    """
+    Computes ratio of hydrophobic atoms in a biotite AtomArray that are also surface
+    exposed. Typically, lower is better.
+    """
+
+    hydrophobic_mask = np.array([aa in _HYDROPHOBICS for aa in atom_array.res_name])
+
+    if start_residue_index is None and end_residue_index is None:
+        selection_mask = np.ones_like(hydrophobic_mask)
+    else:
+        start_residue_index = 0 if start_residue_index is None else start_residue_index
+        end_residue_index = (
+            len(hydrophobic_mask) if end_residue_index is None else end_residue_index
+        )
+        selection_mask = np.array(
+            [
+                i >= start_residue_index and i < end_residue_index
+                for i in range(len(hydrophobic_mask))
+            ]
+        )
+
+    # TODO(scandido): Resolve the float/bool thing going on here.
+    hydrophobic_surf = np.logical_and(
+        selection_mask * hydrophobic_mask, sasa(atom_array)
+    )
+    # TODO(brianhie): Figure out how to handle divide-by-zero.
+    return sum(hydrophobic_surf) / sum(selection_mask * hydrophobic_mask)
+
+
+class MinimizeSurfaceExposure(EnergyTerm):
+    def __init__(self) -> None:
+        super().__init__()
+
+    def compute(self, node, folding_result: FoldingResult) -> float:
+        start, end = node.get_residue_index_range()
+
+        return surface_ratio(folding_result.atoms, list(range(start, end)))
+
+
+class MaximizeSurfaceExposure(EnergyTerm):
+    def __init__(self) -> None:
+        super().__init__()
+
+    def compute(self, node, folding_result: FoldingResult) -> float:
+        start, end = node.get_residue_index_range()
+
+        return 1.0 - surface_ratio(folding_result.atoms, list(range(start, end)))
+
+
+def surface_ratio(atom_array: AtomArray, residue_indices: List[int]) -> float:
+    """Computes ratio of atoms in specified ratios which are on the protein surface."""
+
+    residue_mask = np.array([res_id in residue_indices for res_id in atom_array.res_id])
+    surface = np.logical_and(residue_mask, sasa(atom_array))
+    return sum(surface) / sum(residue_mask)
+
+
+class MinimizeSurfaceExposure(EnergyTerm):
+    def __init__(self) -> None:
+        super().__init__()
+
+    def compute(self, node, folding_result: FoldingResult) -> float:
+        start, end = node.get_residue_index_range()
+
+        return surface_ratio(folding_result.atoms, list(range(start, end)))
+
+
+class MaximizeSurfaceExposure(EnergyTerm):
+    def __init__(self) -> None:
+        super().__init__()
+
+    def compute(self, node, folding_result: FoldingResult) -> float:
+        start, end = node.get_residue_index_range()
+
+        return 1.0 - surface_ratio(folding_result.atoms, list(range(start, end)))
+
+
+def surface_ratio(atom_array: AtomArray, residue_indices: List[int]) -> float:
+    """Computes ratio of atoms in specified ratios which are on the protein surface."""
+
+    residue_mask = np.array([res_id in residue_indices for res_id in atom_array.res_id])
+    surface = np.logical_and(residue_mask, sasa(atom_array))
+    return sum(surface) / sum(residue_mask)
+
+
+class MaximizeGlobularity(EnergyTerm):
+    def __init__(self) -> None:
+        super().__init__()
+
+    def compute(self, node, folding_result: FoldingResult) -> float:
+        start, end = node.get_residue_index_range()
+
+        backbone = get_backbone_atoms(
+            folding_result.atoms[
+                np.logical_and(
+                    folding_result.atoms.res_id >= start,
+                    folding_result.atoms.res_id < end,
+                )
+            ]
+        ).coord
