Protein docking prediction using predicted protein-protein interface

Abstract

BackgroundMany important cellular processes are carried out by protein complexes. To provide physical pictures of interacting proteins, many computational protein-protein prediction methods have been developed in the past. However, it is still difficult to identify the correct docking complex structure within top ranks among alternative conformations.ResultsWe present a novel protein docking algorithm that utilizes imperfect protein-protein binding interface prediction for guiding protein docking. Since the accuracy of protein binding site prediction varies depending on cases, the challenge is to develop a method which does not deteriorate but improves docking results by using a binding site prediction which may not be 100% accurate. The algorithm, named PI-LZerD (using Predicted Interface with Local 3D Zernike descriptor-based Docking algorithm), is based on a pair wise protein docking prediction algorithm, LZerD, which we have developed earlier. PI-LZerD starts from performing docking prediction using the provided protein-protein binding interface prediction as constraints, which is followed by the second round of docking with updated docking interface information to further improve docking conformation. Benchmark results on bound and unbound cases show that PI-LZerD consistently improves the docking prediction accuracy as compared with docking without using binding site prediction or using the binding site prediction as post-filtering.ConclusionWe have developed PI-LZerD, a pairwise docking algorithm, which uses imperfect protein-protein binding interface prediction to improve docking accuracy. PI-LZerD consistently showed better prediction accuracy over alternative methods in the series of benchmark experiments including docking using actual docking interface site predictions as well as unbound docking cases.

DockRank: Ranking docked conformations using partner‐specific sequence homology‐based protein interface prediction

“…Li and Kihara 55 had concluded that predicted interfaces cannot be used to reliably identify near-native conformations, based on their study using predicted interfaces from meta-PPISP, 56 a nonpartner-specific interface predictor, to rank docked conformations using their scoring function. In light of the fact that most existing protein interface predictors are nonpartner-specific, it is of interest to examine the extent to which partner-specific interface predictions can improve the reliability of identifying near-native docked conformations.…”

Docking studies and network analyses reveal capacity of compounds from Kandelia rheedii to strengthen cellular immunity by interacting with host proteins during tuberculosis infection

“…Network based drug discovery has become a recurrent theme for natural compounds too; characterization of the drug regulated genes and targets have been assisted greatly by computational techniques [78–80]. Rational drug designing from natural sources have made drug costs and required resources to go down significantly over the years [81–83].…”

Predicting residue contacts for protein-protein interactions by integration of multiple information

“…In addition, the conformation changes of monomers during the formation of proteinprotein complexes are also a challenge in them [8]. Recently, some docking methods combined knowledge of PPI binding sites with the docking process to improve their performance [8,10], but their applicability is still limited. Because of these limitations, it is difficult to predict large protein complexes consisting of many structure units (e.g., domains and monomers) by docking methods.…”