| --- |
| license: mit |
| tags: |
| - biology |
| --- |
| |
| # SwissIPG |
| > SwissProt-based Protein Function (InterPro and Gene Ontology) Benchmark Datasets |
|
|
| This repository provides benchmark datasets for protein function evaluation, constructed from **UniProt/SwissProt**, **InterPro (IPR)**, and **Gene Ontology (GO)**. |
| It includes both test sets and training sets, supporting **keyword-guided** and **description-guided** protein design/evaluation tasks. |
|
|
|
|
| ## 📖 Dataset Overview |
|
|
| **Keyword-guided protein tasks** previously lacked a **publicly available, high-quality evaluation dataset**. To address this gap, we curated a novel dataset from SwissProt, together with their **IPR entries** and **GO terms**. Note that the GO terms are restricted to **Molecular Function (MF)**. |
|
|
| | Subset | Num of Proteins | Num of IPR entries | Num of GO terms | |
| | ------------ | --------------- | ------------------ | --------------- | |
| | Test Set | 1,057 | 1,297 | 380 | |
| | Train Set | 555543 | 39431 | 8110 | |
|
|
| ## 🔧 Dataset Construction |
|
|
| 1. Protein selection |
| Extracted proteins from UniProt/SwissProt released between `2025-01-01` and `2025-08-25` for testing. The remaining proteins are used for training. |
|
|
| 2. Keyword collection |
| Retrieved corresponding protein sequences, InterPro IDs, and GO terms. |
|
|
| 3. Instruction construction for description-guided task |
| Following [Mol-Instructions](https://github.com/zjunlp/Mol-Instructions), we concatenated text descriptions of InterPro entries and GO terms to form natural-language prompts. |
|
|
| | Keyword | Description | |
| |---------------------------------------|-------------------------------------------------------------------------------------------------| |
| | InterPro (Domain) | The protein should contain one or more *{}* that are essential for its biological function | |
| | InterPro (Family) | The protein should belong to *{}* that shares evolutionary origin and functional similarity | |
| | InterPro (Homologous_Superfamily) | The protein should be classified within *{}* sharing conserved structural features | |
| | InterPro (Repeat) | The protein should include one or more *{}* that provide structural or functional support | |
| | InterPro (Conserved_Site) | The protein should contain *{}* that is preserved across related proteins | |
| | InterPro (Active_Site) | The protein must have *{}* that is conserved among related catalytic enzymes | |
| | InterPro (Binding_Site) | The protein should include a *{}* that enables ligand binding under diverse conditions | |
| | InterPro (PTM) | The protein should contain *{}* that allow regulation through chemical modifications | |
| | Gene Ontology (molecular function) | The protein must be able to perform the *{}* required for its activity. | |
|
|
| 4. Final curation |
| Curated into **test** and **train** sets with consistent formats. |
|
|
|
|
| ## 📂 Dataset Structure |
|
|
| | File | Func-Seq Pairs | Description | |
| | ------------------- | -------------- | -------------------------- | |
| | `go_test.json` | 693 | Test set for GO task. | |
| | `go_train.json` | 492124 | Train set for GO task. | |
| | `ipr_test.json` | 870 | Test set for IPR task. | |
| | `ipr_train.json` | 555543 | Train set for IPR task. | |
| | `ipr_go_test.json` | 674 | Test set for IPR&GO task. | |
| | `ipr_go_train.json` | 487848 | Train set for IPR&GO task. | |
| | `SwissIPG.py` | - | Building Class | |
|
|
| ## 🎯 Intended Use |
| - **Benchmarking protein design models** under keyword-guided and description-guided settings |
| - **Training and fine-tuning** models using consistent annotation standards |
| - Facilitating **fair comparison** across models through a unified dataset |
|
|
| ## 🔍 Evaluation Metrics |
|
|
| Our work, [PDFBench](https://github.com/PDFBench/PDFBench), provides a comprehensive evaluation for both tasks. For further details, please refer to the repository. |
|
|
| ## 📑 Citation |
|
|
| If you use this dataset, please cite: |
|
|
| ```bibtex |
| @misc{kuang2025pdfbenchbenchmarknovoprotein, |
| title={PDFBench: A Benchmark for De novo Protein Design from Function}, |
| author={Jiahao Kuang and Nuowei Liu and Changzhi Sun and Tao Ji and Yuanbin Wu}, |
| year={2025}, |
| eprint={2505.20346}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.LG}, |
| url={https://arxiv.org/abs/2505.20346}, |
| } |
| ``` |
