A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Three-dimensional alpha shapes
VVS '92 Proceedings of the 1992 workshop on Volume visualization
Fast and robust computation of molecular surfaces
Proceedings of the eleventh annual symposium on Computational geometry
Efficient Unbound Docking of Rigid Molecules
WABI '02 Proceedings of the Second International Workshop on Algorithms in Bioinformatics
Diffusion Distance for Histogram Comparison
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
3D object retrieval using the 3D shape impact descriptor
Pattern Recognition
Laplace-Beltrami spectra as 'Shape-DNA' of surfaces and solids
Computer-Aided Design
$F^2$Dock: Fast Fourier Protein-Protein Docking
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A Shape Descriptor for Fast Complementarity Matching in Molecular Docking
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Bag of words and local spectral descriptor for 3D partial shape retrieval
EG 3DOR'11 Proceedings of the 4th Eurographics conference on 3D Object Retrieval
SHREC'11 track: shape retrieval on non-rigid 3D watertight meshes
EG 3DOR'11 Proceedings of the 4th Eurographics conference on 3D Object Retrieval
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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.