--- configs: - config_name: secstr data_files: - split: train path: data/secstr/train.parquet - split: validation path: data/secstr/validation.parquet - split: test path: data/secstr/test.parquet - config_name: secstr8 data_files: - split: train path: data/secstr8/train.parquet - split: validation path: data/secstr8/validation.parquet - split: test path: data/secstr8/test.parquet - config_name: disorder data_files: - split: train path: data/disorder/train.parquet - split: validation path: data/disorder/validation.parquet - split: test path: data/disorder/test.parquet - config_name: rsa data_files: - split: train path: data/rsa/train.parquet - split: validation path: data/rsa/validation.parquet - split: test path: data/rsa/test.parquet - config_name: meltome data_files: - split: train path: data/meltome/train.parquet - split: validation path: data/meltome/validation.parquet - split: test path: data/meltome/test.parquet - config_name: subloc data_files: - split: train path: data/subloc/train.parquet - split: validation path: data/subloc/validation.parquet - split: test path: data/subloc/test.parquet - config_name: gpsite_dna data_files: - split: train path: data/gpsite_dna/train.parquet - split: validation path: data/gpsite_dna/validation.parquet - split: test path: data/gpsite_dna/test.parquet - config_name: gpsite_rna data_files: - split: train path: data/gpsite_rna/train.parquet - split: validation path: data/gpsite_rna/validation.parquet - split: test path: data/gpsite_rna/test.parquet - config_name: gpsite_pep data_files: - split: train path: data/gpsite_pep/train.parquet - split: validation path: data/gpsite_pep/validation.parquet - split: test path: data/gpsite_pep/test.parquet - config_name: gpsite_pro data_files: - split: train path: data/gpsite_pro/train.parquet - split: validation path: data/gpsite_pro/validation.parquet - split: test path: data/gpsite_pro/test.parquet - config_name: gpsite_atp data_files: - split: train path: data/gpsite_atp/train.parquet - split: validation path: data/gpsite_atp/validation.parquet - split: test path: data/gpsite_atp/test.parquet - config_name: gpsite_hem data_files: - split: train path: data/gpsite_hem/train.parquet - split: validation path: data/gpsite_hem/validation.parquet - split: test path: data/gpsite_hem/test.parquet - config_name: gpsite_zn data_files: - split: train path: data/gpsite_zn/train.parquet - split: validation path: data/gpsite_zn/validation.parquet - split: test path: data/gpsite_zn/test.parquet - config_name: gpsite_ca data_files: - split: train path: data/gpsite_ca/train.parquet - split: validation path: data/gpsite_ca/validation.parquet - split: test path: data/gpsite_ca/test.parquet - config_name: gpsite_mg data_files: - split: train path: data/gpsite_mg/train.parquet - split: validation path: data/gpsite_mg/validation.parquet - split: test path: data/gpsite_mg/test.parquet - config_name: gpsite_mn data_files: - split: train path: data/gpsite_mn/train.parquet - split: validation path: data/gpsite_mn/validation.parquet - split: test path: data/gpsite_mn/test.parquet license: mit tags: - protein - biology - benchmark - data-leakage - protein-language-model - multi-task --- # Moomboh/mutafitup-datasets Leakage-free protein function prediction benchmarks for multi-task protein language model (pLM) fine-tuning. This dataset contains **16 prediction tasks** drawn from three independent data families, resplit to eliminate cross-dataset sequence similarity-based data leakage between training, validation, and test sets. ## Datasets | Dataset | Task type | Labels | Metric | Provenance | |---------|-----------|--------|--------|------------| | `secstr` | per-residue classification | 3 | accuracy | DSSP assignments from [NetSurfP-2.0](https://doi.org/10.1002/prot.25674) (Klausen et al., 2019), test set from [ProtTrans](https://doi.org/10.1109/TPAMI.2021.3095381) NEW364 (Elnaggar et al., 2022) | | `secstr8` | per-residue classification | 8 | accuracy | DSSP assignments from [NetSurfP-2.0](https://doi.org/10.1002/prot.25674) (Klausen et al., 2019), test set from [ProtTrans](https://doi.org/10.1109/TPAMI.2021.3095381) NEW364 (Elnaggar et al., 2022) | | `disorder` | per-residue regression | continuous | spearman | [CheZOD database](https://doi.org/10.1038/s41598-020-71716-1) (Dass et al., 2020) -> [SETH](https://doi.org/10.3389/fbinf.2022.1019597) (Ilzhofer et al., 2022) -> [Schmirler et al., 2024](https://doi.org/10.1038/s41467-024-51844-2) | | `rsa` | per-residue regression | continuous | spearman | Isolated-chain RSA from [NetSurfP-2.0](https://doi.org/10.1002/prot.25674) (Klausen