license: apache-2.0
tags:
- ppi
- proteins
- biology
- bacteria
- stringdb
pretty_name: Dataset for predicting protein-protein interactions in bacterial genomes
size_categories:
- 10K<n<100K
Dataset for protein-protein interaction prediction across bacteria (Protein sequences)
A dataset of 10,533 bacterial genomes across 6,956 species with protein-protein interaction (PPI) scores for each genome.
The genome protein sequences and PPI scores have been extracted from STRING DB.
Each row contains a set of protein sequences from a genome, ordered by their location on the chromosome and plasmids and a set of associated PPI scores.
The PPI scores have been extracted using the combined score from STRING DB.
The interaction between two proteins is represented by a triple: [prot1_index, prot2_index, score]. Where to get a probability score, you must divide the score by 1000
(i.e. if the score is 721 then to get a true score do 721/1000=0.721). The index of a protein refers to the index of the protein in the protein_sequences column of the
row. See example below in Usage
Usage
We recommend loading the dataset in a streaming mode to prevent memory errors.
from datasets import load_dataset
ds = load_dataset("macwiatrak/bacbench-ppi-stringdb-protein-sequences", split="validation", streaming=True)
item = next(iter(ds))
# fetch protein sequences from a genome (list of strings)
prot_seqs = item["protein_sequences"]
# fetch PPI triples labels (i.e. [prot1_index, prot2_index, score])
ppi_triples = item["triples_combined_score"]
# get protein seqs and label for one pair of proteins
prot1 = prot_seqs[ppi_triples[0][0]]
prot2 = prot_seqs[ppi_triples[0][1]]
score = ppi_triples[0][2] / 1000
# we recommend binarizing the labels based on the threshold of 0.6
binary_ppi_triples = [
(prot1_index, prot2_index, int((score / 1000) >= 0.6)) for prot1_index, prot2_index, score in ppi_triples
]
Split
We provide train, validation and test splits with proportions of 70 / 10 / 20 (%) respectively as part of the dataset. The split was performed randomly at genome level.
See github repository for details on how to embed the dataset with DNA and protein language models as well as code to predict antibiotic resistance from sequence.