Visual Analysis with Dynamic Geometric Complementarity and Physicochemical Matching in Protein Docking

Prediction of the 3D structure of protein-protein complexes is crucial for understanding the biological system of life and thus for designing drugs. However it is a challenging problem due to the flexibility of the backbone and side-chains of the receptor and ligand. In this paper, we present a new framework for fast and flexible protein-protein docking based on geometric and physicochemical complementarity. In our approach, we segment the protein surfaces into several local surface patches and describe the effect of side-chain flexibility on each patch by sampling the space of conformation of its side-chains. Then the geometric complementarity can be determined by matching these surface patches. During the matching, we propose a new 3D shape descriptor, Spherical Harmonics Descriptor. The main property of the Spherical Harmonics Descriptor is rotation invariance which avoids taking an exhaustive set of rotations for each pair of patches. Thus, the geometric complementarity matching problem is simplified to a histogram matching problem. This can greatly reduce the computational cost. Finally, the docking candidates are evaluated by a scoring function which can accommodate geometric complementarity and physicochemical matching. The experimental results demonstrate the high efficiency and accuracy of the proposed method.