ProteinKG65

Introduction

ProteinKG65 is a large-scale KG dataset with aligned descriptions and protein sequences respectively to GO term and proteins entities. It contains about 614,099 entities, 5,620,437 triples (including 5,510,437 protein-go triplets and 110,000 Go-Go triplets). We provide both the inductive and the transductive settings used in the original paper.

Setting		#protein entity	#go entity	#relation	#triplet
	train	543,110	28,524	57	4,884,034
Transductive	valid	25,241	5,009	44	51,243
	test	217,463	17,908	57	575,160
	train	543,110	28,524	57	4,884,034
Induvtive	valid	855	270	31	2,216
	test	3,085	1,062	50	11,127

We get Go term information from Gene Ontology. Gene Ontology is a major bioinformatics initiative to unify the representation of gene and gene product attributes across all species, which consists of a set of Go terms(or concepts) with relations that operate between them. The vocabulary of genes and gene products involved in Gene Ontology is divided into three categories, covering three aspects of biology:

Molecular Function
Cellular Component
Biological Process

Go term

All entities in Gene Ontology belong to BPO, MFO and CCO. The relationship between Go term and Protein is mainly concentrated in the following tree types:

located in (1,111,771)
enables (1,946,834)
involved in (1,720,804)

To mitigate this severe long-tail effect, we refine some of these relationships to keep the data balanced. The total number of relationships after refinement is 65, compared with 25 before.

Data

Download ProteinKG65

Zenodo

Details

ProteinKG65 follows Gene Ontology and Gene Annotations released in April 2020. Each Entity (Go term entity or Protein entity) is identified by a unique ID.

Protein-Go triplet example:

71090(Q14028) 36(enables_nucleotide_binding) 117(GO:0000166)

GO-Go triplet example:

0(GO:0000001) 0(is_a) 23558(GO:0048308)

Protein Sequence example:

P0DQM8: QKCCSGGSCPLYFRDRLICPCC

Publication

Multi-modal Protein Knowledge Graph Construction and Applications
Siyuan Cheng, Xiaozhuan Liang, Zhen Bi, Huajun Chen, Ningyu Zhang,
arXiv

Cite

@article{
cheng2022proteinkg65,
title={Multi-modal Protein Knowledge Graph Construction and Applications},
author={Cheng, Siyuan and Liang, Xiaozhuan and Bi, Zhen and Chen, Huajun and Zhang, Ningyu},
journal={arXiv preprint arXiv:2207.10080},
year={2022}
}