3D protein docking

The problem of protein docking involves prediction of a protein’s (ligand) conformation and orientation, also known as pose, within the active site of another protein (receptor). This is a highly time consuming task, when it is performed experimentally in the laboratory. Alternatively, computational approaches can be utilized in order to rapidly predict a set of candidate poses for a given pair of proteins. This can significantly speed-up the process of predicting the correct pose, since computational tools can provide a fast filtering, leaving for experimental validation only a very limited set of poses. Computational methods of protein docking are based either on physicochemical complementarity or on geometric complementarity. The latter indicates that the surfaces of the two proteins at their binding regions should be complementary.

The proposed protein docking tool is able to predict the final complex, which is produced by the interaction between two proteins. More specifically, it accurately predicts the translation and rotation (pose) of one protein (ligand) with respect to another protein (receptor) in order to produce a stable complex. The tool computes the molecular surfaces of the interacting proteins and then compares these surfaces in terms of geometric complementarity. The method outperforms existing state-of-the-art approaches.

Demonstration

Relevant Projects

Internal Research

Relevant Publications

Axenopoulos, P. Daras, G. Papadopoulos, E. Houstis, SP-Dock: Protein-Protein Docking using Shape and Physicochemical Complementarity“, IEEE/ACM Transactions on Computational Biology and Bioinformatics, accepted for publication

A.      Axenopoulos, P. Daras, G. Papadopoulos, E. Houstis, A Shape Descriptor for Fast Complementarity Matching in Molecular Docking“, IEEE/ACM Transactions on Computational Biology and Bioinformatics, DOI: 10.1109/TCBB.2011.72, 2011

P. Daras, D. Zarpalas, A. Axenopoulos, D. Tzovaras, M. G. Strintzis, Three-Dimensional Shape-Structure Comparison Method for Protein Classification“, IEEE/ACM transactions on Computational Biology and Bioinformatics, Vol. 3, No. 3, pp. 193-207, Jul 2006

A. Axenopoulos, P. Daras, G. Papadopoulos, E. Houstis, 3D Protein-Protein Docking using Shape Complementarity and Fast Alignment“, IEEE International Conference on Image Processing, (ICIP 2011), Brussels, Belgium, Sep 11-14, 2011

V. Tsatsaias, P. Daras, M. G. Strintzis, 3D Protein Classification Using Topological, Geometrical and Biological Information” IEEE International Conference on Image Processing, (ICIP 2007), San Antonio, Texas, USA, Sep 2007.