Human and server docking prediction for CAPRI round 30‐35 using LZerD with combined scoring functions. Issue 3 (14th October 2016)

• Record Type: Journal Article
• Authors: Peterson, Lenna X.; Kim, Hyungrae; Esquivel‐Rodriguez, Juan; Roy, Amitava; Han, Xusi; Shin, Woong‐Hee; Zhang, Jian; Terashi, Genki; Lee, Matt; Kihara, Daisuke
• Is Part Of: Proteins. Volume 85:Issue 3(2017)
• Page Start: 513
• Page End: 527
• Publication Date: 2016-10-14
• Subjects: CAPRI -- protein docking prediction -- protein–protein docking -- protein structure prediction -- computational methods -- prediction accuracy -- structure modeling; Proteins -- Periodicals; Proteins -- Periodicals; 572.6
• DOI: 10.1002/prot.25165
• Languages: English
• ISSNs: 0887-3585

Abstract

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. © 2016 Wiley Periodicals, Inc.