Anonymoususer2223/ProtCompass_Embeddings

ProtCompass embeddings

Frozen protein embeddings, input-audit records and label files for the paper ProtCompass: Biological Information and Predictive Utility in Protein Representations (anonymous submission to ICLR 2027).

Protein encoders are usually evaluated by structural probing, downstream prediction and concept erasure. These measurements can mix signal learned by the encoder with information that is already present in the input or that the evaluation adds. ProtCompass separates these sources. It audits which inputs each encoder actually reads, compares structural probe scores with non-learned descriptions of the same inputs, compares downstream scores with simple sequence baselines, and controls concept-erasure experiments for the representation capacity that is removed together with the target property. The paper evaluates 33 encoders on a probing suite of 21 probes built from CATH 4.3 domains and on ten downstream tasks.

• Code, result tables and analysis scripts: https://anonymous.4open.science/r/ProEmbedEva-C4F6
• Predicted structures for four downstream tasks: https://huggingface.co/datasets/Anonymoususer2223/ProtEnv

This repository holds the release that backs the paper: embeddings, labels, the input audit and the pretrained weights of the structure encoders. Its paths mirror the working directory of the code (for example extract/embeddings/<task>/<encoder>/ and checkpoints/). An earlier release was removed; see Earlier release (removed).

Contents

Sizes are in GB (10^9 bytes).

Path	Content	Files	Size	Paper appendix
extract/embeddings/<task>/<encoder>/	pooled embeddings of 33 encoders and one non-learned reference on the ten downstream tasks	2,237	6.5	Tasks and protocols (The downstream tasks); Controls for the downstream comparison; Controls for the erasure analysis; Seed variability
extract/manifest/<task>.csv	one row per protein: id, split, sequence, structure file, label	11	0.12	Tasks and protocols (The downstream tasks)
extract/pairs/ppi_affinity_pairs.csv	chain pairs and labels of the PPI affinity task	1	<0.01	Tasks and protocols (The downstream tasks)
extract/encoder_input_audit.csv, encoder_atom_audit.csv, encoder_sidechain_audit.csv	measured input audit	3	<0.01	Study Design; The Encoder Panel
probing_embeddings/<encoder>/	per-residue and pooled embeddings of the 33 encoders on 19,461 CATH 4.3 domains	101	96.9	Tasks and protocols (Probe protocol); Controls for the probing comparison; Two residue-level tasks, and what the pooled readout hides
probing_embeddings_vframe/<encoder>_vframe/	GVP, GCPNet (random and pretrained) and MACE with the direction of their vector channels kept	19	5.2	Controls for the probing comparison (Equivariant encoders with the direction of their vector channels kept)
probing_data/labels.h5, probing_data/splits/	probe labels, coordinates and splits of the CATH domains	8	0.32	Tasks and protocols (The probing suite; Probe protocol)
extract/ab_modality/embeddings/, extract/ab_modality/probing_embeddings/	ESM3 and SaProt with the structure input removed, and SaProt with shuffled 3Di tokens	210	24.9	Controls for the downstream comparison (Removing the structure input from the same checkpoint)
extract/ab_modality/encoder_input_audit_ab*.csv	input audit of those three sources	2	<0.01	same
confirmation/, confirmation2/	probing cohorts A and B: CATH 4.4 domains absent from CATH 4.3	52	6.3	Confirmation on data and splits the development did not use (The probing headline on CATH 4.4 domains absent from CATH 4.3)
confirmation_downstream/	held-out downstream proteins never used in development	280	0.54	Confirmation on data and splits the development did not use (The demand results on downstream proteins never scored)
masif_site/	residue-level interface task rebuilt from MaSIF-site	90	20.2	Two residue-level tasks, and what the pooled readout hides (Interface residues)
atlas/data_v4/	latent-space atlas: 2D maps, k-NN purity and CKA for every task layer	12	0.06	Latent-Space Atlas
checkpoints/<encoder>/	pretrained weights of six structure encoders, with licences and SHA256SUMS	14	0.16	The Encoder Panel

The current release has 3,040 files and 161.2 GB in total. The appendix names are section and subsection titles of the paper.

