APIS: accurate prediction of hot spots in protein interfaces by combining protrusion index with solvent accessibility

Jun-Feng Xia, Xing-Ming Zhao, Jiangning Song, De-Shuang Huang

Research output: Contribution to journalArticleResearchpeer-review

146 Citations (Scopus)

Abstract

BACKGROUND: It is well known that most of the binding free energy of protein interaction is contributed by a few key hot spot residues. These residues are crucial for understanding the function of proteins and studying their interactions. Experimental hot spots detection methods such as alanine scanning mutagenesis are not applicable on a large scale since they are time consuming and expensive. Therefore, reliable and efficient computational methods for identifying hot spots are greatly desired and urgently required. RESULTS: In this work, we introduce an efficient approach that uses support vector machine (SVM) to predict hot spot residues in protein interfaces. We systematically investigate a wide variety of 62 features from a combination of protein sequence and structure information. Then, to remove redundant and irrelevant features and improve the prediction performance, feature selection is employed using the F-score method. Based on the selected features, nine individual-feature based predictors are developed to identify hot spots using SVMs. Furthermore, a new ensemble classifier, namely APIS (A combined model based on Protrusion Index and Solvent accessibility), is developed to further improve the prediction accuracy. The results on two benchmark datasets, ASEdb and BID, show that this proposed method yields significantly better prediction accuracy than those previously published in the literature. In addition, we also demonstrate the predictive power of our proposed method by modelling two protein complexes: the calmodulin/myosin light chain kinase complex and the heat shock locus gene products U and V complex, which indicate that our method can identify more hot spots in these two complexes compared with other state-of-the-art methods. CONCLUSIONS: We have developed an accurate prediction model for hot spot residues, given the ....
Original languageEnglish
Pages (from-to)1 - 14
Number of pages14
JournalBMC Bioinformatics
Volume11
Issue number174
DOIs
Publication statusPublished - 2010

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