title: Integrating water exclusion theory into β contacts to predict binding free energy changes and binding hot spots.
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.
authors.forename: Qian
authors.initials: Q
authors.lastname: Liu
authors.forename: Steven C H
authors.initials: SC
authors.lastname: Hoi
authors.forename: Chee Keong
authors.initials: CK
authors.lastname: Kwoh
authors.forename: Limsoon
authors.initials: L
authors.lastname: Wong
authors.affiliation: Advanced Analytics Institute and Center for Health Technologies, Faculty of Engineering and IT, University of Technology, Sydney, Australia. jinyan.li@uts.edu.au.
authors.forename: Jinyan
authors.initials: J
authors.lastname: Li
mesh_terms: D000409:Alanine / Q000737:chemistry / Q000378:metabolism; D019295:Computational Biology / Q000379:methods*; D008958:Models, Molecular; D009154:Mutation; D011485:Protein Binding; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism*; D013816:Thermodynamics; D014867:Water
chemical_list: D011506:Proteins; D014867:Water; D000409:Alanine