QAUST: Protein Function Prediction Using Structure Similarity, Protein Interaction, and Functional Motifs
- Author:
Smaili Zohra FATIMA
1
;
Tian SHUYE
;
Roy AMBRISH
;
Alazmi MESHARI
;
T.Arold STEFAN
;
Mukherjee SRAYANTA
;
Hefty P.SCOTT
;
Chen WEI
;
Gao XIN
Author Information
1. Computational Bioscience Research Center(CBRC),Computer,Electrical and Mathematical Sciences and Engineering(CEMSE)Division,King Abdullah University of Science and Technology(KAUST),Thuwal 23955,Saudi Arabia
- Keywords:
Protein function prediction;
GO term;
EC number;
Protein structure similarity;
Functionally discriminative motif
- From:
Genomics, Proteomics & Bioinformatics
2021;19(6):998-1011
- CountryChina
- Language:Chinese
-
Abstract:
The number of available protein sequences in public databases is increasing exponentially. However, a sig-nificant percentage of these sequences lack functional annotation, which is essential for the understanding of how bio-logical systems operate. Here, we propose a novel method, Quantitative Annotation of Unknown STructure (QAUST), to infer protein functions, specifically Gene Ontology (GO) terms and Enzyme Commission (EC) numbers. QAUST uses three sources of information: structure information encoded by global and local structure similarity search, biological network information inferred by protein–protein interaction data, and sequence information extracted from functionally discriminative sequence motifs. These three pieces of information are combined by consensus averaging to make the final prediction. Our approach has been tested on 500 protein targets from the Critical Assessment of Functional Annotation (CAFA) benchmark set. The results show that our method provides accurate functional annotation and outperforms other prediction methods based on sequence similarity search or threading. We further demonstrate that a previously unknown function of human tripartite motif-containing 22 (TRIM22) protein predicted by QAUST can be experimentally validated.