+
+        return float(np.std(distances_to_centroid(backbone)))
+
+
+def distances_to_centroid(coordinates: np.ndarray) -> np.ndarray:
+    """
+    Computes the distances from each of the coordinates to the
+    centroid of all coordinates.
+    """
+    assert _is_Nx3(coordinates), "Coordinates must be Nx3."
+    center_of_mass = get_center_of_mass(coordinates)
+    m = coordinates - center_of_mass
+    return np.linalg.norm(m, axis=-1)
+
+
+class MinimizeCRmsd(EnergyTerm):
+    def __init__(self, template: AtomArray, backbone_only: bool = False) -> None:
+        super().__init__()
+
+        self.template: AtomArray = template
+        self.backbone_only: bool = backbone_only
+        if self.backbone_only:
+            self.template = get_backbone_atoms(template)
+
+    def compute(self, node, folding_result: FoldingResult) -> float:
+        start, end = node.get_residue_index_range()
+
+        atoms = get_atomarray_in_residue_range(folding_result.atoms, start, end)
+
+        if self.backbone_only:
+            atoms = get_backbone_atoms(atoms)
+
+        return crmsd(self.template, atoms)
+
+
+def crmsd(atom_array_a: AtomArray, atom_array_b: AtomArray) -> float:
+    # TODO(scandido): Add this back.
+    # atom_array_a = canonicalize_within_residue_atom_order(atom_array_a)
+    # atom_array_b = canonicalize_within_residue_atom_order(atom_array_b)
+    superimposed_atom_array_b_onto_a, _ = superimpose(atom_array_a, atom_array_b)
+    return float(rmsd(atom_array_a, superimposed_atom_array_b_onto_a).mean())
+
+
+class MinimizeDRmsd(EnergyTerm):
+    def __init__(self, template: AtomArray, backbone_only: bool = False) -> None:
+        super().__init__()
+
+        self.template: AtomArray = template
+        self.backbone_only: bool = backbone_only
+        if self.backbone_only:
+            self.template = get_backbone_atoms(template)
+
+    def compute(self, node, folding_result: FoldingResult) -> float:
+        start, end = node.get_residue_index_range()
+
+        atoms = get_atomarray_in_residue_range(folding_result.atoms, start, end)
+
+        if self.backbone_only:
+            atoms = get_backbone_atoms(atoms)
+
+        return drmsd(self.template, atoms)
+
+
+def drmsd(atom_array_a: AtomArray, atom_array_b: AtomArray) -> float:
+    # TODO(scandido): Add this back.
+    # atom_array_a = canonicalize_within_residue_atom_order(atom_array_a)
+    # atom_array_b = canonicalize_within_residue_atom_order(atom_array_b)
+
+    dp = pairwise_distances(atom_array_a.coord)
+    dq = pairwise_distances(atom_array_b.coord)
+
+    return float(np.sqrt(((dp - dq) ** 2).mean()))
+
+
+def pairwise_distances(coordinates: np.ndarray) -> np.ndarray:
+    assert _is_Nx3(coordinates), "Coordinates must be Nx3."
+    m = coordinates[:, np.newaxis, :] - coordinates[np.newaxis, :, :]
+    distance_matrix = np.linalg.norm(m, axis=-1)
+    return distance_matrix[np.triu_indices(distance_matrix.shape[0], k=1)]
+
+
+class MatchSecondaryStructure(EnergyTerm):
+    def __init__(self, secondary_structure_element: str) -> None:
+        super().__init__()
+        self.secondary_structure_element = secondary_structure_element
+
+    def compute(self, node, folding_result: FoldingResult) -> float:
+        start, end = node.get_residue_index_range()
+
+        subprotein = folding_result.atoms[
+            np.logical_and(
+                folding_result.atoms.res_id >= start,
+                folding_result.atoms.res_id < end,
+            )
+        ]
+        sse = annotate_sse(subprotein)
+
+        return np.mean(sse != self.secondary_structure_element)

