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archive/COVID/ym005/README.md

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-# Covid mutagenesis dataset (YM_005)
-
-## Overview
-
-YM_005 is a Covid receptor-binding domain (RBD) single-site mutagenesis (SSM) library against a panel of 33 ScFvs.
-
-## Experimental details
-
-We studied the effects of a panel of ScFvs against COVID RBD. In this dataset, our panel of ScFvs are tested in 2 orientations: LH for light-heavy chains and HL heavy-light chains. We explore the local landscape of the RBD in tandem with Covid for epitope mapping by mutating each position. We include a control of ACE2, as the protein of most therapeutic antibodies disrupt.
-
-This dataset includes 62 unique scFvs and 2431 unique RBD sequences.
-
-A more extensive methods section can be found in our publication [here](https://academic.oup.com/abt/article/5/2/130/6584706#372391532).
-
-## Dataset schema
-
-The dataset will contain the following columns:
-
-- `mata_description`: Description of the scfvs; HL/LH indicate orientation of light/heavy or heavy/light
-- `mata_sequence`: Scfv sequences
-- `matalpha_description`: Description of the RBD mutation
-- `matalpha_sequence`: Sequence of the covid binding protein
-- `alphaseq_affinity`: Log10 Kd affinity score between the pair of sequences
-- `alphaseq_affinity_lower_bound`: Lower bound of affinity
-- `alphaseq_affinity_upper_bound`: Upper bound of affinity
-
-## Misc dataset details
-
-We define the following binders:
-
-### A-library: (scFvs)
-- `ACE2_Full`:
-- `CR3022_scFv_LH_Mod`:
-- `MERS_VHH55`:
-- `SARS_VHH72`:
-- `m396_scFv_LH_Mod`:
-
-### Alpha-library: 
-- Any `matalpha_description` that contains the term "WT" can be assumed as a replicate of the WT target. Other sequences will be indicated by their original residue, position of mutation, and mutated residue (i.e. S23A is Ser -> Ala mutation in position 23). 
-
-## Citation
-Please cite 
-

archive/COVID/ym005/YM_005.csv

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archive/COVID/ym1068/README.md

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-# Covid antibody optimization designs (YM_1068)
-
-## Overview
-
-YM_1068 are VHH72 designs against SARS CoV-2 RBD. We explored several model hypothesis: (i) Does pre-training aid predicitivity and (ii) does the featurization of the input sequences matter. To test pretraining, we refer to `mata_descriptions` with the term \textbf{warm} to include pretraining, and \textbf{cold} to start from a randomly initialized seed. For featurization, we explored \textbf{label-encoded} sequences with a one-hot-encoder of amino acid identities, versus an \textbf{ESM}-featurized embedding to represent each sequence in the PPI.
-
-## Experimental details
-
-We studied the efficacy of generating binders with different model hyperparameters. This dataset includes 36862 unique VHHs and 8 unique RBD sequences.
-
-A more extensive methods section can be found in our publication [here](https://pmc.ncbi.nlm.nih.gov/articles/PMC12296056/). 
-
-## Dataset schema
-
-The dataset will contain the following columns:
-
-- `mata_description`: Description of the binder designs; contains warm/cold or label-encoded/ESM information. WT indicated as "WT"
-- `mata_sequence`: VHH sequences
-- `matalpha_description`: SARS-Cov 2 RBD proteins
-- `matalpha_sequence`: Sequence of the covid binding protein
-- `alphaseq_affinity`: Log10 Kd affinity score between the pair of sequences
-- `alphaseq_affinity_lower_bound`: Lower bound of affinity
-- `alphaseq_affinity_upper_bound`: Upper bound of affinity
-
-## Misc dataset details
-
-We define the following binders:
-
-### A-library: (scFvs)
-There are several terms you can filter by:
-
-- `VHH_WT_<i>`: These are WT replicates.
-- `VHH_label_encoded_cold`: Label encoded sequences with no pretraining
-- `VHH_label_encoded_warm`: Label encoded sequences with pretraining
-- `VHH_esm_cold`: ESM featurized sequences with no pretraining
-- `VHH_esm_warm`: ESM featurized sequences with pretraining
-
-### Alpha-library: 
-- `SARS-CoV2_RBD_(6LZG)`
-- `WIV1`
-- `LYRa11`
-- `MERS_RBD`
-- `OMICRON_BA2_TStarr_Seq&Trunc`
-- `SARS-COV-2_GAMMA_RBD_LLNL_Trunc`
-- `SARS-CoV-2_Delta_RBD_L452R_T478K`
-- `SARS-CoV1_RBD_(6LZG_Mimic)`
-
-## Citation
-Please cite 
-

archive/COVID/ym1068/YM_1068.csv

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archive/COVID/ym549/README.md

