Protein domain recurrence and order can enhance prediction of protein functions

Mario A. Abdel Messih, Meghana Chitale, Vladimir B. Bajic, Daisuke Kihara, Xin Gao

Research output: Contribution to journalArticlepeer-review

22 Scopus citations


Motivation: Burgeoning sequencing technologies have generated massive amounts of genomic and proteomic data. Annotating the functions of proteins identified in this data has become a big and crucial problem. Various computational methods have been developed to infer the protein functions based on either the sequences or domains of proteins. The existing methods, however, ignore the recurrence and the order of the protein domains in this function inference. Results: We developed two new methods to infer protein functions based on protein domain recurrence and domain order. Our first method, DRDO, calculates the posterior probability of the Gene Ontology terms based on domain recurrence and domain order information, whereas our second method, DRDO-NB, relies on the nave Bayes methodology using the same domain architecture information. Our large-scale benchmark comparisons show strong improvements in the accuracy of the protein function inference achieved by our new methods, demonstrating that domain recurrence and order can provide important information for inference of protein functions. The Author(s) 2012. Published by Oxford University Press.
Original languageEnglish (US)
Pages (from-to)i444-i450
Number of pages1
Issue number18
StatePublished - Sep 3 2012

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01

ASJC Scopus subject areas

  • Biochemistry
  • Computational Theory and Mathematics
  • Computational Mathematics
  • Molecular Biology
  • Statistics and Probability
  • Computer Science Applications


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