Protein-protein docking using region-based 3D Zernike descriptors

Abstract

BackgroundProtein-protein interactions are a pivotal component of many biological processes and mediate a variety of functions. Knowing the tertiary structure of a protein complex is therefore essential for understanding the interaction mechanism. However, experimental techniques to solve the structure of the complex are often found to be difficult. To this end, computational protein-protein docking approaches can provide a useful alternative to address this issue. Prediction of docking conformations relies on methods that effectively capture shape features of the participating proteins while giving due consideration to conformational changes that may occur.ResultsWe present a novel protein docking algorithm based on the use of 3D Zernike descriptors as regional features of molecular shape. The key motivation of using these descriptors is their invariance to transformation, in addition to a compact representation of local surface shape characteristics. Docking decoys are generated using geometric hashing, which are then ranked by a scoring function that incorporates a buried surface area and a novel geometric complementarity term based on normals associated with the 3D Zernike shape description. Our docking algorithm was tested on both bound and unbound cases in the ZDOCK benchmark 2.0 dataset. In 74% of the bound docking predictions, our method was able to find a near-native solution (interface C-αRMSD ≤ 2.5 Å) within the top 1000 ranks. For unbound docking, among the 60 complexes for which our algorithm returned at least one hit, 60% of the cases were ranked within the top 2000. Comparison with existing shape-based docking algorithms shows that our method has a better performance than the others in unbound docking while remaining competitive for bound docking cases.ConclusionWe show for the first time that the 3D Zernike descriptors are adept in capturing shape complementarity at the protein-protein interface and useful for protein docking prediction. Rigorous benchmark studies show that our docking approach has a superior performance compared to existing methods.

Tuning of Pectin Methylesterification

“…31. C, interaction between PME3 (gray) and PMEI7 (blue) was evaluated as described (55,56) using LZerD based on shape complementarity. Model number 8 from the 15 best models was selected based on the literature data and the number of contacting residues.…”

“…Important putative amino acid residues were labeled according to the literature (29,(52)(53)(54). Interaction between PME3 and PMEI7 was evaluated by docking analysis using LZerD based on shape complementarity (55,56). Model number 8 from the 15 best models was selected based on the literature data and the number of contacting residues.…”

Potential for Protein Surface Shape Analysis Using Spherical Harmonics and 3D Zernike Descriptors

“…The extension of the 2D Zernike to its 3D counterpart was initially formulated by Canterakis [43] and later applied to shape retrieval by Novotni and Klein [44]. Their introduction into bioinformatics has, however, been quite recent with applications in protein shape comparison [26,45] and docking [46]. A difference of the 3DZD from the spherical harmonics is the addition of a radial term, which enables 3D shapes to be modeled more precisely than spherical harmonics.…”

An Exhaustive Shape-Based Approach for Proteins' Secondary, Tertiary and Quaternary Structures Indexing, Retrieval and Docking

“…Venkatraman et al (Venkatraman et al, 2009) also use a global approach, based on 3D Zernike polynomials. The shape is represented by a mixture of Gaussians centred on the constituent carbon atoms.…”