weight/esm-main/examples/protein-programming-language/language/folding_callbacks.py

@@ -0,0 +1,78 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+from abc import ABC, abstractmethod
+from dataclasses import dataclass
+from io import StringIO
+from typing import List
+
+import esm
+import torch
+from biotite.structure import AtomArray
+import numpy as np
+from openfold.np.residue_constants import atom_order
+from torch.utils._pytree import tree_map
+
+from language.utilities import pdb_file_to_atomarray
+
+
+@dataclass
+class FoldingResult:
+    atoms: AtomArray
+    ptm: float
+    plddt: float
+
+
+class FoldingCallback(ABC):
+    "Interface for running ESMFold and other folding methods."
+
+    def __init__(self) -> None:
+        pass
+
+    @abstractmethod
+    def load(self, device: str) -> None:
+        pass
+
+    @abstractmethod
+    def fold(self, sequence: str, residue_indices: List[int]) -> FoldingResult:
+        pass
+
+
+class EsmFoldv1(FoldingCallback):
+    "Runs ESMFold v1.0."
+
+    def __init__(self) -> None:
+        super().__init__()
+
+        self.model = None
+
+    def load(self, device: str) -> None:
+        self.model = esm.pretrained.esmfold_v1().eval()
+        self.model = self.model.to(device)
+
+    def fold(self, sequence: str, residue_indices: List[int]) -> FoldingResult:
+        assert self.model is not None, "Must call load() before fold()."
+
+        # TODO: Current `esm.esmfold.v1.misc.output_to_pdb()` adds 1 to the `residx`
+        # mistakenly, just subtract 1 for now but fix in a later version.
+        residue_indices = np.array(residue_indices) - 1
+
+        raw_output = self.model.infer(
+            sequence, residx=torch.Tensor(residue_indices).long().reshape(1, -1),
+        )
+        raw_output = tree_map(lambda x: x.to("cpu"), raw_output)
+
+        pdb_string = esm.esmfold.v1.misc.output_to_pdb(raw_output)[0]
+        atoms: AtomArray = pdb_file_to_atomarray(StringIO(pdb_string))
+
+        plddt = raw_output["plddt"]
+        plddt = plddt[0, ...].numpy()
+        plddt = plddt.transpose()
+        plddt = plddt[atom_order["CA"], :]
+        plddt = float(plddt.mean()) / 100.0
+
+        ptm = float(raw_output["ptm"])
+
+        return FoldingResult(atoms=atoms, ptm=ptm, plddt=plddt)

