Protein-protein docking based on geometric complementarity
Date of Issue2015
School of Computer Engineering
Emerging Research Lab
Protein complex conformation prediction is crucial for studying the biological systems and thus for drug design. However it is very hard to do the prediction because of the flexibility of the backbone and side-chains of the receptor and ligand. In our work, the “divide and conquer” scheme has been utilized for protein-protein docking, with an initial stage which focuses on generating a reasonable list of near-native candidate docking conformations, and a refinement step which aims to rank the hit within the first several positions by incorporating the protein flexibility into the docking procedure . In the initial docking stage, we present a new framework for an efficient and rigid-body docking in view of geometric complementarity. In our approach, the protein surfaces are firstly segmented into several local surface patches. Then the geometric complementarity can be determined by matching these surface patches. Based on the geometric property, we convert the geometric complementarity matching to geometric similarity comparing. During the matching process, we extract the shape feature for each surface patch, thus the geometric similarity comparing problem will be further simplified as a histogram matching problem. After finding the patch pairs with geometric complementarity, a list of docking candidates can be generated. Finally, the candidate solutions are ranked by a scoring function to filter out the near-native candidate conformations. In the refinement step, to deal with the protein flexibility and possible conformational changes during binding, many approaches have been proposed. Some of them allow some degree of overlap on the interface region and some algorithms simulate the soft docking by smoothing the protein surfaces. In our approach, we will deal with the side-chain flexibility by sampling its conformational space. This will be carried out in our future work. In this work, our major contribution is that we propose to apply an innovative three-dimensional shape descriptor, Spherical Harmonics Descriptor (SHD) in matching. The main property of SHD is rotation invariance that helps to avoid undergoing a very thorough series of rotations between each pair of surface patches. This can greatly reduce the computational cost. The experimental results illustrate the high efficiency and accuracy of our method.
DRNTU::Engineering::Computer science and engineering