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Human and server docking prediction for CAPRI round 30‐35 using LZerD with combined scoring functions
Author(s) -
Peterson Lenna X.,
Kim Hyungrae,
EsquivelRodriguez Juan,
Roy Amitava,
Han Xusi,
Shin WoongHee,
Zhang Jian,
Terashi Genki,
Lee Matt,
Kihara Daisuke
Publication year - 2017
Publication title -
proteins: structure, function, and bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.699
H-Index - 191
eISSN - 1097-0134
pISSN - 0887-3585
DOI - 10.1002/prot.25165
Subject(s) - docking (animal) , macromolecular docking , computer science , casp , artificial intelligence , machine learning , computational biology , protein structure , data mining , protein structure prediction , biology , biochemistry , medicine , nursing
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. © 2016 Wiley Periodicals, Inc.