| | --- |
| | license: mit |
| | task_categories: |
| | - token-classification |
| | language: |
| | - en |
| | tags: |
| | - biology |
| | pretty_name: Protap |
| | configs: |
| | - config_name: AFP |
| | data_files: |
| | - split: test |
| | path: |
| | - "AFP/nrPDB-GO_test.csv" |
| |
|
| | - config_name: PCSP |
| | data_files: |
| | - split: train |
| | path: |
| | - "PCSP/train_C14005.pkl" |
| | - "PCSP/train_M10003.pkl" |
| | - split: test |
| | path: |
| | - "PCSP/test_C14005.pkl" |
| | - "PCSP/test_M10003.pkl" |
| |
|
| | - config_name: PLI_DAVIS |
| | data_files: |
| | - split: test |
| | path: |
| | - "PLI_DAVIS/davis_drug_pdb_data.txt" |
| | - "PLI_DAVIS/pli_structure.json" |
| | - "PLI_DAVIS/data/*" |
| |
|
| | - config_name: PROTACs |
| | data_files: |
| | - split: test |
| | path: |
| | - "PROTACs/PROTAC_clean_structure_label.txt" |
| | - "PROTACs/protac_poi_e3ligase_structure.json" |
| | - "PROTACs/e3_ligand/*" |
| | - "PROTACs/linker/*" |
| | - "PROTACs/warhead/*" |
| |
|
| | size_categories: |
| | - 100K<n<1M |
| | --- |
| | # Protap |
| | - [Protap](#protap) |
| | - [Overview](#overview) |
| | - [Installation](#installation) |
| | - [Dataset Description](#dataset-description) |
| | - [Citation](#citation) |
| | - [Contact](#contact) |
| |
|
| | ## Overview |
| |
|
| |
|
| | **Protap** is a benchmark dataset for evaluating protein modeling algorithms in five biologically realistic downstream applications. It enables comparative evaluation of both pre-trained models and domain-specific architectures. Protap includes both sequence and structural data, with tasks ranging from protein function prediction to targeted protein degradation. |
| |
|
| |  |
| | (I) Masked Language Modeling(MLM) is a self-supervised objective designed to recover masked residues in protein sequences; |
| | (II) Multi-View Contrastive Learning(MVCL) leverages protein structural information by aligning representations of biologically correlated substructures. |
| | (III) Protein Family Prediction(PFP) introduces functional and structural supervision by training models to predict family labels based on protein sequences and 3D structures. |
| |
|
| | Protap was introduced in the paper |
| | [**Protap: A Benchmark for Protein Modeling on Realistic Downstream Applications**](https://arxiv.org/abs/2506.02052) |
| | by Yan et al., arXiv 2025. |
| | ## Installation |
| |
|
| | ```bash |
| | git clone https://github.com/Trust-App-AI-Lab/protap.git |
| | cd protap |
| | ``` |
| | ## Dataset-Description |
| | ## 📦 Configurations |
| |
|
| | | Config Name | Task Description | Modality | Split | File Types | |
| | |-------------|--------------------------------------------------------|----------------|--------------|-------------------------------| |
| | | `AFP` | Protein function annotation (GO term prediction) | Seq + Struct | `test` | `.csv`,`json` | |
| | | `PCSP` | Cleavage site prediction (enzyme-substrate pairs) | Seq + Struct | `train/test` | `.pkl` | |
| | | `PLI_DAVIS` | Protein–ligand binding affinity regression | Seq + Struct | `test` | `.txt`, `.json`, `.sdf` | |
| | | `PROTACs` | Ternary complex prediction in targeted degradation | Seq + Struct | `test` | `.txt`, `.json`, `.sdf` | |
| |
|
| |
|
| | --- |
| |
|
| | ## 💡 Task Descriptions |
| |
|
| | ### 🔹 Enzyme-Catalyzed Protein Cleavage Site Prediction (`PCSP`) |
| |
|
| | - **Description**: Predict residue-level cleavage sites under the catalysis of enzymes. |
