Update README.md

README.md

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+---
+# 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.
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6714bad02d5d8af7a0aabeb5/WH2f4gFfV8qv-SGPU-D0e.png)
+(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 Shuo 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)      | Sequence-only  | `test`       | `.csv`                        |
+| `PCSP`      | Cleavage site prediction (enzyme-substrate pairs)     | Seq + Struct   | `train/test` | `.pkl`                        |
+| `PLI_DAVIS` | Protein–ligand binding affinity regression            | Seq + Struct   | `test`       | `.txt`, `.json`, folder       |
+| `PROTACs`   | Ternary complex prediction in targeted degradation    | Seq + Struct   | `test`       | `.txt`, `.json`, folder       |
+
+
+---
+
+## 💡 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
+- **Output**: Multi-label vector representing associated GO terms
+- **Files**:
+  - `AFP/nrPDB-GO_test.csv`
+- **Format**:
+  - **Input Format**:
+    CSV file with columns:
+    - `sequence_id`: unique ID
+    - `sequence`: amino acid string
+    - [
+    ```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, ...]
+      },
+      ...
+    ]
+    ```
+  - **Label Format**:
+    One or more GO term IDs (e.g., `GO:0007165`) in a multi-hot encoded label vector
+- **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 (shuoyan[at]hkust-gz[dot]edu[dot]cn) for any questions related to the source code.
+