et al., 2019), test set CB513 | | `meltome` | per-protein regression | continuous | spearman | [Meltome Atlas](https://doi.org/10.1038/s41592-020-0801-4) (Jarzab et al., 2020) -> [FLIP](https://doi.org/10.1101/2021.11.09.467890) (Dallago et al., 2021) -> [Schmirler et al., 2024](https://doi.org/10.1038/s41467-024-51844-2) | | `subloc` | per-protein classification | 10 | accuracy | [Light Attention](https://doi.org/10.1093/bioadv/vbab035) (Stärk et al., 2021) -> [Schmirler et al., 2024](https://doi.org/10.1038/s41467-024-51844-2) | | `gpsite_dna` | per-residue classification | 2 | accuracy | [GPSite](https://doi.org/10.7554/eLife.93695) (Yuan et al., 2024) | | `gpsite_rna` | per-residue classification | 2 | accuracy | [GPSite](https://doi.org/10.7554/eLife.93695) (Yuan et al., 2024) | | `gpsite_pep` | per-residue classification | 2 | accuracy | [GPSite](https://doi.org/10.7554/eLife.93695) (Yuan et al., 2024) | | `gpsite_pro` | per-residue classification | 2 | accuracy | [GPSite](https://doi.org/10.7554/eLife.93695) (Yuan et al., 2024) | | `gpsite_atp` | per-residue classification | 2 | accuracy | [GPSite](https://doi.org/10.7554/eLife.93695) (Yuan et al., 2024) | | `gpsite_hem` | per-residue classification | 2 | accuracy | [GPSite](https://doi.org/10.7554/eLife.93695) (Yuan et al., 2024) | | `gpsite_zn` | per-residue classification | 2 | accuracy | [GPSite](https://doi.org/10.7554/eLife.93695) (Yuan et al., 2024) | | `gpsite_ca` | per-residue classification | 2 | accuracy | [GPSite](https://doi.org/10.7554/eLife.93695) (Yuan et al., 2024) | | `gpsite_mg` | per-residue classification | 2 | accuracy | [GPSite](https://doi.org/10.7554/eLife.93695) (Yuan et al., 2024) | | `gpsite_mn` | per-residue classification | 2 | accuracy | [GPSite](https://doi.org/10.7554/eLife.93695) (Yuan et al., 2024) | ## Loading ```python from datasets import load_dataset # Load a single dataset (e.g. meltome) ds = load_dataset("Moomboh/mutafitup-datasets", "meltome") # Access splits train = ds["train"] valid = ds["validation"] test = ds["test"] ``` ## Resplit methodology The original datasets were collected from three independent sources (see provenance chain below). Because these sources share overlapping protein sequences, naively combining their original train/test splits introduces cross-dataset data leakage: a protein in one dataset's training set may appear in another dataset's test set. To eliminate this leakage, all sequences across all 16 datasets were pooled, deduplicated, and clustered using **mmseqs** at **20.0% minimum sequence identity**. New training, validation, and test splits were then assigned at the cluster level so that no two splits share sequence-similar proteins: 1. **Cluster** all unique sequences across datasets using mmseqs at 20.0% minimum sequence identity. 2. **Merge** original train + valid pools per dataset. 3. **Remove** within-dataset test-similar sequences from the pool. 4. **Reassign** cross-dataset test-contaminated cluster members to validation. 5. **Top up** validation to at least 10.0% of the non-test data (random seed: 42). **Shared protein groups:** (secstr, secstr8, rsa) -- datasets sharing the same underlying proteins are coordinated during split assignment. **Test set reconstruction:** `meltome` -- test sets are reconstructed from original test sequences with cluster-aware decontamination. **Duplicate aggregation:** `meltome` -- duplicate sequences within a split are aggregated (mean strategy for regression scores). The `metadata/` directory contains the full clustering and split assignment artifacts for reproducibility. ## Parquet schemas ### Per-residue classification Used by: `secstr`, `secstr8`, `gpsite_*` | Column | Type | Description | |--------|------|-------------| | `sequence` | `str` | Amino acid sequence | | `label` | `list[int]` | Per-residue integer class labels | | `resolved` | `list[int]` | Per-residue binary mask (1 = ordered, 0 = disordered) | ### Per-residue regression Used by: `disorder`, `rsa` | Column | Type | Description | |--------|------|-------------| | `sequence` | `str` | Amino acid sequence | | `score` | `list[float]` | Per-residue float scores (rsa uses 999.0 sentinel for unresolved residues) | ### Per-protein regression Used by: `meltome` | Column | Type | Description | |--------|------|-------------| | `sequence` | `str` | Amino acid sequence | | `score` | `float` | Scalar score (melting temperature in degrees Celsius) | ### Per-protein classification Used by: `subloc` | Column | Type | Description | |--------|------|-------------| | `sequence` | `str` | Amino acid sequence | | `label` | `str` | Class name (subcellular localization compartment) | | `label_numeric` | `int` | Integer-encoded label | ## Data provenance ### Schmirler 2024 (meltome, subloc, disorder) Training data from Schmirler, Heinzinger & Rost (2024), downloaded from the companion GitHub repository [RSchmirler/data-repo_plm-finetune-eval](https://github.com/RSchmirler/data-repo_plm-finetune-eval), also archived at [Zenodo](https://doi.org/10.5281/zenodo.12770310). Original train/valid/test splits were constructed by the authors using MMseqs2 clustering at >20% pairwise sequence identity. **Full provenance chains:** - **meltome**: Experimental thermal proteome profiling from the [Meltome Atlas](https://doi.org/10.1038/s41592-020-0801-4) (Jarzab et al., 2020) -- standardized into benchmark splits by [FLIP](https://doi.org/10.1101/2021.11.09.467890) (Dallago et al., 2021) -- adopted with added validation set by [Schmirler et al., 2024](https://doi.org/10.1038/s41467-024-51844-2). - **subloc**: Curated subcellular localization annotations from [Light Attention](https://doi.org/10.1093/bioadv/vbab035) (Starck et al., 2021) -- included directly by [Schmirler et al., 2024](https://doi.org/10.1038/s41467-024-51844-2). - **disorder**: NMR-derived per-residue disorder Z-scores from the [CheZOD database / ODiNPred](https://doi.org/10.1038/s41598-020-71716-1) (Dass et al., 2020) -- used for pLM embedding prediction by [SETH](https://doi.org/10.3389/fbinf.2022.1019597) (Ilzhofer et al., 2022) -- re-split by [Schmirler et al., 2024](https://doi.org/10.1038/s41467-024-51844-2). ### NetSurfP-2.0 (secstr, secstr8, rsa) Structure-derived annotations from [NetSurfP-2.0](https://doi.org/10.1002/prot.25674) (Klausen et al., 2019), hosted at DTU Health Tech. Secondary structure labels are DSSP-based categorical assignments; RSA targets are solvent-accessibility values for resolved residues. The secondary-structure test set (NEW364) comes from the [ProtTrans](https://doi.org/10.1109/TPAMI.2021.3095381) project (Elnaggar et al., 2022). The RSA test set is CB513. ### GPSite (gpsite_*) Residue-level binding-site annotations from [GPSite](https://doi.org/10.7554/eLife.93695) (Yuan et al., 2024), pinned to commit [`58cfa4e`](https://github.com/biomed-AI/GPSite/tree/58cfa4e59f077e531fb38cf4a04bec6aea706454). Labels correspond to experimentally resolved protein-ligand interactions from the PDB across 10 ligand types (DNA, RNA, peptide, protein, ATP, heme, Zn, Ca, Mg, Mn). ## References - Dallago, C. et al. (2021). FLIP: Benchmark tasks in fitness landscape inference for proteins. *bioRxiv* 2021.11.09.467890. [doi:10.1101/2021.11.09.467890](https://doi.org/10.1101/2021.11.09.467890) - Dass, R., Mulder, F.A.A. & Nielsen, J.T. (2020). ODiNPred: comprehensive prediction of protein order and disorder. *Sci. Rep.* 10, 14780. [doi:10.1038/s41598-020-71716-1](https://doi.org/10.1038/s41598-020-71716-1) - Elnaggar, A. et al. (2022). ProtTrans: Toward Understanding the Language of Life Through Self-Supervised Learning. *IEEE TPAMI* 44(10), 7112--7127. [doi:10.1109/TPAMI.2021.3095381](https://doi.org/10.1109/TPAMI.2021.3095381) - Ilzhofer, D., Heinzinger, M. & Rost, B. (2022). SETH predicts nuances of residue disorder from protein embeddings. *Front. Bioinform.* 2, 1019597. [doi:10.3389/fbinf.2022.1019597](https://doi.org/10.3389/fbinf.2022.1019597) - Jarzab, A. et al. (2020). Meltome atlas -- thermal proteome stability across the tree of life. *Nat. Methods* 17, 495--503. [doi:10.1038/s41592-020-0801-4](https://doi.org/10.1038/s41592-020-0801-4) - Klausen, M.S. et al. (2019). NetSurfP-2.0: Improved prediction of protein structural features by integrated deep learning. *Proteins* 87(6), 520--527. [doi:10.1002/prot.25674](https://doi.org/10.1002/prot.25674) - Schmirler, R., Heinzinger, M. & Rost, B. (2024). Fine-tuning protein language models boosts predictions across diverse tasks. *Nat. Commun.* 15, 7407. [doi:10.1038/s41467-024-51844-2](https://doi.org/10.1038/s41467-024-51844-2) - Starck, H. et al. (2021). Light attention predicts protein location from the language of life. *Bioinformatics Advances* 1(1), vbab035. [doi:10.1093/bioadv/vbab035](https://doi.org/10.1093/bioadv/vbab035) - Steinegger, M. & Soding, J. (2017). MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. *Nat. Biotechnol.* 35, 1026--1028. [doi:10.1038/nbt.3988](https://doi.org/10.1038/nbt.3988) - Yuan, Q., Tian, C. & Yang, Y. (2024). Genome-scale annotation of protein binding sites via language model and geometric deep learning. *eLife* 13, e93695. [doi:10.7554/eLife.93695](https://doi.org/10.7554/eLife.93695)