README.md

metadata

language:
  - en
tags:
  - protein
  - bioinformatics
  - protein sequence
  - protein function
  - enzyme
  - uniprot
  - sequence-to-text
  - protein annotation
  - biological process
  - molecular function
  - cellular component
configs:
  - config_name: default
    description: Protein sequences with functional descriptions and annotations.
    data_files:
      - split: train
        path: NL2Protein.csv
    features:
      - name: ID
        dtype: string
      - name: Text
        dtype: string
      - name: Protein
        dtype: string
      - name: Species
        dtype: string
      - name: Family
        dtype: string
    viewer:
      type: table
      rows_per_page: 10
size_categories:
  - 100K<n<1M

NL2Protein Dataset

I. Dataset Introduction

This dataset provides aligned pairs of natural language descriptions and protein sequences, where the descriptions integrate protein family information, length constraints, and Gene Ontology–based functional relations. The dataset is intended for studying natural language–guided protein sequence generation and the semantic alignment between textual protein descriptions and sequence space.

The data in this dataset are derived from the UniProt database. Protein sequences, organism information, protein family annotations, and associated GO terms were downloaded from UniProt and subsequently integrated, filtered, and reformatted into a unified text–sequence paired dataset for research use.

Each entry includes:

• A protein identifier
• A structured functional description
• A full-length amino acid sequence
• The source organism (species)
• A protein family annotation

The functional description contains:

• Protein family
• Protein length range and size category
• Biological processes
• Molecular functions
• Cellular components
• Parent–child (is_a) relationships between GO terms
• Cross-ontology relationships, such as:
  • part_of
  • occurs_in
  • regulates / positively_regulates / negatively_regulates

This dataset is designed for natural language–to–protein sequence generation. Given a natural language description that specifies functional, and biological constraints—such as protein family, length range, Gene Ontology (GO) terms from the Biological Process, Molecular Function, and Cellular Component namespaces, as well as their parent–child and cross-ontology relationships—the task is to directly generate a full-length amino acid sequence that is consistent with the provided description. The dataset enables training and evaluation of protein language models on text-conditioned sequence generation, supporting research in controllable protein design and semantic alignment between natural language descriptions and protein sequence space.

Ⅱ. Data Structure

Each sample consists of the following fields:

• ID: Protein identifier (UniProt-style accession)
• protein: Amino acid sequence
• Text: Functional description text
• species: Source organism of the protein (UniProt Organism field)
• family: Protein family annotation derived from UniProt

ID	Text	Protein	Species	Family
A0A017SPL2	Belongs to the Tryptophan dimethylallyltransferase family. The protein length falls within the range of 301–500 amino acids, belonging to the category of Large proteins. It participates in biological processes such as alkaloid metabolic process. It exhibits molecular functions including prenyltransferase activity.	MQPYHTLSRVLPFPDANQKAWWDKLGPMLLKAMQS QGYDTEAQYAQLGMVYKCVLPYLGEFPTVENDATRWK SFLCPYGIPIEPSLNISQGILRYAFEPIGPDVGTEKDPQN MN	Accumulibacter regalis	Tryptophan dimethylallyltransferase family

Ⅲ. Dataset Loading

from datasets import load_dataset
dataset = load_dataset(
    "csv",
    data_files="NL2Protein.csv"
)
print(dataset)
print(dataset["train"][0])

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