Encoders

The paper evaluates 33 encoders. They are declared as 9 sequence-based, 19 structure-based and 5 multimodal models. The analyses group them by a measured input audit, which finds that 10 encoders do not read coordinates (sequence-only) and 23 do (coordinate-reading). The only encoder whose measured group differs from its declared class is ProstT5, which is declared multimodal and read on its amino-acid track only.

The directory name is the key used in every path. Width is the width of the stored array. Atoms read comes from the atom audit (extract/encoder_atom_audit.csv); "side chain" marks the three encoders that the side-chain audit (extract/encoder_sidechain_audit.csv) finds to read atoms beyond CB.

Directory	Paper name	Declared class	Weights	Width	Audit group	Reads residue identity	Atoms read
ankh_base	Ankh	sequence	pretrained	768	sequence-only	yes	-
esm1b	ESM-1b	sequence	pretrained	1280	sequence-only	yes	-
esm2_8m	ESM2-8M	sequence	pretrained	320	sequence-only	yes	-
esm2_35m	ESM2-35M	sequence	pretrained	480	sequence-only	yes	-
esm2_150m	ESM2-150M	sequence	pretrained	640	sequence-only	yes	-
esm2_650m	ESM2-650M	sequence	pretrained	1280	sequence-only	yes	-
esm2_3b	ESM2-3B	sequence	pretrained	2560	sequence-only	yes	-
protbert_bfd	ProtBert	sequence	pretrained	1024	sequence-only	yes	-
prottrans_t5_xl_u50	ProtT5-XL	sequence	pretrained	1024	sequence-only	yes	-
prostt5	ProstT5	multimodal	pretrained	1024	sequence-only	yes	-
dimenet	DimeNet	structure	random	128	coordinate-reading	yes	CA
esm_if1	ESM-IF1	structure	pretrained	512	coordinate-reading	no	N, CA, C
foldseek_3di	Foldseek	structure	pretrained (tokeniser, no learned parameters)	20	coordinate-reading	no	N, CA, CB
gcpnet_random	GCPNet	structure	random	116	coordinate-reading	yes	N, CA, C, O, CB, side chain
gcpnet_pretrained	GCPNet-pre	structure	pretrained	116	coordinate-reading	yes	N, CA, C, O, CB, side chain
gvp	GVP	structure	random	116	coordinate-reading	yes	N, CA, C
gearnet	GearNet	structure	random	512	coordinate-reading	yes	CA
gearnet_pretrained	GearNet-pre	structure	pretrained	512	coordinate-reading	yes	CA
ipa	IPA	structure	random	128	coordinate-reading	yes	CA
ipa_pretrained	IPA-pre	structure	pretrained	128	coordinate-reading	yes	CA
mace	MACE	structure	random	128	coordinate-reading	yes	CA
pottsmpnn	PottsMPNN	structure	random	128	coordinate-reading	no	N, CA, C, O
pottsmpnn_ft	PottsMPNN-ft	structure	pretrained	128	coordinate-reading	no	N, CA, C, O
pottsmpnn_pretrained	PottsMPNN-pre	structure	pretrained	128	coordinate-reading	no	N, CA, C, O
pottsmpnn_soluble	PottsMPNN-sol	structure	pretrained	128	coordinate-reading	no	N, CA, C, O
proteinmpnn	ProteinMPNN	structure	pretrained	128	coordinate-reading	no	N, CA, C, O
schnet	SchNet	structure	random	128	coordinate-reading	yes	CA
tfn	TFN	structure	random	128	coordinate-reading	yes	CA
tfn_pretrained	TFN-pre	structure	pretrained	128	coordinate-reading	yes	CA
cdconv	CDConv	multimodal	random	256	coordinate-reading	yes	CA
esm3	ESM3	multimodal	pretrained	1536	coordinate-reading	yes	N, CA, C
foldvision	FoldVision	multimodal	pretrained	1024	coordinate-reading	yes	N, CA, C, O, CB, side chain
saprot	SaProt	multimodal	pretrained	1280	coordinate-reading	yes	N, CA, CB

Randomly initialised encoders use one fixed seed for all proteins; the hash of their weights is recorded in meta.json. Three further encoders were audited and are not released: EGNN (its embeddings do not change when the coordinates change), GeoBPE (its output changes under a rigid motion) and dMaSIF (all-zero embeddings). FoldVision has no extraction on remote homology, so it is scored on nine tasks.

