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SP-Dock: Protein-Protein Docking Using Shape and Physicochemical Complementarity
Jan.-Feb. 2013 (vol. 10 no. 1)
pp. 135-150
A. Axenopoulos, Dept. of Comput. & Commun. Eng., Univ. of Thessaly, Volos, Greece
P. Daras, Centre for Res. & Technol. Hellas, Inf. Technol. Inst., Thessaloniki, Greece
G. E. Papadopoulos, Dept. of Biochem. & Biotechnol., Univ. of Thessaly, Larissa, Greece
E. N. Houstis, Dept. of Comput. & Commun. Eng., Univ. of Thessaly, Volos, Greece
In this paper, a framework for protein-protein docking is proposed, which exploits both shape and physicochemical complementarity to generate improved docking predictions. Shape complementarity is achieved by matching local surface patches. However, unlike existing approaches, which are based on single-patch or two-patch matching, we developed a new algorithm that compares simultaneously, groups of neighboring patches from the receptor with groups of neighboring patches from the ligand. Taking into account the fact that shape complementarity in protein surfaces is mostly approximate rather than exact, the proposed group-based matching algorithm fits perfectly to the nature of protein surfaces. This is demonstrated by the high performance that our method achieves especially in the case where the unbound structures of the proteins are considered. Additionally, several physicochemical factors, such as desolvation energy, electrostatic complementarity (EC), hydrophobicity (HP), Coulomb potential (CP), and Lennard-Jones potential are integrated using an optimized scoring function, improving geometric ranking in more than 60 percent of the complexes of Docking Benchmark 2.4.
Index Terms:
Proteins,Shape,Bioinformatics,Computational biology,Electric potential,Iterative closest point algorithm,Electrostatics,physicochemical complementarity,Protein docking,local descriptors,shape complementarity
Citation:
A. Axenopoulos, P. Daras, G. E. Papadopoulos, E. N. Houstis, "SP-Dock: Protein-Protein Docking Using Shape and Physicochemical Complementarity," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 10, no. 1, pp. 135-150, Jan.-Feb. 2013, doi:10.1109/TCBB.2012.149
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