weight/esm-main/examples/protein-programming-language/language/optimize.py

@@ -0,0 +1,158 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+from copy import deepcopy
+from dataclasses import dataclass
+
+import numpy as np
+from rich.live import Live
+from rich.table import Table
+
+from language.folding_callbacks import FoldingCallback
+from language.program import ProgramNode
+
+
+@dataclass
+class MetropolisHastingsState:
+    program: ProgramNode
+    temperature: float
+    annealing_rate: float
+    num_steps: int
+    candidate_energy: float
+    candidate_energy_term_fn_values: list
+    current_energy: float
+    current_energy_term_fn_values: list
+    best_energy: float
+    best_energy_term_fn_values: list
+
+
+def metropolis_hastings_step(
+    state: MetropolisHastingsState,
+    folding_callback: FoldingCallback,
+    verbose: bool = False,
+) -> MetropolisHastingsState:
+    temperature = state.temperature * state.annealing_rate
+
+    candidate: ProgramNode = deepcopy(state.program)
+    candidate.mutate()
+
+    sequence, residue_indices = candidate.get_sequence_and_set_residue_index_ranges()
+    folding_output = folding_callback.fold(sequence, residue_indices)
+
+    energy_term_fns = candidate.get_energy_term_functions()
+    candidate_energy_term_fn_values = [
+        (name, weight, energy_fn(folding_output)) for name, weight, energy_fn in energy_term_fns
+    ]
+    # TODO(scandido): Log these.
+    candidate_energy: float = sum(
+        [weight * value for _, weight, value in candidate_energy_term_fn_values]
+    )
+
+    accept_candidate = False
+    if state.current_energy is None:
+        accept_candidate = True
+    else:
+        # NOTE(scandido): We are minimizing the function here so instead of
+        # candidate - current we do -1 * (candidate - current) = -candidate + current.
+        energy_differential: float = -candidate_energy + state.current_energy
+        accept_probability: float = np.clip(
+            # NOTE(scandido): We approximate the ratio of transition probabilities from
+            # current to candidate vs. candidate to current to be equal, which is
+            # approximately correct.
+            np.exp(energy_differential / temperature),
+            a_min=None,
+            a_max=1.0,
+        )
+        accept_candidate: bool = np.random.uniform() < accept_probability
+
+    if accept_candidate and verbose:
+        print(f"Accepted {sequence} with energy {candidate_energy:.2f}.")
+
+    best = (state.best_energy is None) or candidate_energy < state.best_energy
+
+    return MetropolisHastingsState(
+        program=candidate if accept_candidate else state.program,
+        temperature=temperature,
+        annealing_rate=state.annealing_rate,
+        num_steps=state.num_steps + 1,
+        candidate_energy=candidate_energy,
+        candidate_energy_term_fn_values=candidate_energy_term_fn_values,
+        current_energy=candidate_energy if accept_candidate else state.current_energy,
+        current_energy_term_fn_values=candidate_energy_term_fn_values
+        if accept_candidate
+        else state.current_energy_term_fn_values,
+        best_energy=candidate_energy if best else state.best_energy,
+        best_energy_term_fn_values=candidate_energy_term_fn_values
+        if best
+        else state.best_energy_term_fn_values,
+    )
+
+
+def run_simulated_annealing(
+    program: ProgramNode,
+    initial_temperature: float,
+    annealing_rate: float,
+    total_num_steps: int,
+    folding_callback: FoldingCallback,
+    display_progress: bool = True,
+    progress_verbose_print: bool = False,
+) -> ProgramNode:
+    # TODO(scandido): Track accept rate.
+
+    state = MetropolisHastingsState(
+        program=program,
+        temperature=initial_temperature,
+        annealing_rate=annealing_rate,
+        num_steps=0,
+        candidate_energy=None,
+        candidate_energy_term_fn_values=None,
+        current_energy=None,
+        current_energy_term_fn_values=None,
+        best_energy=None,
+        best_energy_term_fn_values=None,
+    )
+
+    def _generate_table(state):
+        table = Table()
+        table.add_column("Energy name")
+        table.add_column("Weight")
+        table.add_column("Candidate Value")
+        table.add_column("Current Value")
+        table.add_column("Best Value")
+        if state.current_energy_term_fn_values is None:
+            return table
+        for (name, weight, candidate_value), (_, _, current_value), (_, _, best_value) in zip(
+            state.candidate_energy_term_fn_values,
+            state.current_energy_term_fn_values,
+            state.best_energy_term_fn_values,
+        ):
+            table.add_row(
+                name,
+                f"{weight:.2f}",
+                f"{candidate_value:.2f}",
+                f"{current_value:.2f}",
+                f"{best_value:.2f}",
+            )
+        table.add_row(
+            "Energy",
+            "",
+            f"{state.candidate_energy:.2f}",
+            f"{state.current_energy:.2f}",
+            f"{state.best_energy:.2f}",
+        )
+        table.add_row("Iterations", "", f"{state.num_steps} / {total_num_steps}")
+        return table
+
+    with Live() as live:
+        for _ in range(1, total_num_steps + 1):
+            state = metropolis_hastings_step(
+                state,
+                folding_callback,
+                verbose=progress_verbose_print,
+            )
+            if display_progress:
+                live.update(_generate_table(state))
+
+    return state.program

