AlloPred: prediction of allosteric pockets on proteins using normal mode perturbation analysis

BMC Bioinformatics

Methodology Article

Michael JE Sternberg¹  Joe G Greener¹ 

[1] Centre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London, SW7 2AZ, London, UK;

关键词: Allostery;    Normal modes;    Pocket prediction;    Machine learning;    Web server;   

DOI  :  10.1186/s12859-015-0771-1

 received in 2015-06-20, accepted in 2015-10-13,  发布年份 2015

来源: Springer

【 摘 要 】

BackgroundDespite being hugely important in biological processes, allostery is poorly understood and no universal mechanism has been discovered. Allosteric drugs are a largely unexplored prospect with many potential advantages over orthosteric drugs. Computational methods to predict allosteric sites on proteins are needed to aid the discovery of allosteric drugs, as well as to advance our fundamental understanding of allostery.ResultsAlloPred, a novel method to predict allosteric pockets on proteins, was developed. AlloPred uses perturbation of normal modes alongside pocket descriptors in a machine learning approach that ranks the pockets on a protein. AlloPred ranked an allosteric pocket top for 23 out of 40 known allosteric proteins, showing comparable and complementary performance to two existing methods. In 28 of 40 cases an allosteric pocket was ranked first or second. The AlloPred web server, freely available at http://www.sbg.bio.ic.ac.uk/allopred/home, allows visualisation and analysis of predictions. The source code and dataset information are also available from this site.ConclusionsPerturbation of normal modes can enhance our ability to predict allosteric sites on proteins. Computational methods such as AlloPred assist drug discovery efforts by suggesting sites on proteins for further experimental study.

【 授权许可】

CC BY   
© Greener and Sternberg. 2015