Bioinformatic approaches for predicting substrates of proteases

Jiangning Song, Hao Tan, Sarah Elizabeth Boyd, Hongbin Shen, Khalid Mahmood, Geoffrey Webb, Tatsuya Akutsu, James Whisstock, Robert Pike

Research output: Contribution to journalArticleResearchpeer-review

23 Citations (Scopus)

Abstract

Proteases have central roles in life and death processes due to their important ability to catalytically hydrolyze protein substrates, usually altering the function and/or activity of the target in the process. Knowledge of the substrate specificity of a protease should, in theory, dramatically improve the ability to predict target protein substrates. However, experimental identification and characterization of protease substrates is often difficult and time-consuming. Thus solving the substrate identification problem is fundamental to both understanding protease biology and the development of therapeutics that target specific protease-regulated pathways. In this context, bioinformatic prediction of protease substrates may provide useful and experimentally testable information about novel potential cleavage sites in candidate substrates. In this article, we provide an overview of recent advances in developing bioinformatic approaches for predicting protease substrate cleavage sites and identifying novel putative substrates. We discuss the advantages and drawbacks of the current methods and detail how more accurate models can be built by deriving multiple sequence and structural features of substrates. We also provide some suggestions about how future studies might further improve the accuracy of protease substrate specificity prediction.
Original languageEnglish
Pages (from-to)149 - 178
Number of pages30
JournalJournal of Bioinformatics and Computational Biology
Volume9
Issue number1
DOIs
Publication statusPublished - 2011

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