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-# VHH72 COVID designs (YM-549)
-
-## Overview
-
-YM_549 are VHH72 designs against SARS CoV-2 RBD. We wanted to perturb the local landscape of VHH72, so we performed mutagenesis to observe the sensitivity of VHH-72 to different RBD targets. This dataset is great to study the local sensitivity of mutations relative to parent "WT" VHH72. 
-
-## Experimental details
-
-This dataset includes 29765 unique VHHs and 8 unique RBD sequences. The alpha-library is the same as YM_1068.
-
-## Dataset schema
-
-The dataset will contain the following columns:
-
-- `mata_description`: Candidate labels
-- `mata_sequence`: VHH sequences
-- `matalpha_description`: SARS-Cov 2 RBD proteins
-- `matalpha_sequence`: Sequence of the covid binding protein
-- `alphaseq_affinity`: Log10 Kd affinity score between the pair of sequences
-- `alphaseq_affinity_lower_bound`: Lower bound of affinity
-- `alphaseq_affinity_upper_bound`: Upper bound of affinity
-
-## Misc dataset details
-
-We define the following binders:
-
-### A-library: (scFvs)
-There are several terms you can filter by:
-
-- `wt_<i>`: These are WT replicates.
-- `candidate_`: Various mutations of VHH-72
-
-### Alpha-library: 
-- `SARS-CoV2_RBD_(6LZG)`
-- `WIV1`
-- `LYRa11`
-- `MERS_RBD`
-- `OMICRON_BA2_TStarr_Seq&Trunc`
-- `SARS-COV-2_GAMMA_RBD_LLNL_Trunc`
-- `SARS-CoV-2_Delta_RBD_L452R_T478K`
-- `SARS-CoV1_RBD_(6LZG_Mimic)`
-
-## Citation
-Please cite 
-

archive/COVID/ym549/YM_549.csv

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archive/HER2/ym989/README.md

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-# Trastuzumab CDR designs (YM_989)
-
-## Overview
-
-YM_989 are Trastuzumab designs against HER2. We explored several model hypothesis: (i) Does pre-training aid predicitivity and (ii) does the featurization of the input sequences matter. To test pretraining, we refer to `mata_descriptions` with the term \textbf{warm} to include pretraining, and \textbf{cold} to start from a randomly initialized seed. For featurization, we explored \textbf{label-encoded} sequences with a one-hot-encoder of amino acid identities, versus an \textbf{ESM}-featurized embedding to represent each sequence in the PPI.
-
-## Experimental details
-
-We studied the efficacy of generating binders with different model hyperparameters. This dataset includes 20828 unique VHHs and 1 unique sequences. All designs are limited to the CDRs of the proteins of interest
-
-A more extensive methods section can be found in our publication [here](https://pmc.ncbi.nlm.nih.gov/articles/PMC12296056/). 
-
-## Dataset schema
-
-The dataset will contain the following columns:
-
-- `mata_description`: Description of the binder designs; contains warm/cold or label-encoded/ESM information. WT indicated as "WT"
-- `mata_sequence`: scFv sequences
-- `matalpha_description`: HER2 protein
-- `matalpha_sequence`: HER2 sequence
-- `alphaseq_affinity`: Log10 Kd affinity score between the pair of sequences
-- `alphaseq_affinity_lower_bound`: Lower bound of affinity
-- `alphaseq_affinity_upper_bound`: Upper bound of affinity
-
-## Misc dataset details
-
-We define the following binders:
-
-### A-library: (scFvs)
-There are several terms you can filter by:
-
-- `TrastuzumabCDR_WT_<i>`: These are WT replicates.
-- `TrastuzumabCDR_label_encoded_cold`: Label encoded sequences with no pretraining
-- `TrastuzumabCDR_label_encoded_warm`: Label encoded sequences with pretraining
-- `TrastuzumabCDR_esm_cold`: ESM featurized sequences with no pretraining
-- `TrastuzumabCDR_esm_warm`: ESM featurized sequences with pretraining
-
-### Alpha-library: 
-There is only 1 target of interest.
-
-## Citation
-Please cite 
-

archive/HER2/ym989/YM_989.csv

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archive/HER2/ym990/README.md