Which layer is read

Every embedding is taken from the last representation the encoder computes before any task head or decoder. Where an encoder has residue states, a protein is the masked mean of its residue states. The rule of each encoder is recorded in the rule field of its meta.json.

Encoders	Representation read
Ankh, ESM-1b, ESM2 (all sizes), ProtBert, ProtT5-XL	final hidden state, special tokens removed
ProstT5	final hidden state of the amino-acid track with the <AA2fold> prefix, special tokens removed; no 3Di input
ESM3	final trunk residue embeddings, sequence and structure tracks
SaProt	final hidden states at the residue positions of the combined amino-acid and 3Di tokens
SchNet, TFN, DimeNet, MACE, GearNet, CDConv, IPA (random and pretrained versions)	final invariant node states; IPA reads the structure in a canonical pose
GVP, GCPNet (random and pretrained)	final scalar channels plus the norm of each vector channel (100 + 16 = 116)
ProteinMPNN, PottsMPNN (all four)	encoder output h_V, before the decoder
ESM-IF1	output of the GVP-Transformer encoder, before decoding
Foldseek	one-hot 3Di state per residue; the protein vector is the 3Di state frequency
FoldVision	the model's global embedding; no residue states

Pretrained weights

checkpoints/ holds the local checkpoint files that six of the released structure encoders read. Every file is byte-identical to its upstream release, and loading it with the project's wrapper on CPU reproduces the weight hash (weights_sha256) recorded in the meta.json of the released embeddings. The upstream licence is stored next to each file, and checkpoints/SHA256SUMS lists the full hashes. checkpoints/README.md has a script that links the files to the paths the code reads.

Encoder	File in this repository	Size (MB)	SHA-256 (first 16)	Source	Licence	Path the code reads
ProteinMPNN	checkpoints/proteinmpnn/proteinmpnn_v48_020.pt	6.7	c9cb4a671d796041	ProteinMPNN repository, vanilla_model_weights/v_48_020.pt	MIT	protcompass/checkpoints/proteinmpnn_v48_020.pt
GearNet-pre	checkpoints/gearnet_pretrained/mc_gearnet_edge.pth	80.7	6dc11e2db6eff3c9	Zenodo, DOI 10.5281/zenodo.7593637	CC BY 4.0	protcompass/checkpoints/gearnet/mc_gearnet_edge.pth
GCPNet-pre (GCPNet reads its hyper-parameters)	checkpoints/gcpnet_pretrained/model_epoch_115_..._globalkendall_0_676.ckpt	14.3	ff7b73be01fb57d5	GCPNet repository, checkpoints/PSR/ (PSR task model)	MIT	protcompass/external_repos/GCPNet/checkpoints/PSR/
PottsMPNN-pre	checkpoints/pottsmpnn_pretrained/pottsmpnn_20.pt	20.5	28b1620a71883142	PottsMPNN repository, proteinmpnn_compatible_model_weights/	MIT	protcompass/external_repos/PottsMPNN/proteinmpnn_compatible_model_weights/pottsmpnn_20.pt
PottsMPNN-sol	checkpoints/pottsmpnn_soluble/sol_pottsmpnn_20.pt	20.7	f203a69773df41a1	PottsMPNN repository, soluble_model_weights/	MIT	protcompass/external_repos/PottsMPNN/soluble_model_weights/sol_pottsmpnn_20.pt
PottsMPNN-ft	checkpoints/pottsmpnn_ft/potts_ft.pt	20.7	b672ff4ed1492560	PottsMPNN repository, ft_model_weights/	MIT	protcompass/external_repos/PottsMPNN/ft_model_weights/potts_ft.pt

The code paths are relative to the root of the code repository, whose protcompass/ folder mirrors this repository. The upstream repositories are github.com/dauparas/ProteinMPNN (commit 8907e66), github.com/BioinfoMachineLearning/GCPNet (commit 172733b) and github.com/KeatingLab/PottsMPNN (commit a6ccac3); a clone of GCPNet or PottsMPNN at that commit already holds its files at the paths the code reads.