weight/esm-main/examples/protein-programming-language/language/program.py

@@ -0,0 +1,127 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+from functools import partial
+from typing import Callable, List, Optional, Tuple
+
+import numpy as np
+
+from language.energy import EnergyTerm
+from language.folding_callbacks import FoldingResult
+from language.sequence import SequenceSegmentFactory
+
+MULTIMER_RESIDUE_INDEX_SKIP_LENGTH: int = 1000
+
+
+class ProgramNode:
+    def __init__(
+        self,
+        children: List["ProgramNode"] = None,
+        sequence_segment: SequenceSegmentFactory = None,
+        children_are_different_chains: bool = False,
+        energy_function_terms: List[EnergyTerm] = [],
+        energy_function_weights: Optional[List[float]] = None,
+    ) -> None:
+        self.children: Optional[List["ProgramNode"]] = children
+        self.sequence_segment: SequenceSegmentFactory = sequence_segment
+        self.children_are_different_chains: bool = children_are_different_chains
+        self.energy_function_terms: List[energy_function_terms] = energy_function_terms
+        self.energy_function_weights: List[
+            float
+        ] = energy_function_weights if energy_function_weights else [
+            1.0 for _ in self.energy_function_terms
+        ]
+        if self.energy_function_weights:
+            assert len(self.energy_function_terms) == len(
+                self.energy_function_weights
+            ), "One must have the same number of energy function terms and weights on a node."
+
+        self.residue_index_range: Optional[Tuple[int, int]] = None
+
+    def get_sequence_and_set_residue_index_ranges(
+        self, residue_index_offset: int = 1
+    ) -> Tuple[str, List[int]]:
+        if self.is_leaf_node():
+            sequence = self.sequence_segment.get()
+            self.residue_index_range = (
+                residue_index_offset,
+                residue_index_offset + len(sequence),
+            )
+            return sequence, list(range(*self.residue_index_range))
+
+        offset: int = residue_index_offset
+        sequence = ""
+        residue_indices = []
+        for child in self.children:
+            (
+                sequence_segment,
+                residue_indices_segment,
+            ) = child.get_sequence_and_set_residue_index_ranges(
+                residue_index_offset=offset
+            )
+            sequence += sequence_segment
+            residue_indices += residue_indices_segment
+            offset = residue_indices[-1] + 1
+            if self.children_are_different_chains:
+                offset += MULTIMER_RESIDUE_INDEX_SKIP_LENGTH
+        self.residue_index_range = (residue_indices[0], residue_indices[-1] + 1)
+        return sequence, residue_indices
+
+    def get_residue_index_range(self) -> Tuple[int, int]:
+        assert (
+            self.residue_index_range
+        ), "Must call get_sequence_and_set_residue_index_ranges() first."
+        return self.residue_index_range
+
+    def get_children(self) -> List["ProgramNode"]:
+        return self.children
+
+    def is_leaf_node(self) -> bool:
+        return self.children is None
+
+    def get_energy_term_functions(
+        self, name_prefix: str = ""
+    ) -> List[Tuple[str, float, Callable[[FoldingResult], float]]]:
+        name_prefix = name_prefix if name_prefix else "root"
+
+        terms = [
+            (
+                f"{name_prefix}:{type(term).__name__}",
+                weight,
+                partial(term.compute, self),
+            )
+            for weight, term in zip(
+                self.energy_function_weights, self.energy_function_terms
+            )
+        ]
+
+        if self.is_leaf_node():
+            return terms
+
+        for i, child in enumerate(self.children):
+            terms += child.get_energy_term_functions(
+                name_prefix=name_prefix + f".n{i+1}"
+            )
+
+        return terms
+
+    def mutate(self) -> None:
+        if self.is_leaf_node():
+            return self.sequence_segment.mutate()
+
+        weights = np.array(
+            [float(child.num_mutation_candidates()) for child in self.children]
+        )
+        assert (
+            weights.sum() > 0
+        ), "Some mutations should be possible if mutate() was called."
+        child_to_mutate = np.random.choice(self.children, p=weights / weights.sum())
+        child_to_mutate.mutate()
+
+    def num_mutation_candidates(self) -> int:
+        if self.is_leaf_node():
+            return self.sequence_segment.num_mutation_candidates()
+
+        return sum([child.num_mutation_candidates() for child in self.children])

