Memdock: An α-Helical Membrane Protein Docking Algorithm

2021 ◽  
pp. 111-117
Author(s):  
Naama Hurwitz ◽  
Haim J. Wolfson
Keyword(s):  
Membrane Protein ◽  
Protein Docking ◽  
Docking Algorithm

scholarly journals Memdock: an α-helical membrane protein docking algorithm

2016 ◽  
Vol 32 (16) ◽  
pp. 2444-2450 ◽  
Author(s):  
Naama Hurwitz ◽  
Dina Schneidman-Duhovny ◽  
Haim J. Wolfson
Keyword(s):  
Membrane Protein ◽  
Protein Docking ◽  
Docking Algorithm

scholarly journals Improved protein docking by predicted interface residues

2021 ◽  
Author(s):  
Gabriele Pozzati ◽  
Petras Kundrotas ◽  
Arne Elofsson
Keyword(s):  
State Of The Art ◽  
Protein Docking ◽  
Prediction Methods ◽  
Scoring Functions ◽  
Protein Interface ◽  
High Importance ◽  
Docking Algorithm ◽  
Docking Protocol ◽  
Template Free ◽  
Interface Prediction

ABSTRACTScoring docking solutions is a difficult task, and many methods have been developed for this purpose. In docking, only a handful of the hundreds of thousands of models generated by docking algorithms are acceptable, causing difficulties when developing scoring functions. Today’s best scoring functions can significantly increase the number of top-ranked models but still fails for most targets. Here, we examine the possibility of utilising predicted residues on a protein-protein interface to score docking models generated during the scan stage of a docking algorithm. Many methods have been developed to infer the portions of a protein surface that interact with another protein, but most have not been benchmarked using docking algorithms. Different interface prediction methods are systematically tested for scoring >300.000 low-resolution rigid-body template free docking decoys. Overall we find that BIPSPI is the best method to identify interface amino acids and score docking solutions. Further, using BIPSPI provides better docking results than state of the art scoring functions, with >12% of first ranked docking models being acceptable. Additional experiments indicated precision as a high-importance metric when estimating interface prediction quality, focusing on docking constraints production. We also discussed several limitations for the adoption of interface predictions as constraints in a docking protocol.

scholarly journals A protein–protein docking algorithm dependent on the type of complexes

2003 ◽  
Vol 16 (4) ◽  
pp. 265-269 ◽  
Author(s):  
Chun Hua Li ◽  
Xiao Hui Ma ◽  
Wei Zu Chen ◽  
Cun Xin Wang
Keyword(s):  
Protein Docking ◽  
Docking Algorithm

scholarly journals An insight into SARS-CoV-2 Membrane protein interaction with Spike, Envelope, and Nucleocapsid proteins

2020 ◽  
Author(s):  
Amit kumar ◽  
Prateek Kumar ◽  
Neha Garg ◽  
Rajanish Giri
Keyword(s):  
Membrane Protein ◽  
Protein Interactions ◽  
Protein Complex ◽  
Drug Targets ◽  
Virus Assembly ◽  
Protein Docking ◽  
Structural Proteins ◽  
Valuable Insight ◽  
Nucleocapsid Proteins ◽  
Insight Into

AbstractIntraviral protein-protein interactions are crucial for replication, pathogenicity, and viral assembly. Among these, virus assembly is a critical step as it regulates the arrangements of viral structural proteins and helps in the encapsulation of genomic material. SARS-CoV-2 structural proteins play an essential role in the self-rearrangement, RNA encapsulation, and mature virus particle formation. In SARS-CoV, the membrane protein interacts with the envelope and spike protein in Endoplasmic Reticulum Golgi Intermediate Complex (ERGIC) to form an assembly in the lipid bilayer, followed by membrane-ribonucleoprotein (nucleocapsid) interaction. In this study, using protein-protein docking, we tried to understand the interaction of membrane protein’s interaction with envelope, spike and nucleocapsid proteins. Further, simulation studies performed up to 100ns agreed that protein complexes M-E, M-S, and M-N were stable. Moreover, the calculated free binding energy and dissociation constant values support the protein complex formation. The interaction identified in the study will be of great importance, as it provides valuable insight into the protein complex, which could be the potential drug targets for future studies.