Weights loaded from a public source, which this repository does not copy:

Encoder	Source	Revision
ESM-1b	facebook/esm1b_t33_650M_UR50S	7b37824baec4d3658e1df7479222a7c79b465b76
ESM2-8M	facebook/esm2_t6_8M_UR50D	c731040fcd8d73dceaa04b0a8e6329b345b0f5df
ESM2-35M	facebook/esm2_t12_35M_UR50D	6fbf070e65b0b7291e7bbcd451118c216cff79d8
ESM2-150M	facebook/esm2_t30_150M_UR50D	a695f6045e2e32885fa60af20c13cb35398ce30c
ESM2-650M	facebook/esm2_t33_650M_UR50D	08e4846e537177426273712802403f7ba8261b6c
ESM2-3B	facebook/esm2_t36_3B_UR50D	476b639933c8baad5ad09a60ac1a87f987b656fc
ProtBert	Rostlab/prot_bert_bfd	6c5c8a55a52ff08a664dfd584aa1773f125a0487
ProtT5-XL	Rostlab/prot_t5_xl_half_uniref50-enc	94a6abc029ae13029317b140b7424e012bf8dfbf
Ankh	ElnaggarLab/ankh-base	d99cb6b966530dfc2ae96bc69d9255c2a07308b0
ProstT5	Rostlab/ProstT5	d7d097d5bf9a993ab8f68488b4681d6ca70db9e5
SaProt	westlake-repl/SaProt_650M_AF2	d9b9ad00ef61c0990e611b2b43f2231c7de24b38
FoldVision	AlexanderKroll/foldvision-encoder	not recorded
ESM-IF1	torch.hub.load("facebookresearch/esm:main", "esm_if1_gvp4_t16_142M_UR50") (ESM release, MIT)	not recorded
Foldseek 3Di	the mini3di package, version 0.2.1 (BSD-3-Clause); no weights file	0.2.1

The first twelve are Hugging Face hub ids; the revisions are those recorded in the checkpoint_revision field of each meta.json.

• ESM3. The esm3-sm-open-v1 weights are distributed by EvolutionaryScale under the Cambrian Non-Commercial License Agreement, which does not allow redistribution, so they are not in this repository. Accept the licence and download them from the gated Hugging Face repository EvolutionaryScale/esm3-sm-open-v1. The code expects them under protcompass/esm3-sm-open-v1/ (weights file data/weights/esm3_sm_open_v1.pth).
• TFN-pre and IPA-pre. Their weights were taken from the models' released repositories and are not redistributed here. The meta.json of their embeddings records the SHA-256 prefix of the file that was used (17af58de61a4674c for TFN-pre, cd4ca8e7f61daba2 for IPA-pre). The code expects them at protcompass/checkpoints/tfn_pretrained/best.pt and protcompass/checkpoints/ipa_pretrained/best.pt.
• Randomly initialised encoders (SchNet, TFN, DimeNet, MACE, GearNet, IPA, GVP, GCPNet, CDConv, PottsMPNN) have no weights file. They are built with the fixed seed 20260919, and the hash of the resulting weights is in each meta.json.

Downstream tasks

Fourteen tasks were assembled. Four (contact prediction, mutation effect, PPI sites and metal binding) were excluded because their labels or metric were unsuitable (appendix "Tasks and analyses the paper evaluated and does not use"), and ten are scored. Every encoder reads the same rows of a task. For each task the embeddings cover the rows of the manifest with stage1 == 1.

Directory	Task	Metric	Source dataset	Structures	train / valid / test
ec_classification	EC classification	accuracy	ProteinWorkshop	experimental (PDB)	6,000 / 1,500 / 3,000
go_mf	GO molecular function	F_max	ProteinWorkshop	experimental (PDB)	6,000 / 1,500 / 2,904
go_bp	GO biological process	F_max	ProteinWorkshop	experimental (PDB)	6,000 / 1,500 / 2,903
remote_homology	remote homology (fold)	accuracy	TAPE	experimental (PDB)	6,000 / 506 / 480
subcellular_localization	localisation, 10 classes	accuracy	DeepLoc 2	predicted (AlphaFold DB)	6,000 / 1,500 / 2,473
membrane_soluble	membrane versus soluble	AUROC	DeepLoc 2	predicted (AlphaFold DB)	6,000 / 1,500 / 3,000
stability	stability	Spearman	TAPE	predicted (ESMFold)	6,000 / 1,500 / 3,000
fluorescence	fluorescence	Spearman	TAPE	predicted (ESMFold)	6,000 / 1,500 / 3,000
solubility	solubility	AUROC	FLIP	predicted (ESMFold)	5,417 / 1,363 / 1,329
ppi_affinity	PPI affinity	Spearman	SKEMPI2	predicted (ESMFold)	2,589 chains; 2,352 / 194 / 326 pairs

