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.