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Datasets:
GrimSqueaker
/
protsent-data

Tasks:
Feature Extraction
Sentence Similarity
Modalities:
Text
Formats:
parquet
Languages:
English
Size:
100M - 1B
ArXiv:
Tags:
biology
proteins
protein-language-model
contrastive-learning
embeddings
sentence-transformers
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metadata 
license: other
license_name: mixed-upstream
language:
  - en
tags:
  - biology
  - proteins
  - protein-language-model
  - contrastive-learning
  - embeddings
  - sentence-transformers
  - pfam
  - alphafold
  - string-db
  - proteingym
pretty_name: ProtSent Training Data
size_categories:
  - 100M<n<1B
task_categories:
  - feature-extraction
  - sentence-similarity
configs:
  - config_name: pfam
    data_files: pfam_sorted.parquet
  - config_name: afdb
    data_files: afdb_sorted.parquet
  - config_name: stringdb
    data_files: stringdb_train.parquet
  - config_name: dms
    data_files: dms_cosent.parquet

ProtSent Training Data

Training data for ProtSent (Protein Sentence Transformers) — a contrastive fine-tuning framework that adapts protein language models (ESM-2, ESM-C) into general-purpose sequence embeddings, so that nearest-neighbor distance reflects functional, evolutionary, and structural similarity between proteins.

Four of the paper's five training sources are included as parquet files. The synthetic Pfam hard-negatives set is excluded: the paper's ablation (Tables 4, 7) shows removing it improves aggregate performance (20/23 tasks improved, +7.9% mean delta, vs. 16/23 at +6.7% with it included).

Source Signal Positive-pair criterion Loss
Pfam evolutionary / functional homology same Pfam family MNRL / GIST
AFDB 3D structural similarity same Foldseek structural cluster MNRL / GIST
STRING physical / functional interaction pair-native (interacting proteins) MNRL / GIST
DMS continuous mutational fitness continuous score CoSENT

Files and schema

Two conventions are used. Grouped files store one sequence per row plus a group label; positive pairs are formed at training time from sequences sharing a group (capped per group via --max_pairs_per_cluster). Pair-native files store explicit pairs, one per row.

pfam_sorted.parquet — Pfam families (grouped, 2.4 GB)

column type notes
sequence string amino-acid sequence (Pfam domain)
family_id string Pfam family (PF accession, version stripped)
clan_id string Pfam clan; orphan families inherit clan_id := family_id
group_id string positive-pair label; equals family_id

Sorted clan_id → family_id; singleton families dropped. 28.5M domains after MMseqs2 linclust@70% deduplication. Do not shuffle — training relies on the sort order for windowed slicing.

afdb_sorted.parquet — AlphaFold DB Foldseek clusters (grouped, 13 GB)

column type notes
sequence string amino-acid sequence (pLDDT > 70, non-fragment)
cluster_id string Foldseek structural-cluster representative
afdb50_cluster_id string AFDB50 (50%-identity) representative
group_id string positive-pair label; equals cluster_id

Sorted by cluster_id; single-member clusters dropped. The positive label is the Foldseek structural cluster (cluster_id), coarser than AFDB50 sequence identity.

stringdb_train.parquet — STRING-DB v12 PPI (pair-native, 34 GB)

column type notes
seq1 string interacting protein A
seq2 string interacting protein B

combined_score ≥ 400, Bernett-decontaminated and cluster-deduplicated (see Provenance). Endpoint length filter [10, 2048].

dms_cosent.parquet — ProteinGym DMS / clinical (pair-native, scored, 106 MB)

column type notes
sentence_0 string sequence A
sentence_1 string sequence B
score float fitness-similarity target for CoSENT (0–1)

2.18M pairs over 3,576 wild-type targets and 2.09M unique mutants.