The structure sources and split sizes are those of the paper's table "Structures and splits of the ten scored tasks". Every extracted row has a structure file whose sequence equals the task sequence (structure_coverage in meta.json).

File formats

Downstream embeddings: extract/embeddings/<task>/<encoder>/

File	Content
train.npy, valid.npy, test.npy	float32 array of shape (number of proteins in the split, width)
ids_train.txt, ids_valid.txt, ids_test.txt	one protein id per line; line i is row i of the array of the same split
meta.json	extraction record (fields below)
chains.npy, ids_chains.txt	PPI affinity only: one row per chain, in place of the three splits

• Row order equals the order of the manifest rows with stage1 == 1 and the same split. The id files are identical for every encoder of a task.
• Labels are in the label column of extract/manifest/<task>.csv: an integer class index (EC, localisation), a fold index (remote homology), a list of term indices written as a string (GO-MF, GO-BP), 0 or 1 (solubility, membrane versus soluble) or a real value (stability, fluorescence).
• PPI affinity: extract/pairs/ppi_affinity_pairs.csv has columns id, split, chain_a, chain_b, label. The paper represents a pair by concatenating the embeddings of chain_a and chain_b in that order.
• ref_composition/ in every task directory is not an encoder. It is the non-learned composition reference of the paper: 20 amino-acid frequencies, log length, length/600 and the fraction of residues outside the 20-letter alphabet (23 values).
• extract/manifest/<task>.csv has columns id, split, sequence, structure_path, length, label, stage1, plus align_path, struct_coverage (experimental and AlphaFold DB structures) or mean_plddt, chain_break (ESMFold structures).

Fields of meta.json (not every field is present for every encoder):

Field	Meaning
encoder, task	directory keys
family, target, patch, env	how the encoder was loaded: a Hugging Face language model (hf_plm), a model wrapper (registry) or a patched official implementation (patched)
weights	pretrained, random or none
checkpoint, checkpoint_revision, checkpoint_sha256, checkpoint_loaded_by, weights_sha256, seed	the weights used. Hub checkpoints give the hub id and revision; local checkpoint paths are given relative to the project directory
rule	which representation is read (see Which layer is read)
pooling	how residue states become one protein vector (masked mean over all residues of the chain)
inputs_passed, pose	what the encoder receives
dtype, hidden_size, n_layers, n_parameters	model details
manifest, rows, structure_coverage, n_per_split	which manifest rows were extracted
health	n_rows, dim, nan_values, all_zero_rows, distinct_rows, numerical_rank, max_abs of the stored array
input_audit	relative change of the embedding under a 1 Å coordinate jitter, a coordinate swap, a sequence shuffle and a rigid motion, and the audit verdict
date, seconds	when and how long

Probing embeddings: probing_embeddings/

The probing set is 19,461 CATH 4.3 domains with 2,823,525 residues. The same row convention holds for every probing directory in this repository.

File	Content
ids.txt	CATH domain id per line; line k is domain k
offsets.npy, lengths.npy	int64, shape (19461,); the residues of domain k are rows offsets[k] to offsets[k] + lengths[k] - 1
<encoder>/residue_fp16.npy	float16, shape (2823525, width); row r is row r of residue/* in probing_data/labels.h5
<encoder>/pooled_fp32.npy	float32, shape (19461, width); row k is domain k (mean over its residues)
<encoder>/meta.json	extraction record; its row_convention or rows field states the row order

FoldVision has no residue states, so probing_embeddings/foldvision/ holds pooled_fp32.npy only, and only the protein-level probes apply to it. Language models were run on the sequence of the residues kept in labels.h5; a discontinuous CATH domain is passed as one concatenated sequence.