| | - **Input**: A protein substrate and an enzyme, both represented by sequences and 3D structures. |
| | - **Output**: A binary vector indicating whether each residue is a cleavage site. |
| | - **Files**: |
| | - Train: `PCSP/train_C14005.pkl`, `PCSP/train_M10003.pkl` |
| | - Test: `PCSP/test_C14005.pkl`, `PCSP/test_M10003.pkl` |
| | - **Format**: |
| | - **Input Format**: |
| | Python pickle file (`.pkl`) with a list of samples. Each sample is a dictionary with keys like: |
| | - `enzyme_seq`: amino acid string |
| | - `enzyme_coords`: array of 3D coordinates |
| | - `substrate_seq`: amino acid string |
| | - `substrate_coords`: array of 3D coordinates |
| | - Structural data of proteins: |
| | ```json |
| | { |
| | "Q5QJ38": { |
| | "name": "Q5QJ38", |
| | "seq": "MPQLLRNVLCVIETFHKYASEDSNGAT...", |
| | "coords": [[[-0.432, 25.507, -8.242], ...], ...], |
| | "cleave_site": [136] |
| | } |
| | } |
| | ``` |
| | -Sequence and structure of substrate proteins |
| | ```json |
| | { |
| | "P31001_MER0000622": [110, 263], |
| | "000232_MER0000622": [276, 334, 19], |
| | ... |
| | } |
| | ``` |
| | - **Label Format**: |
| | `cleavage_sites`: list of 0/1 values (length = number of substrate residues) |
| | - **Metric**: AUC, AUPR |
| | |
| | --- |
| |
|
| | ### 🔹 Targeted Protein Degradation by Proteolysis-Targeting Chimeras (`PROTACs`) |
| |
|
| | - **Description**: Predict whether a given PROTAC, E3 ligase, and target protein form a functional ternary complex for degradation. |
| | - **Input**: |
| | - PROTAC molecule (warhead, linker, E3 ligand), target protein, and E3 ligase |
| | - **Output**: Binary label (1: degradation occurs, 0: no degradation) |
| | - **Files**: |
| | - `PROTACs/PROTAC_clean_structure_label.txt` |
| | - `PROTACs/protac_poi_e3ligase_structure.json` |
| | - Subfolders: `PROTACs/e3_ligand/`, `linker/`, `warhead/` |
| | - **Format**: |
| | - **Input Format**: |
| | - `*.json`: Contains structural coordinates of proteins |
| | - `*.sdf` or `.mol` under folders: 3D conformers of molecules (SMILES-based) |
| | - `PROTAC_clean_structure_label.txt`: sample list with molecule paths and label |
| | ```json |
| | [ |
| | "Q8IWV7": { |
| | "seq": "MADEEAGGTERMEISAELPQTPQRLASWWDQQVDFYTA...", |
| | "coord": [[21.9960, 68.3170, -49.9029], ...] |
| | }, |
| | ..... |
| | ] |
| | ``` |
| | |
| | ```bash |
| | uniprot e3_ligase_structure linker_sdf warhead_sdf e3_ligand_sdf label |
| | Q00987 Q96SW2 linker_2.sdf warhead_7.sdf e3_ligand_7.sdf 1 |
| | Q00987 Q96SW2 linker_2.sdf warhead_7.sdf e3_ligand_16.sdf 1 |
| | P10275 P40337 linker_13.sdf warhead_27.sdf e3_ligand_27.sdf 0 |
| | P10275 P40337 linker_33.sdf warhead_27.sdf e3_ligand_27.sdf 0 |
| | ``` |
| | - **Label Format**: |
| | Binary label (0 or 1) in final column of `*_label.txt` |
| | - **Metric**: Accuracy, AUC |
| | |
| | --- |
| |
|
| | ### 🔹 Protein–Ligand Interactions (`PLI_DAVIS`) |
| | |
| | - **Description**: Predict the binding affinity between a protein and a small molecule ligand. |
| | - **Input**: Protein and ligand 3D structures |
| | - **Output**: Real-valued regression target representing binding affinity |
| | - **Files**: |
| | - `PLI_DAVIS/davis_drug_pdb_data.txt` |
| | - `PLI_DAVIS/pli_structure.json` |
| | - `PLI_DAVIS/data/`: contains structure files for small molecules |
| | - **Format**: |
| | - **Input Format**: |
| | - `pli_structure.json`: Dictionary of protein structures with residue positions |
| | - `davis_drug_pdb_data.txt`: Tab-separated file with fields: `drug`, `protein`, `y_true` |
