Title
Protein-protein binding site identification by enumerating the configurations.
Abstract
The ability to predict protein-protein binding sites has a wide range of applications, including signal transduction studies, de novo drug design, structure identification and comparison of functional sites. The interface in a complex involves two structurally matched protein subunits, and the binding sites can be predicted by identifying structural matches at protein surfaces.We propose a method which enumerates "all" the configurations (or poses) between two proteins (3D coordinates of the two subunits in a complex) and evaluates each configuration by the interaction between its components using the Atomic Contact Energy function. The enumeration is achieved efficiently by exploring a set of rigid transformations. Our approach incorporates a surface identification technique and a method for avoiding clashes of two subunits when computing rigid transformations. When the optimal transformations according to the Atomic Contact Energy function are identified, the corresponding binding sites are given as predictions. Our results show that this approach consistently performs better than other methods in binding site identification.Our method achieved a success rate higher than other methods, with the prediction quality improved in terms of both accuracy and coverage. Moreover, our method is being able to predict the configurations of two binding proteins, where most of other methods predict only the binding sites. The software package is available at http://sites.google.com/site/guofeics/dobi for non-commercial use.
Year
DOI
Venue
2012
10.1186/1471-2105-13-158
BMC Bioinformatics
Keywords
Field
DocType
binding sites,microarrays,algorithms,protein binding,bioinformatics
Plasma protein binding,Binding site,Biology,Molecular Docking Simulation,Protein protein,Signal transduction,Bioinformatics,Protein subunit,DNA microarray
Journal
Volume
Issue
ISSN
13
1
1471-2105
Citations 
PageRank 
References 
7
0.38
14
Authors
4
Name
Order
Citations
PageRank
Fei Guo14210.37
Shuai Cheng Li218430.25
Lusheng Wang32433224.97
Daming Zhu422642.54