probing_data/labels.h5 holds three groups. protein/ has one row per domain (id, length, CATH labels, split codes, protein-level targets). residue/ has one row per residue (amino acid, N, CA, C, O and CB coordinates with CB built from ideal geometry where it is absent, DSSP 3- and 8-state, relative solvent accessibility, B-factor, backbone angles, contact counts and other residue labels). pair/ has the sampled residue pairs with their distances. The split codes are split_superfamily (15,565 train, 1,948 validation, 1,948 test; no superfamily is shared between splits) and split_topology (15,378 / 2,159 / 1,924; the topology-disjoint split of the confirmation experiments), with 0 = train, 1 = validation and 2 = test. probing_data/splits/ lists the same splits as domain ids. The CATH domain structure files are public and are not redistributed here.

probing_embeddings_vframe/<encoder>_vframe/ follows the same layout. It stores the scalar channels together with the vector channels projected onto a per-residue frame built from N, CA and C (width 148 for GVP and the two GCPNets, 176 for MACE). The two GCPNet directories also hold residue_atommean_fp16.npy and pooled_atommean_fp32.npy, the same representation averaged over the heavy atoms of each residue. The frame definition is in each meta.json.

Modality ablation: extract/ab_modality/

Three extra sources hold the weights fixed and change only the input.

Source	Input
esm3_seqonly	ESM3 with the true sequence and the mask token at every structure position (no coordinates)
saprot_seqonly	SaProt with every 3Di token replaced by # (the token for a residue without structure)
saprot_shuf3di	SaProt with each protein's own 3Di tokens permuted within the protein (seed from the protein id)

extract/ab_modality/embeddings/<task>/<source>/ follows the downstream format for all ten tasks, and extract/ab_modality/probing_embeddings/<source>/ follows the probing format. The audit rows of these sources are in extract/ab_modality/encoder_input_audit_ab.csv and encoder_input_audit_ab_shuf.csv. The full-input ESM3 and SaProt embeddings they are compared with are the esm3 and saprot entries of the main release.

Confirmation sets

Path	Content
confirmation/	cohort A: 1,990 CATH v4.4 S40 domains absent from CATH v4.3. embeddings/<encoder>/ in the probing format for Ankh, ESM2-650M, ESM3, ESM-IF1, ProteinMPNN and ProtT5-XL; labels_v44.h5 in the schema of labels.h5; cohort_manifest.csv with the CATH class, architecture, topology and superfamily of each domain
confirmation2/	cohort B: 1,316 further CATH v4.4 domains, same layout, the six encoders of cohort A plus both GCPNets
confirmation_downstream/	eight held-out downstream cohorts, 13,632 proteins taken from manifest rows that were never extracted or scored in development. embeddings/<task>/<encoder>/ holds test.npy, ids_test.txt and meta.json; ids_<task>.txt and cohort_<task>.csv list the proteins and labels. Solubility and PPI affinity have no unused rows and have no cohort

Residue-level interface task: masif_site/

Interface residues rebuilt from MaSIF-site with a label defined from geometry: a residue of the target chain is an interface residue if any of its heavy atoms lies within 5 Å of a heavy atom of another chain. There are 3,327 chains (2,677 train, 297 validation, 353 test) and 647,332 residues. embeddings/<encoder>/ follows the probing format for 28 of the 33 encoders (SaProt, Foldseek, both GCPNets and FoldVision are not scored on this task). labels.h5 holds the residue labels (residue/interface) and the split (protein/split_masif); exclusions.csv lists the 35 entries that were removed.

Latent-space atlas: atlas/data_v4/

atlas.json holds every layer; the other files hold one task layer each (ec, go_mf, go_bp, localisation, homology, solubility, membrane, stability, fluorescence, affinity). Each layer is a stratified sample of about 2,000 proteins of the task's own rows. For every protein it gives the id, split and label; for every encoder the UMAP coordinates (k = 10, seed 42, Procrustes-aligned to ESM2-150M), the k-NN purity with its chance level and a five-fold linear probe accuracy; and between encoders the debiased linear CKA and k-NN overlap, both computed on the full embeddings. summary.json holds the per-layer summary. The atlas is built from the embeddings in extract/embeddings/.