| | - `data/`: ligand structures (`.sdf`, `.pdbqt` or similar) |
| | ```json |
| | [ |
| | "4WSQ.B": { |
| | "uniprot_id": "Q2M2I8", |
| | "seq": "EVLAEGGFAIVFLCALKRMVCKREIQIMRDLS...", |
| | "coord": [[[6.6065, 16.2524, 52.3289], ...], ...] |
| | } |
| | ] |
| | ``` |
| |
|
| | ```bash |
| | drug protein Kd y protein_pdb |
| | 5291 AAK1 10000.0 5.0 4WSQ.B |
| | 5291 ABL1p 10000.0 5.0 3QRJ.B |
| | 5291 ABL2 10.0 7.99568 2XYN.C |
| | ``` |
| | - **Label Format**: |
| | Real-valued log-transformed binding affinity (e.g., pKd or −log(Kd)) |
| | - **Metric**: Mean Squared Error (MSE), Pearson Correlation |
| | |
| | --- |
| |
|
| | ### 🔹 Protein Function Annotation Prediction (`AFP`) |
| |
|
| | - **Description**: Predict GO (Gene Ontology) terms for proteins. |
| | - **Input**: Protein sequence and structure |
| | - **Output**: Multi-label vector representing associated GO terms |
| | - **Files**: |
| | - `AFP/processed_data.json` |
| | - `AFP/data_splits.json` |
| | - `AFP/nrPDB-GO_annot.tsv` |
| | - `AFP/nrPDB-GO_test.csv` |
| | - **Format**: |
| | - **Input Format**: |
| | ```AFP/processed_data.json```: json file with key value pair: |
| | - `name`: unique ID |
| | - `seq`: amino acid sequence |
| | - `coords`: protein structure information |
| | - `molecular_function`,`biological_process`,`cellular_component`: GO term labels |
| | ```json |
| | - [ |
| | { |
| | "name": "2P1Z-A", |
| | "seq": "SKKAELAELVKELAVYVDLRRATLHARASRLIGELLRELTADWDYVA...", |
| | "coords": [[ [6.4359, 51.3870, 15.4490], ... ], ...], |
| | "molecular_function": [0, 0, ..., 1, ...], |
| | "biological_process": [0, 0, ..., 0, ...], |
| | "cellular_component": [0, 0, ..., 1, ...] |
| | }, |
| | ... |
| | ] |
| | ``` |
| | ```AFP/data_splits.json```: The division of the train and test datasets |
| | - **annotation file Format**: |
| | ```AFP/nrPDB-GO_annot.tsv```：tsv file with multi-column contains the detailed GO term information for all proteins. |
| | ``` |
| | ### PDB-chain GO-terms (molecular_function) GO-terms (biological_process) GO-terms (cellular_component) |
| | 1A6X-A GO:0016874 GO:0016053, ... , GO:0032787 GO:1902494 |
| | ``` |
| | ```AFP/nrPDB-GO_test.csv```: csv file with multi-column contains the sequence similarity between the test proteins and the training set. |
| | ``` |
| | PDB-chain,<30%,<40%,<50%,<70%,<95% |
| | 3ONG-B , 0 , 1 , 1 , 1 , 1 |
| | 1AD3-A , 0 , 0 , 1 , 1 , 1 |
| | 1FI8-A , 0 , 0 , 0 , 1 , 1 |
| | 2FWM-X , 0 , 1 , 1 , 1 , 1 |
| | ``` |
| | - **Label Format**: |
| | One or more GO term IDs (e.g., `GO:0007165`) in a multi-hot encoded label vector, The detailed GO term label information for each protein can be found in the **AFP/nrPDB-GO\_annot.tsv** file. |
| | - **Metric**: Fmax, AUPR |
| | |
| |
|
| |
|
| | ## Citation |
| | If you use this dataset, please cite: |
| |
|
| | ```bibtex |
| | @misc{yan2025protapbenchmarkproteinmodeling, |
| | title={Protap: A Benchmark for Protein Modeling on Realistic Downstream Applications}, |
| | author={Shuo Yan and Yuliang Yan and Bin Ma and Chenao Li and Haochun Tang and Jiahua Lu and Minhua Lin and Yuyuan Feng and Hui Xiong and Enyan Dai}, |
| | year={2025}, |
| | eprint={2506.02052}, |
| | archivePrefix={arXiv}, |
| | primaryClass={q-bio.BM}, |
| | url={https://arxiv.org/abs/2506.02052}, |
| | } |
| | ``` |
| | ## Contact |
| | Please submit GitHub issues or contact Shuo Yan ([syan225@connect.hkust-gz.edu.cn](mailto:syan225@connect.hkust-gz.edu.cn) |
| | ) for any questions related to the source code. |
| |
|
| |
|