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-# Trastuzumab CDR + framework designs (YM_989)
-
-## Overview
-
-YM_989 are Trastuzumab designs against HER2. We explored several model hypothesis: (i) Does pre-training aid predicitivity and (ii) does the featurization of the input sequences matter. To test pretraining, we refer to `mata_descriptions` with the term \textbf{warm} to include pretraining, and \textbf{cold} to start from a randomly initialized seed. For featurization, we explored \textbf{label-encoded} sequences with a one-hot-encoder of amino acid identities, versus an \textbf{ESM}-featurized embedding to represent each sequence in the PPI.
-
-## Experimental details
-
-We studied the efficacy of generating binders with different model hyperparameters. This dataset includes 20828 unique VHHs and 1 unique sequences. In this experiment, we enable designs to span a window that encompasses the frameworks and CDR regions.
-
-A more extensive methods section can be found in our publication [here](https://pmc.ncbi.nlm.nih.gov/articles/PMC12296056/). 
-
-## Dataset schema
-
-The dataset will contain the following columns:
-
-- `mata_description`: Description of the binder designs; contains warm/cold or label-encoded/ESM information. WT indicated as "WT"
-- `mata_sequence`: scFv sequences
-- `matalpha_description`: HER2 protein
-- `matalpha_sequence`: HER2 sequence
-- `alphaseq_affinity`: Log10 Kd affinity score between the pair of sequences
-- `alphaseq_affinity_lower_bound`: Lower bound of affinity
-- `alphaseq_affinity_upper_bound`: Upper bound of affinity
-
-## Misc dataset details
-
-We define the following binders:
-
-### A-library: (scFvs)
-There are several terms you can filter by:
-
-- `TrastuzumabCDR_WT_<i>`: These are WT replicates.
-- `TrastuzumabCDR_label_encoded_cold`: Label encoded sequences with no pretraining
-- `TrastuzumabCDR_label_encoded_warm`: Label encoded sequences with pretraining
-- `TrastuzumabCDR_esm_cold`: ESM featurized sequences with no pretraining
-- `TrastuzumabCDR_esm_warm`: ESM featurized sequences with pretraining
-
-### Alpha-library: 
-There is only 1 target of interest.
-
-## Citation
-Please cite 
-

archive/HER2/ym990/YM_990.csv

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archive/PD1/ym852/README.md

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-# {DATASET}
-
-## Overview
-
-YM_852 are Pembrolizumab mutations against its native target; we introduce mutations that include deletions, insertions, double and single mutations to quantify the sensitivity of the local landscape in engaging the native target.
-
-## Experimental details
-
-This dataset includes 29883 unique scFvs and 1 unique native target sequence. A more extensive methods section can be found in our publication [here](https://pmc.ncbi.nlm.nih.gov/articles/PMC12296056/). 
-
-## Dataset schema
-
-The dataset will contain the following columns:
-
-- `mata_description`: Description of the binder designs; contains warm/cold or label-encoded/ESM information. WT indicated as "WT_"
-- `mata_sequence`: VHH sequences
-- `matalpha_description`: SARS-Cov 2 RBD proteins
-- `matalpha_sequence`: Sequence of the covid binding protein
-- `alphaseq_affinity`: Log10 Kd affinity score between the pair of sequences
-- `alphaseq_affinity_lower_bound`: Lower bound of affinity
-- `alphaseq_affinity_upper_bound`: Upper bound of affinity
-
-## Misc dataset details
-
-We define the following binders:
-
-### A-library: (scFvs)
-There are several terms you can filter by:
-
-- `WT_<i>`: These are WT replicates.
-- `del_<i>`: A deletion from the WT
-- `ins_<i>`: An insertion from WT
-- `pair_<i>`: A double mutation (2 residues mutated from WT)
-- `single_<i>`: A single mutation 
-
-To get the mutations of interest relative to the parent, we recommend an alignment to the WT sequence.
-
-### Alpha-library: 
-There is only 1 sequence, which is the native target.
-
-## Citation
-Please cite 
-

archive/PD1/ym852/YM_852.csv

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archive/PD1/ym985/README.md

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-# Pembrolizumab Designs (YM_985)
-
-## Overview
-
-YM_985 includes Alphabind designs against PD-1. We explored several model hypothesis: (i) Does pre-training aid predicitivity and (ii) does the featurization of the input sequences matter. To test pretraining, we refer to `mata_descriptions` with the term \textbf{warm} to include pretraining, and \textbf{cold} to start from a randomly initialized seed. For featurization, we explored \textbf{label-encoded} sequences with a one-hot-encoder of amino acid identities, versus an \textbf{ESM}-featurized embedding to represent each sequence in the PPI.
-
-## Experimental details
-
-We studied the efficacy of generating binders with different model hyperparameters. This dataset includes 34890 unique VHHs and 1 unique RBD sequences.
-
-A more extensive methods section can be found in our publication [here](https://pmc.ncbi.nlm.nih.gov/articles/PMC12296056/). 
-
-## Dataset schema
-
-The dataset will contain the following columns:
-
-- `mata_description`: Description of the binder designs; contains warm/cold or label-encoded/ESM information. WT indicated as "WT"
-- `mata_sequence`: scFv sequences
-- `matalpha_description`: The target protein of interest
-- `matalpha_sequence`: Target sequence (only 1)
-- `alphaseq_affinity`: Log10 Kd affinity score between the pair of sequences
-- `alphaseq_affinity_lower_bound`: Lower bound of affinity
-- `alphaseq_affinity_upper_bound`: Upper bound of affinity
-
-## Misc dataset details
-
-We define the following binders:
-
-### A-library: (scFvs)
-There are several terms you can filter by:
-
-- `Pembro144_WT_<i>`: These are WT replicates.
-- `Pembro144_label_encoded_cold`: Label encoded sequences with no pretraining
-- `Pembro144_label_encoded_warm`: Label encoded sequences with pretraining
-- `Pembro144_esm_cold`: ESM featurized sequences with no pretraining
-- `Pembro144_esm_warm`: ESM featurized sequences with pretraining
-
-### Alpha-library: 
-There is only 1 target
-
-## Citation
-Please cite 
-

