QAUST: Protein Function Prediction Using Structure Similarity, Protein Interaction, and Functional Motifs

Fatima Zohra Smaili, Shuye Tian, Ambrish Roy, Meshari Alazmi, Stefan T. Arold, Srayanta Mukherjee, P. Scott Hefty, Wei Chen*, Xin Gao

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

Abstract

The number of available protein sequences in public databases is increasing exponentially. However, a significant percentage of these sequences lack functional annotation, which is essential for the understanding of how biological 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.

Original languageEnglish (US)
Pages (from-to)998-1011
Number of pages14
JournalGenomics, Proteomics and Bioinformatics
Volume19
Issue number6
DOIs
StatePublished - Dec 2021

Keywords

  • EC number
  • Functionally discriminative motif
  • GO term
  • Protein function prediction
  • Protein structure similarity

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Biology
  • Genetics
  • Computational Mathematics

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