ZDOCK: An initial-stage protein-docking algorithm

2003 ◽  
Vol 52 (1) ◽  
pp. 80-87 ◽  
Author(s):  
Rong Chen ◽  
Li Li ◽  
Zhiping Weng
Keyword(s):  
Protein Docking ◽  
Docking Algorithm ◽  
Initial Stage

scholarly journals ParaDock: a flexible non-specific DNA--rigid protein docking algorithm

2011 ◽  
Vol 39 (20) ◽  
pp. e135-e135 ◽  
Author(s):  
I. Banitt ◽  
H. J. Wolfson
Keyword(s):  
Protein Docking ◽  
Docking Algorithm

scholarly journals ASPDock: protein-protein docking algorithm using atomic solvation parameters model

2011 ◽  
Vol 12 (1) ◽  
pp. 36 ◽  
Author(s):  
Lin Li ◽  
Dachuan Guo ◽  
Yangyu Huang ◽  
Shiyong Liu ◽  
Yi Xiao
Keyword(s):  
Protein Docking ◽  
Docking Algorithm ◽  
Solvation Parameters

scholarly journals Extension of a protein docking algorithm to membranes and applications to amyloid precursor protein dimerization

2015 ◽  
Vol 83 (12) ◽  
pp. 2170-2185 ◽  
Author(s):  
Shruthi Viswanath ◽  
Laura Dominguez ◽  
Leigh S. Foster ◽  
John E. Straub ◽  
Ron Elber
Keyword(s):  
Amyloid Precursor Protein ◽  
Protein Docking ◽  
Precursor Protein ◽  
Protein Dimerization ◽  
Docking Algorithm

scholarly journals A novel volumetric criterion for optimal shape matching of surfaces for protein-protein docking

2017 ◽  
Vol 5 (2) ◽  
pp. 180-190
Author(s):  
Hari K. Voruganti ◽  
Bhaskar Dasgupta
Keyword(s):  
Shape Matching ◽  
Contact Point ◽  
Scoring Function ◽  
Protein Docking ◽  
Surface Generation ◽  
Shape Complementarity ◽  
Surface Patches ◽  
Docking Algorithm ◽  
Cad Cam ◽  
Top 40

Abstract The problem of molecular docking is to predict whether two given molecules bind together to interact. A shape-based algorithm is proposed for predictive docking by noting that shape complementarity between their outer surfaces is necessary for two molecules to bind. A methodology with five stages has been developed to find the pose in which the shape complementarity is maximum. It involves surface generation, segmentation, parameterization, shape matching, and filtering and scoring. The most significant contribution of this paper is the novel scoring function called ‘Normalized Volume Mismatch’ which evaluates the matching between a pair of surface patches efficiently by measuring the gap or solid volume entrapped between two patches of a pair of proteins when they are placed one against the other at a contact point. After the evaluation, it is found that, with local shape complementarity as the only criterion, the algorithm is able to predict a conformation close to the exact one, in case of known docking conformations, and also rank the same among the top 40 solutions. This is remarkable considering the fact that many existing docking methods fail to rank a near-native conformation among top 50 solutions. The shape-based approaches are used for the initial stage of docking to identify a small set of candidate solutions to be investigated further with exhaustive energy studies etc. The ability of capturing the correct conformation as highly ranked among top few candidate solutions is the most valuable facet of this new predictive docking algorithm. Highlights A new rigid-body docking algorithm is proposed for protein–protein docking. An approach using techniques of cad/cam for a problem in biology is presented. Unlike many existing ones, a volume based scoring criterion is proposed. The new criteria can capture even multiple possible docking conformation. Entire automatic docking procedures is based on shape complementarity only.

Sign in / Sign up

Export Citation Format

Share Document