Datasets:
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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.
|