Usage

Load with datasets 

from datasets import load_dataset

pfam     = load_dataset("GrimSqueaker/protsent-data", "pfam",     split="train")
afdb     = load_dataset("GrimSqueaker/protsent-data", "afdb",     split="train")
stringdb = load_dataset("GrimSqueaker/protsent-data", "stringdb", split="train")
dms      = load_dataset("GrimSqueaker/protsent-data", "dms",      split="train")

Download raw parquets (for the ProtSent training code) 

from huggingface_hub import snapshot_download
snapshot_download("GrimSqueaker/protsent-data", repo_type="dataset", local_dir="data")

Files land flat in data/. Then train:

accelerate launch --num_processes 8 --mixed_precision bf16 \
  protein_pipeline.py train \
  --model Synthyra/ESMplusplus_large \
  --files data/pfam_sorted.parquet data/afdb_sorted.parquet data/stringdb_train.parquet \
  --dms_file data/dms_cosent.parquet \
  --loss_mode multi --multi_primary_loss gist \
  --max_seq_length 1024 --max_pairs_per_cluster 100

The training code detects file type by schema: seq1/seq2 → PPI pairs; sentence_0/sentence_1/score → DMS/CoSENT; otherwise grouped (sequence + group_id), with positive pairs formed per group.

Provenance and filtering

  • Pfam — Pfam-A.fasta.gz + Pfam-A.clans.tsv.gz (EBI current release). MMseqs2 easy-linclust at 70% identity (--cov-mode 1 -c 0.8), family→clan join, singleton families dropped, sorted by (clan_id, family_id).
  • AFDB — sequences from willdaspit/afdb_clustered_seqs (pLDDT > 70, non-fragment) inner-joined to the Steinegger-Lab AFDB Foldseek v6 S-cluster mapping (cluFlag ∈ {1,2}) on the AFDB50 representative; positive label = Foldseek cluster.
  • STRING — STRING v12.0 physical links (combined_score ≥ 400). Bernett decontamination: any STRING protein clustering (MMseqs2 linclust, 50% id / 80% cov) with a Synthyra/bernett_gold_ppi test sequence is removed. Survivors two-stage clustered (65%/85% then 50%/75%); pairs canonicalized and deduplicated per cluster-pair; endpoint length filter [10, 2048].
  • DMS — OATML-Markslab/ProteinGym_v1 (DMS + clinical, substitutions + indels). Per-assay z-scored, clipped to [−3, 3], rescaled to [0, 1]; clinical Pathogenic→0 / Benign→1. GB1 and GFP assays dropped (benchmark overlap); supervised test folds removed.

Thresholds, identity cutoffs, and seeds are fixed in data_prep.py.

Leakage controls

  • DMS and STRING are decontaminated against their downstream benchmarks (ProteinGym excluded from evaluation; Bernett test set removed from STRING).
  • Pfam training uses family-membership labels, disjoint from the fold-level remote-homology eval split.
  • AFDB uses Foldseek-cluster co-membership rather than SCOPe labels. AFDB sequences are not filtered against SCOPe test domains, so partial sequence overlap with SCOPe-40 retrieval is possible — noted in the paper as a limitation.

Reproduce 

# requires MMseqs2 on PATH for pfam + stringdb
python data_prep.py --dataset pfam
python data_prep.py --dataset afdb --limit_gb 0
python data_prep.py --dataset stringdb --max_seq_len 2048 --min_seq_len 10
python data_prep.py --dataset dms

Citation 

@article{ofer2026protsent,
  title         = {ProtSent: Protein Sentence Transformers},
  author        = {Ofer, Dan and Perets, Oriel and Linial, Michal and Rappoport, Nadav},
  journal       = {arXiv preprint arXiv:2605.06830},
  year          = {2026},
  eprint        = {2605.06830},
  archivePrefix = {arXiv},
  primaryClass  = {cs.LG},
  url           = {https://arxiv.org/abs/2605.06830}
}

License

Pfam (EBI), AlphaFold DB (CC-BY-4.0), STRING (CC-BY-4.0), and ProteinGym each carry their own license.