Input audit

extract/encoder_input_audit.csv has one row per encoder and dataset (probing for the CATH set, stage1 for the downstream set). Each row gives the relative change of the pooled embedding over the same 32 proteins under a 1 Å coordinate jitter, a coordinate swap with another protein, a sequence shuffle and a rigid motion, the measured uses_coordinates, uses_residue_identity and invariant, the declared inputs, and a verdict. The file also holds the failing rows of EGNN and GeoBPE. extract/encoder_atom_audit.csv gives the change when each of the N, CA, C, O and CB coordinates is perturbed and the resulting atom_set. extract/encoder_sidechain_audit.csv gives the same for side-chain atoms.

Loading example

import numpy as np
import pandas as pd
from huggingface_hub import snapshot_download

repo = "Anonymoususer2223/ProtCompass_Embeddings"
task, encoder = "subcellular_localization", "esm2_650m"

# Download one encoder on one task and the task manifest (about 65 MB).
local = snapshot_download(repo, allow_patterns=[f"extract/embeddings/{task}/{encoder}/*",
                                                f"extract/manifest/{task}.csv"])
d = f"{local}/extract/embeddings/{task}/{encoder}"
splits = ("train", "valid", "test")
X = {s: np.load(f"{d}/{s}.npy") for s in splits}
ids = {s: open(f"{d}/ids_{s}.txt").read().split() for s in splits}

manifest = pd.read_csv(f"{local}/extract/manifest/{task}.csv")
manifest = manifest[manifest.stage1 == 1].set_index("id")
y = {s: manifest.loc[ids[s], "label"].to_numpy() for s in splits}

# A quick check with a logistic regression. The paper's heads (linear and MLP-2, selected on validation,
# three seeds) are in extract/score2.py of the code repository.
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler().fit(X["train"])
clf = LogisticRegression(max_iter=1000).fit(scaler.transform(X["train"]), y["train"])
print("test accuracy", clf.score(scaler.transform(X["test"]), y["test"]))

Per-residue probing embeddings are large (up to 14.5 GB for ESM2-3B), so open them with mmap_mode="r":

import h5py
# In allow_patterns a * also matches '/', so the top-level files are named one by one.
local = snapshot_download(repo, allow_patterns=["probing_embeddings/esm2_650m/*", "probing_embeddings/offsets.npy",
                                                "probing_embeddings/lengths.npy", "probing_embeddings/ids.txt",
                                                "probing_data/labels.h5"])
R = np.load(f"{local}/probing_embeddings/esm2_650m/residue_fp16.npy", mmap_mode="r")
offsets = np.load(f"{local}/probing_embeddings/offsets.npy")
lengths = np.load(f"{local}/probing_embeddings/lengths.npy")
with h5py.File(f"{local}/probing_data/labels.h5", "r") as f:
    k = 10
    rows = slice(offsets[k], offsets[k] + lengths[k])
    residue_embeddings = R[rows]          # (lengths[k], 1280)
    dssp3 = f["residue/dssp3"][rows]      # 0 = H, 1 = E, 2 = C
    split = f["protein/split_superfamily"][k]

Structures and datasets

The manifests give, for every row, the structure file the extraction read (structure_path, relative to the directory that holds the code repository's protcompass/ folder). The ESMFold structures of stability, fluorescence, solubility and PPI affinity are in the ProtEnv dataset under predicted_structures/; the last two components of structure_path (for example predictions_fluorescence/fluor_20897.pdb) are the member name in the corresponding archive. The experimental (PDB), AlphaFold DB and CATH structures are public and are not redistributed. The scripts that fetch them and build the manifests are in the code repository.

Earlier release (removed)

An earlier release, produced by an earlier extraction pipeline for a previous version of this work, was stored under embeddings/ and probing_IF/. The current paper does not use it. An audit in September 2026 found defects in it: structure encoders received placeholder or all-zero coordinates on several tasks, some entries were degenerate or mislabelled, and four of its tasks had defects in their labels or metric. It was removed from this repository. Every file the paper uses is in the current release.

License

The embeddings, labels, manifests, audit files and atlas data produced for this work are released under the MIT licence. The files under checkpoints/ keep their upstream licences: MIT for the ProteinMPNN, GCPNet and PottsMPNN files (the licence text is next to each file) and CC BY 4.0 for the GearNet weights (checkpoints/gearnet_pretrained/NOTICE.md).