weight/esm-main/examples/protein-programming-language/language/sequence.py

@@ -0,0 +1,221 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+from abc import ABC, abstractmethod
+from copy import deepcopy
+from typing import List, Union
+
+import numpy as np
+from biotite.structure import AtomArray
+
+ALL_RESIDUE_TYPES = [
+    "A",
+    "R",
+    "N",
+    "D",
+    "C",
+    "Q",
+    "E",
+    "G",
+    "H",
+    "I",
+    "L",
+    "K",
+    "M",
+    "F",
+    "P",
+    "S",
+    "T",
+    "W",
+    "Y",
+    "V",
+]
+
+RESIDUE_TYPES_WITHOUT_CYSTEINE = deepcopy(ALL_RESIDUE_TYPES)
+RESIDUE_TYPES_WITHOUT_CYSTEINE.remove("C")
+
+RESIDUE_TYPES_1to3 = {
+    "A": "ALA",
+    "R": "ARG",
+    "N": "ASN",
+    "D": "ASP",
+    "C": "CYS",
+    "Q": "GLN",
+    "E": "GLU",
+    "G": "GLY",
+    "H": "HIS",
+    "I": "ILE",
+    "L": "LEU",
+    "K": "LYS",
+    "M": "MET",
+    "F": "PHE",
+    "P": "PRO",
+    "S": "SER",
+    "T": "THR",
+    "W": "TRP",
+    "Y": "TYR",
+    "V": "VAL",
+}
+RESIDUE_TYPES_3to1 = {v: k for k, v in RESIDUE_TYPES_1to3.items()}
+
+
+class SequenceSegmentFactory(ABC):
+    def __init__(self) -> None:
+        pass
+
+    @abstractmethod
+    def get(self) -> str:
+        pass
+
+    @abstractmethod
+    def mutate(self) -> None:
+        pass
+
+    @abstractmethod
+    def num_mutation_candidates(self) -> int:
+        pass
+
+
+class ConstantSequenceSegment(SequenceSegmentFactory):
+    def __init__(self, sequence: str) -> None:
+        super().__init__()
+        self.sequence = sequence
+
+    def get(self) -> str:
+        return self.sequence
+
+    def mutate(self) -> None:
+        pass
+
+    def num_mutation_candidates(self) -> int:
+        return 0
+
+
+class FixedLengthSequenceSegment(SequenceSegmentFactory):
+    def __init__(
+        self, initial_sequence: Union[str, int], disallow_mutations_to_cysteine=True,
+    ) -> None:
+        super().__init__()
+        self.mutation_residue_types = (
+            RESIDUE_TYPES_WITHOUT_CYSTEINE
+            if disallow_mutations_to_cysteine
+            else ALL_RESIDUE_TYPES
+        )
+
+        self.sequence = (
+            initial_sequence
+            if type(initial_sequence) == str
+            else random_sequence(
+                length=initial_sequence, corpus=self.mutation_residue_types
+            )
+        )
+
+    def get(self) -> str:
+        return self.sequence
+
+    def mutate(self) -> None:
+        self.sequence = substitute_one_amino_acid(
+            self.sequence, self.mutation_residue_types
+        )
+
+    def num_mutation_candidates(self) -> int:
+        return len(self.sequence)
+
+
+def substitute_one_amino_acid(sequence: str, corpus: List[str]) -> str:
+    sequence = list(sequence)
+    index = np.random.choice(len(sequence))
+    sequence[index] = np.random.choice(corpus)
+    return "".join(sequence)
+
+
+def random_sequence(length: int, corpus: List[str]) -> str:
+    "Generate a random sequence using amino acids in corpus."
+
+    return "".join([np.random.choice(corpus) for _ in range(length)])
+
+
+def sequence_from_atomarray(atoms: AtomArray) -> str:
+    return "".join(
+        [RESIDUE_TYPES_3to1[aa] for aa in atoms[atoms.atom_name == "CA"].res_name]
+    )
+
+
+class VariableLengthSequenceSegment(SequenceSegmentFactory):
+    def __init__(
+        self,
+        initial_sequence: Union[str, int],
+        disallow_mutations_to_cysteine=True,
+        mutation_operation_probabilities: List[float] = [
+            3., # Substitution weight.
+            1., # Deletion weight.
+            1., # Insertion weight.
+        ],
+    ) -> None:
+        super().__init__()
+        self.mutation_residue_types = (
+            RESIDUE_TYPES_WITHOUT_CYSTEINE
+            if disallow_mutations_to_cysteine
+            else ALL_RESIDUE_TYPES
+        )
+
+        self.sequence = (
+            initial_sequence
+            if type(initial_sequence) == str
+            else random_sequence(
+                length=initial_sequence, corpus=self.mutation_residue_types
+            )
+        )
+
+        self.mutation_operation_probabilities = np.array(mutation_operation_probabilities)
+        self.mutation_operation_probabilities /= self.mutation_operation_probabilities.sum()
+
+    def get(self) -> str:
+        return self.sequence
+
+    def mutate(self) -> None:
+        mutation_operation = np.random.choice(
+            [
+                self._mutate_substitution,
+                self._mutate_deletion,
+                self._mutate_insertion,
+            ],
+            p=self.mutation_operation_probabilities,
+        )
+        mutation_operation()
+
+    def _mutate_substitution(self) -> str:
+        self.sequence = substitute_one_amino_acid(
+            self.sequence, self.mutation_residue_types
+        )
+
+    def _mutate_deletion(self) -> str:
+        self.sequence = delete_one_amino_acid(self.sequence)
+
+    def _mutate_insertion(self) -> str:
+        self.sequence = insert_one_amino_acid(
+            self.sequence, self.mutation_residue_types
+        )
+
+    def num_mutation_candidates(self) -> int:
+        # NOTE(brianhie): This should be `3*len(self.sequence) + 1`,
+        # since there are `len(self.sequence)` substitutions and
+        # deletions, and `len(self.sequence) + 1` insertions.
+        # However, as this is used to weight sequence segments for
+        # mutations when combined into a multi-segment program, we
+        # just weight by `len(self.sequence)` for now.
+        return len(self.sequence)
+
+
+def delete_one_amino_acid(sequence: str) -> str:
+    index = np.random.choice(len(sequence))
+    return sequence[:index] + sequence[index + 1 :]
+
+
+def insert_one_amino_acid(sequence: str, corpus: List[str]) -> str:
+    n = len(sequence)
+    index = np.random.randint(0, n) if n > 0 else 0
+    insertion = np.random.choice(corpus)
+    return sequence[:index] + insertion + sequence[index:]