archive/PD1/ym985/YM_985.csv

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archive/TIGIT/ym693/README.md

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-# anti-TIGIT designs (YM_693)
-
-## Overview
-
-YM_693 is a dataset of anti-TIGIT designs against the TIGIT target. This dataset contains only 2 targets, but they are species homologs of human and mouse. The designs offer a few mutations to study the local interaction between  and TIGIT. This is a dataset to explore relative affinities to the parent for antibody optimization.
-
-## Experimental details
-
-We studied the efficacy of generating binders with different model hyperparameters. This dataset includes 26726 unique scFvs and 2 unique target sequences.
-
-A more extensive methods section can be found in our publication [here](https://pmc.ncbi.nlm.nih.gov/articles/PMC12296056/). 
-
-## Dataset schema
-
-The dataset will contain the following columns:
-
-- `mata_description`: Candidate labels
-- `mata_sequence`: scFv sequences
-- `matalpha_description`: TIGIT proteins
-- `matalpha_sequence`: Sequence of the TIGIT homolog
-- `alphaseq_affinity`: Log10 Kd affinity score between the pair of sequences
-- `alphaseq_affinity_lower_bound`: Lower bound of affinity
-- `alphaseq_affinity_upper_bound`: Upper bound of affinity
-
-## Misc dataset details
-
-We define the following binders:
-
-### A-library: (scFvs)
-There are several terms you can filter by:
-
-- `wt_<i>`: These are WT replicates.
-- `candidate_`: Various mutations of Pembrolizumab
-
-### Alpha-library: 
-- `TIGIT_22-137_POI-AGA2`: Human TIGIT
-- `TIGIT_Mouse`: Mouse TIGIT
-
-## Citation
-Please cite 
-

archive/TIGIT/ym693/YM_693.csv

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archive/TIGIT/ym988/README.md

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-# anti-TIGIT designs (YM_1068)
-
-## Overview
-
-YM_988 includes ABC001 against 2 TIGIT homologs. We explored several model hypothesis: (i) Does pre-training aid predicitivity and (ii) does the featurization of the input sequences matter. To test pretraining, we refer to `mata_descriptions` with the term \textbf{warm} to include pretraining, and \textbf{cold} to start from a randomly initialized seed. For featurization, we explored \textbf{label-encoded} sequences with a one-hot-encoder of amino acid identities, versus an \textbf{ESM}-featurized embedding to represent each sequence in the PPI. Optimization was performed on the human ortholog. 
-
-## Experimental details
-
-We studied the efficacy of generating binders with different model hyperparameters. This dataset includes 35929 unique scFvs and 2 unique TIGIT homologs sequences.
-
-A more extensive methods section can be found in our publication [here](https://pmc.ncbi.nlm.nih.gov/articles/PMC12296056/). 
-
-## Dataset schema
-
-The dataset will contain the following columns:
-
-- `mata_description`: Description of the binder designs; contains warm/cold or label-encoded/ESM information. WT indicated as "WT"
-- `mata_sequence`: svFv sequences
-- `matalpha_description`: TIGIT homologs
-- `matalpha_sequence`: Sequence of the TIGIT protein
-- `alphaseq_affinity`: Log10 Kd affinity score between the pair of sequences
-- `alphaseq_affinity_lower_bound`: Lower bound of affinity
-- `alphaseq_affinity_upper_bound`: Upper bound of affinity
-
-## Misc dataset details
-
-We define the following binders:
-
-### A-library: (scFvs)
-There are several terms you can filter by:
-
-- `ABC001_WT_<i>`: These are WT replicates.
-- `ABC001_label_encoded_cold`: Label encoded sequences with no pretraining
-- `ABC001_label_encoded_warm`: Label encoded sequences with pretraining
-- `ABC001_esm_cold`: ESM featurized sequences with no pretraining
-- `ABC001_esm_warm`: ESM featurized sequences with pretraining
-
-### Alpha-library: 
-- `TIGIT_22-137_POI-AGA2`: Human TIGIT
-- `TIGIT_Mouse`: Mouse TIGIT
-
-## Citation
-Please cite 
-

archive/TIGIT/ym988/YM_988.csv

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