FireProtDB

This is a flat, Dataset Viewer-friendly version of LiteFold/FireProtDB. The previous repository layout stored each row as a deeply nested row object, which made schema inference fail in the Hugging Face Dataset Viewer. This version keeps one row per upstream FireProtDB sequence or mutant experiment and exposes the main protein, mutation, structure, experiment, measurement, annotation, and publication fields as scalar columns.

Variable upstream lists are summarized into pipe-delimited columns such as measurement_types, annotation_types, uniprot_accessions, interpro_accessions, and pdb_ids. The original annotation, measurement, and feature lists are also retained as JSON strings in annotations_json, measurements_json, and features_json.

Dataset Summary

Metric	Value
Rows	5,465,660
Columns	99
Source table bytes	8,466,920,603
Mutant rows	5,453,252
Sequence rows	12,408
Substitution events	5,543,778
Deletion events	54,296
Insertion events	50,613

Splits

Splits are deterministic by source row id:

sha256("fireprotdb:{row_index}") % 10

Bucket 0 is test; buckets 1 through 9 are train.

Split	Rows
train	4,919,161
test	546,499

Source Datasets

Experiment dataset	Rows
Domainome DDG	4,071,188
MegaScale	775,235
Domainome FITNESS	591,671
ProTherm	27,406
COZYME	160

Common Measurement Columns

The table includes scalar columns for common FireProtDB measurements:

tm, dtm, dg, dg_text, ddg, dh, dcp, dhvh, cm, m_value, trypsin_ml, chymotrypsin_ml, stabilizing, stabilizing_text, domainome_fitness, domainome_fitness_std, domainome_ddg, domainome_ddg_std, reversibility, and state.

If a row has multiple measurements of the same type, the scalar column stores the first value and measurements_json retains the full upstream measurement list.

Loading With datasets

from datasets import load_dataset

ds = load_dataset("LiteFold/FireProtDB")
train = ds["train"]
test = ds["test"]

print(train[0]["protein_name"], train[0]["mutations"], train[0]["tm"])

Load a split directly:

from datasets import load_dataset

train = load_dataset("LiteFold/FireProtDB", split="train")

Filter for rows with DDG measurements:

from datasets import load_dataset

train = load_dataset("LiteFold/FireProtDB", split="train")
ddg_rows = train.filter(lambda row: row["ddg"] is not None)

Stream rows without downloading the full table first:

from datasets import load_dataset

rows = load_dataset("LiteFold/FireProtDB", split="train", streaming=True)
for row in rows:
    print(row["row_id"], row["experiment_dataset"], row["measurement_types"])
    break

Column Groups

Provenance: row_id, dataset_id, source_dataset, source_file, source_table, source_sha, row_index, split, subject_type.

Protein and sequence: entry_id, sequence_id, target_sequence_id, source_sequence_length, target_sequence_length, protein_id, protein_name, organism, isoform, protein_ids, protein_names, organisms, isoforms, uniprot_accessions, interpro_accessions, ec_numbers, megascale_ids, other_references.

Mutation and features: mutations, substitutions, deletions, insertions, mutation_count, substitution_count, deletion_count, insertion_count, first_position, first_source_aa, first_target_aa, conservation, feature_types, features_json.

Structure: pdb_ids, afdb_ids, structure_ids, structure_methods, structure_resolution_min, residue_positions, residue_chain_names, residue_secondary_structures, residue_in_pocket_any, residue_in_tunnel_any, residue_asa_mean, residue_bfactor_mean.

Experiment and measurements: experiment_id, experiment_dataset, ph, measure, method, buffer, buffer_conc, exp_temperature, ion, ion_conc, pdb_chain_mutation, measurement columns, measurement_types, measurement_datasets, annotation_types, annotations_json, measurements_json.

Publication: publication_id, publication_type, publication_title, publication_year, publication_doi, publication_pmid, publication_journal, publication_url, publication_author_count, publication_authors.

Files

• data/train-*.parquet: train split.
• data/test-*.parquet: test split.
• _MANIFEST.json: source provenance, split counts, and output schema.
• dataset_summary.json: processing summary and source/measurement counts.
• scripts/prepare_fireprotdb_dataset.py: script used to generate the flat table.

Source

Derived from LiteFold/FireProtDB, originally sourced from FireProtDB.

License

CC BY 4.0.

Citation

If you use this dataset, cite FireProtDB:

Stourac J, et al. FireProtDB: database of manually curated protein stability data. Nucleic Acids Research, 49(D1):D319-D324, 2021.