weight/esm-main/examples/protein-programming-language/programs/__init__.py

@@ -0,0 +1 @@
+from programs.symmetric_binding import symmetric_binding_il10

weight/esm-main/scripts/atlas/v0/full/esm2_embeddings/backfill_v0_tm_.40_.50_plddt_.50_.60.txt

@@ -0,0 +1 @@
+https://dl.fbaipublicfiles.com/esmatlas/v0/full/lm_reps/backfill_v0_tm_.40_.50_plddt_.50_.60.npz

weight/esm-main/scripts/atlas/v0/full/esm2_embeddings/backfill_v0_tm_.50_.60_plddt_.50_.60.txt

@@ -0,0 +1 @@
+https://dl.fbaipublicfiles.com/esmatlas/v0/full/lm_reps/backfill_v0_tm_.50_.60_plddt_.50_.60.npz

weight/esm-main/scripts/atlas/v0/full/esm2_embeddings/backfill_v0_tm_.50_.60_plddt_.70_.80.txt

@@ -0,0 +1 @@
+https://dl.fbaipublicfiles.com/esmatlas/v0/full/lm_reps/backfill_v0_tm_.50_.60_plddt_.70_.80.npz

weight/esm-main/scripts/atlas/v0/full/esm2_embeddings/backfill_v0_tm_.60_.70_plddt_.50_.60.txt

@@ -0,0 +1 @@
+https://dl.fbaipublicfiles.com/esmatlas/v0/full/lm_reps/backfill_v0_tm_.60_.70_plddt_.50_.60.npz

weight/esm-main/scripts/atlas/v0/full/esm2_embeddings/backfill_v0_tm_.60_.70_plddt_.60_.70.txt

@@ -0,0 +1 @@
+https://dl.fbaipublicfiles.com/esmatlas/v0/full/lm_reps/backfill_v0_tm_.60_.70_plddt_.60_.70.npz

weight/esm-main/scripts/atlas/v0/full/esm2_embeddings/backfill_v0_tm_.60_.70_plddt_.70_.80.txt

@@ -0,0 +1 @@
+https://dl.fbaipublicfiles.com/esmatlas/v0/full/lm_reps/backfill_v0_tm_.60_.70_plddt_.70_.80.npz