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  The Beijing Academy of Artificial Intelligence (hereinafter referred to as
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license: cc-by-nc-4.0
language:
  - en
tags:
  - biology
  - protein
  - instruction dataset
  - instruction tuning
pretty_name: Open Protein Instructions(OPI)
size_categories:
  - 1M<n<10M
task_categories:
  - text-generation

Dataset Card for Open Protein Instructions (OPI)

Dataset Update

The previous version of OPI dataset is based on the release 2022_01 of UniProtKB/Swiss-Prot protein knowledgebase. At current, OPI is updated to contain the latest release 2023_05, which can be accessed via the dataset file OPI_updated_160k.json.

Reference:

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 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.

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 and Privacy Notice of UniProt.