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Prediction of small molecule binding property of protein domains with Bayesian classifiers based on Markov chains

Paper ID Volume ID Publish Year Pages File Format Full-Text
15275 1399 2009 4 PDF Available
Title
Prediction of small molecule binding property of protein domains with Bayesian classifiers based on Markov chains
Abstract

Accurate computational methods that can help to predict biological function of a protein from its sequence are of great interest to research biologists and pharmaceutical companies. One approach to assume the function of proteins is to predict the interactions between proteins and other molecules. In this work, we propose a machine learning method that uses a primary sequence of a domain to predict its propensity for interaction with small molecules. By curating the Pfam database with respect to the small molecule binding ability of its component domains, we have constructed a dataset of small molecule binding and non-binding domains. This dataset was then used as training set to learn a Bayesian classifier, which should distinguish members of each class. The domain sequences of both classes are modelled with Markov chains. In a Jack-knife test, our classification procedure achieved the predictive accuracies of 77.2% and 66.7% for binding and non-binding classes respectively. We demonstrate the applicability of our classifier by using it to identify previously unknown small molecule binding domains. Our predictions are available as supplementary material and can provide very useful information to drug discovery specialists. Given the ubiquitous and essential role small molecules play in biological processes, our method is important for identifying pharmaceutically relevant components of complete proteomes. The software is available from the author upon request.

Keywords
Function prediction; Proteomics; Small molecule binding domains; Drug discovery; Bayesian classifiers; Markov chains
First Page Preview
Prediction of small molecule binding property of protein domains with Bayesian classifiers based on Markov chains
Publisher
Database: Elsevier - ScienceDirect
Journal: Computational Biology and Chemistry - Volume 33, Issue 6, December 2009, Pages 457–460
Authors
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Subjects
Physical Sciences and Engineering Chemical Engineering Bioengineering