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Proteins. 2017 Mar;85(3):513-527. doi: 10.1002/prot.25165. Epub 2016 Oct 14.

Human and server docking prediction for CAPRI round 30-35 using LZerD with combined scoring functions.

Author information

1
Department of Biological Sciences, Purdue University, West Lafayette, Indiana.
2
Department of Computer Science, Purdue University, West Lafayette, Indiana.
3
Department of Medicinal Chemistry and Molecular Pharmacology, Purdue University, West Lafayette, Indiana.
4
Bioinformatics and Computational Biosciences Branch, Rocky Mountain Laboratories, NIAID, National Institutes of Health, Hamilton, Montana, 59840.
5
School of Pharmacy, Kitasato University, Minato-Ku, Tokyo, 108-8641, Japan.
6
Lilly Biotechnology Center San Diego, 10300 Campus Point Drive, San Diego, California.

Abstract

We report the performance of protein-protein docking predictions by our group for recent rounds of the Critical Assessment of Prediction of Interactions (CAPRI), a community-wide assessment of state-of-the-art docking methods. Our prediction procedure uses a protein-protein docking program named LZerD developed in our group. LZerD represents a protein surface with 3D Zernike descriptors (3DZD), which are based on a mathematical series expansion of a 3D function. The appropriate soft representation of protein surface with 3DZD makes the method more tolerant to conformational change of proteins upon docking, which adds an advantage for unbound docking. Docking was guided by interface residue prediction performed with BindML and cons-PPISP as well as literature information when available. The generated docking models were ranked by a combination of scoring functions, including PRESCO, which evaluates the native-likeness of residues' spatial environments in structure models. First, we discuss the overall performance of our group in the CAPRI prediction rounds and investigate the reasons for unsuccessful cases. Then, we examine the performance of several knowledge-based scoring functions and their combinations for ranking docking models. It was found that the quality of a pool of docking models generated by LZerD, that is whether or not the pool includes near-native models, can be predicted by the correlation of multiple scores. Although the current analysis used docking models generated by LZerD, findings on scoring functions are expected to be universally applicable to other docking methods. Proteins 2017; 85:513-527.

KEYWORDS:

CAPRI; computational methods; prediction accuracy; protein docking prediction; protein structure prediction; protein-protein docking; structure modeling

PMID:
27654025
PMCID:
PMC5313330
DOI:
10.1002/prot.25165
[Indexed for MEDLINE]
Free PMC Article

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