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Datasets:
AMix-Bio
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PFArena

Tasks:
Text Generation
Text Ranking
Languages:
English
Size:
100K<n<1M
Tags:
protein
mutation
protein-engineering
deep-mutational-scanning
fitness
stability
License:
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metadata 
annotations_creators:
  - expert-generated
  - machine-generated
language:
  - en
language_creators:
  - found
license:
  - other
multilinguality:
  - monolingual
pretty_name: PFArena
size_categories:
  - 100K<n<1M
source_datasets:
  - original
tags:
  - protein
  - mutation
  - protein-engineering
  - deep-mutational-scanning
  - fitness
  - stability
  - binding
  - benchmark
task_categories:
  - text-generation
  - text-ranking

Dataset Card for PFArena

Dataset Description

Dataset Summary

PFArena is an assay-grounded protein-mutation benchmark that systematically evaluates protein language models (PLMs), large language models (LLMs), and LLM-based agents under four protein modification settings. Comprising 202 unique assays, 293 assay--task instances, and 607,269 target candidate rows, PFArena defines four task interfaces that correspond to common decisions in protein-engineering workflows:

Task Directory Task purpose
T1 T1_single_mutant_generation Propose promising mutations without target-specific mutation measurements
T2 T2_measurement_free_multi_mutant_ranking Prioritize a supplied pool of multi-mutant candidates
T3 T3_anchor_informed_multi_mutant_ranking Rank successors of an experimentally measured mutant
T4 T4_single_mutant_informed_multi_mutant_ranking Prioritize combinations using measured effects of their component mutations

Every assay is normalized to a shared schema. The canonical ground-truth label is DMS_score, an assay-resolved score in which larger values always correspond to the better-performing direction for that assay (higher fitness, higher stability, higher binding, etc.).

Supported Tasks

The four tasks map to two model capabilities:

  • T1 — protein-mutation-generation: produce a ranked list of single-point substitutions predicted to be most beneficial. Evaluated with NMS@40 (normalized max score) and Recall@40.
  • T2–T4 — protein-mutation-ranking: rank a fixed candidate set of multi-point mutants. Evaluated with global ranking agreement (Spearman, NDCG) and top-five quality/recovery (NMS@5, Recall@5).

Dataset Structure

Data Instances

The dataset is a directory of CSV tables and FASTA/A3M alignment files. It has no train/validation/test split — the data are benchmark inputs and ground-truth labels, not a training corpus.

Two documentation files describe the schema precisely:

  • assay_columns.md — assay-level metadata columns (root assay.csv and per-task assay.csv).
  • norm_data_columns.md — candidate-table columns (norm_data/ files).

A single norm_data row (T1) looks like:

candidate_group_id,candidate_label,mutant,mutated_sequence,DMS_score
CDKN2A_elife_95347_supp4_supp6_T1,single_mutant,M1A,AEPAAGSSMEPS...,-0.5788823699525986

A single norm_data row (T2) looks like:

candidate_group_id,mutant,mutated_sequence,DMS_score
COMBINGYM_CreiLOV_fluorescence_log_mean_T2,G3E+R5D+T7H+...,MAELDHHFVVADA...,0.8368092913493479

Data Fields

Root assay.csv — one row per assay (202 rows). Shared assay metadata:

Column Description
assay_id Stable identifier for the assay–task record.
source_dataset Curation source (one of the seven sources below).
primary_task_class Primary benchmark class (activity_function, binding, stability).
uniprot_id UniProt accession (blank when no reliable accession was available).
fitness_type Broad biological objective.
readout_subclass Fine-grained assay readout subclass.
assay_modality Experimental measurement modality.
fitness_subtype Specific phenotype/readout taxonomy.
wildtype_sequence Wild-type protein sequence.
sequence_length Length of the wild-type sequence.
single_site_mutation_coverage Fraction of wild-type positions with measured single substitutions (empty only for the FLAb T2 library that has no single-mutant measurements).
biophysical_directness low (former scores 1–2), medium (3), or high (4–5).
n_rows Number of retained normalized measurement rows.
n_positions Number of sequence positions represented among retained variants.
a3m_relative_path Relative path to the assay-linked A3M alignment.
a3m_status_detail Status and coverage detail for the linked A3M alignment.

Per-task assay.csv adds the task-specific count/context columns. For example:

  • T3 adds anchor_mutant, anchor_DMS_score, anchor_mutation_count, anchor_effect_provided, anchor_is_multi_mutant, n_successors_available, n_successors_selected.
  • T4 adds n_context_single, n_combo_candidates, n_combo_available, single_context_data_path.

norm_data/ core columns (all candidate tables):

Column Description
candidate_group_id Candidate-group identifier, one per assay per task (e.g. {assay_id}_T2).
mutant Canonical mutation notation, e.g. A12V or A12V+G35L.
mutated_sequence Protein sequence carrying the listed mutations.
DMS_score Canonical assay score used for evaluation; larger = better (assay-resolved direction).

Task-specific variations:

  • T1 adds candidate_label (the single-mutant candidate-set label).
  • T3 files are named anchor_successor.csv and contain the successor candidate set; the anchor constraint is stored in the task assay.csv metadata, not per row.
  • T4 uses two files per assay: single_context_combo.csv (hidden multi-mutant candidates to rank) and single_mutant_context.csv (the visible measured single-mutant context).

Data Source

The 202 assays are drawn from seven sources:

Source Assays
ProteinGym 53
MaveDB 44
Human_Domainome 36
MegaScale 34
FLAb 23
CombinGym 7
target_dms 5

Biological objectives span stability (81 assays), binding (37), enzymatic activity (37), organismal/cellular fitness (31), fluorescence (13), electrophysiology (1), polyreactivity (1), and abundance/expression (1). Sequence lengths range from roughly 62 to over 1,360 residues, including multi-chain antibody constructs (heavy/light chains joined by :).

For each assay, an MSA is provided under msa_mmseqs_uniref100/ (256 .a3m files generated with MMseqs2 against UniRef100; multi-chain assays have one alignment per chain).

Considerations for Using the Data

  • Ground truth must not be leaked to inference. The benchmark is intended to measure a model's predictions; do not expose DMS_score (or other ground-truth columns) to an inference backend.
  • Keep the downloaded data unchanged. Candidate tables, wild-type sequences, and MSAs are frozen; modifying them will invalidate evaluation.
  • Score direction. DMS_score is already oriented so that larger is better per assay; do not re-normalize or re-orient without consulting the per-assay metadata.
  • uniprot_id may be blank where no reliable accession could be assigned.

Citation Information

If you use PFArena, cite the dataset and the original source datasets. Refer to the AMix-Bio/PFArena repository for the canonical citation.