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language:
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pretty_name: OPI
size_categories:
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---

![image.png](./OPI_logo.png)
# Dataset Card for Open Protein Instructions (OPI)

## Dataset Description

- **Homepage:** 
- **Repository:** 
- **Paper:** 
- **Leaderboard:** 
- **Point of Contact:** 

### Dataset Summary

Open Protein Instructions(OPI) is the initial part of Open Biology Instructions(OBI) project, together with the subsequent Open Molecule Instructions(OMI), Open DNA Instructions(ODI), Open RNA Instructions(ORI) and Open Single-cell Instructions (OSCI). OBI is a project which aims to fully leverage the potential ability of Large Language Models(LLMs), especially the scientific LLMs like Galactica, to facilitate research in AI for Life Science community. While OBI is still in an early stage, we hope to provide a starting point for the community to bridge LLMs and biological domain knowledge.

## Dataset Structure

### Data Instances

```
instruction: 
    What is the EC classification of the input protein sequence based on its biological function?
input:                         
    MGLVSSKKPDKEKPIKEKDKGQWSPLKVSAQDKDAPPLPPLVVFNHLTPPPPDEHLDEDKHFVVALYDYTAMNDRDLQMLKGEKLQVLKGTGDWWLARS
    LVTGREGYVPSNFVARVESLEMERWFFRSQGRKEAERQLLAPINKAGSFLIRESETNKGAFSLSVKDVTTQGELIKHYKIRCLDEGGYYISPRITFPSL
    QALVQHYSKKGDGLCQRLTLPCVRPAPQNPWAQDEWEIPRQSLRLVRKLGSGQFGEVWMGYYKNNMKVAIKTLKEGTMSPEAFLGEANVMKALQHERLV
    RLYAVVTKEPIYIVTEYMARGCLLDFLKTDEGSRLSLPRLIDMSAQIAEGMAYIERMNSIHRDLRAANILVSEALCCKIADFGLARIIDSEYTAQEGAK
    FPIKWTAPEAIHFGVFTIKADVWSFGVLLMEVVTYGRVPYPGMSNPEVIRNLERGYRMPRPDTCPPELYRGVIAECWRSRPEERPTFEFLQSVLEDFYT
    ATERQYELQP
output: 
    2.7.10.2
```

### Data Splits
The OPI dataset folder structure is as follows:
```
./OPI_DATA/
├── AP
│   ├── Function
│   │   ├── test
│   │   │   ├── CASPSimilarSeq_function_test.jsonl
│   │   │   ├── IDFilterSeq_function_test.jsonl
│   │   │   └── UniProtSeq_function_test.jsonl
│   │   └── train
│   │       ├── function_description_train.json
│   │       └── function_description_train_0.01.json
│   ├── GO
│   │   ├── test
│   │   │   ├── CASPSimilarSeq_go_test.jsonl
│   │   │   ├── IDFilterSeq_go_test.jsonl
│   │   │   └── UniProtSeq_go_test.jsonl
│   │   └── train
│   │       ├── go_terms_train.json
│   │       └── go_terms_train_0.01.json
│   └── Keywords
│       ├── test
│       │   ├── CASPSimilarSeq_keywords_test.jsonl
│       │   ├── IDFilterSeq_keywords_test.jsonl
│       │   └── UniProtSeq_keywords_test.jsonl
│       └── train
│           ├── keywords_train.json
│           └── keywords_train_0.01.json
├── KM
│   ├── gSymbol2Cancer
│   │   ├── test
│   │   │   └── gene_symbol_to_cancer_test.jsonl
│   │   └── train
│   │       └── gene_symbol_to_cancer_train.json
│   ├── gName2Cancer
│   │   ├── test
│   │   │   └── gene_name_to_cancer_test.jsonl
│   │   └── train
│   │       └── gene_name_to_cancer_train.json
│   └── gSymbol2Tissue
│       ├── test
│       │   └── gene_symbol_to_tissue_test.jsonl
│       └── train
│           └── gene_symbol_to_tissue_train.json
└── SU
    ├── EC_number
    │   ├── test
    │   │   ├── CLEAN_EC_number_new_test.jsonl
    │   │   └── CLEAN_EC_number_price_test.jsonl
    │   └── train
    │       ├── CLEAN_EC_number_train.json
    ├── Fold_type-Remote
    │   ├── test
    │   │   └── Remote_test.jsonl
    │   └── train
    │       └── Remote_train.json
    └── Subcellular_location
        ├── test
        │   ├── location_test.jsonl
        └── train
            └── location_train.json
```

## Dataset Creation

The OPI dataset is curated on our own by extracting key information from [Swiss-Prot](https://www.uniprot.org/uniprotkb?facets=reviewed%3Atrue&query=%2A) database. The detailed construction pipeline is depicted in the supplementary material of our manuscript which has been submitted to NeurIPS 2023 Datasets and Benchmarks. The following figure shows the general construction process.
![image.png](./OPI_data.png)

## License
The dataset is licensed under a Creative Commons Attribution Non Commercial 4.0 License. The use of this dataset should also abide by the original [License & Disclaimer](https://www.uniprot.org/help/license) and [Privacy Notice](https://www.uniprot.org/help/privacy) of UniProt.