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Open Access Research article

Integrating water exclusion theory into βcontacts to predict binding free energy changes and binding hot spots

Qian Liu12, Steven CH Hoi2, Chee Keong Kwoh2, Limsoon Wong3 and Jinyan Li1*

Author Affiliations

1 Advanced Analytics Institute and Center for Health Technologies, Faculty of Engineering and IT, University of Technology, Sydney, Australia

2 School of Computer Engineering, Nanyang Technological University, Singapore 639798, Singapore

3 School of Computing, National University of Singapore, Singapore 117417, Singapore

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BMC Bioinformatics 2014, 15:57  doi:10.1186/1471-2105-15-57

Published: 26 February 2014

Abstract

Background

Binding free energy and binding hot spots at protein-protein interfaces are two important research areas for understanding protein interactions. Computational methods have been developed previously for accurate prediction of binding free energy change upon mutation for interfacial residues. However, a large number of interrupted and unimportant atomic contacts are used in the training phase which caused accuracy loss.

Results

This work proposes a new method, βACVASA, to predict the change of binding free energy after alanine mutations. βACVASA integrates accessible surface area (ASA) and our newly defined β contacts together into an atomic contact vector (ACV). A β contact between two atoms is a direct contact without being interrupted by any other atom between them. A β contact’s potential contribution to protein binding is also supposed to be inversely proportional to its ASA to follow the water exclusion hypothesis of binding hot spots. Tested on a dataset of 396 alanine mutations, our method is found to be superior in classification performance to many other methods, including Robetta, FoldX, HotPOINT, an ACV method of β contacts without ASA integration, and ACVASA methods (similar to βACVASA but based on distance-cutoff contacts). Based on our data analysis and results, we can draw conclusions that: (i) our method is powerful in the prediction of binding free energy change after alanine mutation; (ii) β contacts are better than distance-cutoff contacts for modeling the well-organized protein-binding interfaces; (iii) β contacts usually are only a small fraction number of the distance-based contacts; and (iv) water exclusion is a necessary condition for a residue to become a binding hot spot.

Conclusions

βACVASA is designed using the advantages of both β contacts and water exclusion. It is an excellent tool to predict binding free energy changes and binding hot spots after alanine mutation.