Hierarchical Flexible Peptide Docking by Conformer Generation and Ensemble Docking of Peptides

2018 ◽  
Vol 58 (6) ◽  
pp. 1292-1302 ◽  
Author(s):  
Pei Zhou ◽  
Botong Li ◽  
Yumeng Yan ◽  
Bowen Jin ◽  
Libang Wang ◽  
...  
Keyword(s):  
Conformer Generation ◽  
Ensemble Docking ◽  
Peptide Docking

Drug Repurposing Studies Targeting SARS-nCoV2: An Ensemble Docking Approach on Drug Target 3C-like Protease (3CLpro)

2020 ◽  
Author(s):  
Shruti Koulgi ◽  
Vinod Jani ◽  
Mallikarjunachari Uppuladinne ◽  
Uddhavesh Sonavane ◽  
Asheet Kumar Nath ◽  
...  
Keyword(s):  
Drug Repurposing ◽  
Drug Binding ◽  
Ensemble Docking ◽  
Drug Molecules ◽  
Drug Binding Site ◽  
Main Protease ◽  
Docking Approach ◽  
Novel Coronavirus ◽  
Markov State Modeling ◽  
Viral Polyprotein

<p>The COVID-19 pandemic has been responsible for several deaths worldwide. The causative agent behind this disease is the Severe Acute Respiratory Syndrome – novel Coronavirus 2 (SARS-nCoV2). SARS-nCoV2 belongs to the category of RNA viruses. The main protease, responsible for the cleavage of the viral polyprotein is considered as one of the hot targets for treating COVID-19. Earlier reports suggest the use of HIV anti-viral drugs for targeting the main protease of SARS-CoV, which caused SARS in the year 2002-03. Hence, drug repurposing approach may prove to be useful in targeting the main protease of SARS-nCoV2. The high-resolution crystal structure of 3CL<sup>pro</sup> (main protease) of SARS-nCoV2 (PDB ID: 6LU7) was used as the target. The Food and Drug Administration (FDA) approved and SWEETLEAD database of drug molecules were screened. The apo form of the main protease was simulated for a cumulative of 150 ns and 10 μs open source simulation data was used, to obtain conformations for ensemble docking. The representative structures for docking were selected using RMSD-based clustering and Markov State Modeling analysis. This ensemble docking approach for main protease helped in exploring the conformational variation in the drug binding site of the main protease leading to efficient binding of more relevant drug molecules. The drugs obtained as best hits from the ensemble docking possessed anti-bacterial and anti-viral properties. Small molecules with these properties may prove to be useful to treat symptoms exhibited in COVID-19. This <i>in-silico</i> ensemble docking approach would support identification of potential candidates for repurposing against COVID-19.</p>

BCL::Conf – Improved Open-Source Knowledge-Based Conformation Sampling using the Crystallographic Open Database

2020 ◽  
Author(s):  
Jeffrey Mendenhall ◽  
Benjamin Brown ◽  
Sandeepkumar Kothiwale ◽  
Jens Meiler
Keyword(s):  
Ligand Binding ◽  
Open Source ◽  
Operating Systems ◽  
Generation Algorithm ◽  
Rotamer Library ◽  
Conformer Generation ◽  
Knowledge Based

<div>This paper describes recent improvements made to the BCL::Conf rotamer generation algorithm and comparison of its performance against other freely available and commercial conformer generation software. We demonstrate that BCL::Conf, with the use of rotamers derived from the COD, more effectively recovers crystallographic ligand-binding conformations seen in the PDB than other commercial and freely available software. BCL::Conf is now distributed with the COD-derived rotamer library, free for academic use. The BCL can be downloaded at <a href="http://meilerlab.org/index.php/bclcommons/show/b_apps_id/1">http://meilerlab.org/ bclcommons</a> for Windows, Linux, or Apple operating systems.<br></div>

scholarly journals Combined Ensemble Docking and Machine Learning in Identification of Therapeutic Agents with Potential Inhibitory Effect on Human CES1

Molecules ◽  
2019 ◽  
Vol 24 (15) ◽  
pp. 2747 ◽  
Author(s):  
Eliane Briand ◽  
Ragnar Thomsen ◽  
Kristian Linnet ◽  
Henrik Berg Rasmussen ◽  
Søren Brunak ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Interactions ◽  
Scoring Function ◽  
Docking Study ◽  
Inhibitory Effect ◽  
Therapeutic Agents ◽  
Learning Approaches ◽  
Ensemble Docking ◽  
Ic50 Values

The human carboxylesterase 1 (CES1), responsible for the biotransformation of many diverse therapeutic agents, may contribute to the occurrence of adverse drug reactions and therapeutic failure through drug interactions. The present study is designed to address the issue of potential drug interactions resulting from the inhibition of CES1. Based on an ensemble of 10 crystal structures complexed with different ligands and a set of 294 known CES1 ligands, we used docking (Autodock Vina) and machine learning methodologies (LDA, QDA and multilayer perceptron), considering the different energy terms from the scoring function to assess the best combination to enable the identification of CES1 inhibitors. The protocol was then applied on a library of 1114 FDA-approved drugs and eight drugs were selected for in vitro CES1 inhibition. An inhibition effect was observed for diltiazem (IC50 = 13.9 µM). Three others drugs (benztropine, iloprost and treprostinil), exhibited a weak CES1 inhibitory effects with IC50 values of 298.2 µM, 366.8 µM and 391.6 µM respectively. In conclusion, the binding site of CES1 is relatively flexible and can adapt its conformation to different types of ligands. Combining ensemble docking and machine learning approaches improves the prediction of CES1 inhibitors compared to a docking study using only one crystal structure.

scholarly journals Covalent Flexible Peptide Docking in Rosetta

2021 ◽  
Author(s):  
Barr Tivon ◽  
Ronen Gabizon ◽  
Bente A Somsen ◽  
Peter J Cossar ◽  
Christian Ottmann ◽  
...  
Keyword(s):  
Covalent Bond ◽  
Peptide Docking

Electrophilic peptides that form an irreversible covalent bond with their target have great potential for binding targets that have been previously considered undruggable. However, the discovery of such peptides remains...

scholarly journals Elucidation of the Molecular Basis of Cholecystokinin Peptide Docking to Its Receptor Using Site-Specific Intrinsic Photoaffinity Labeling and Molecular Modeling

Biochemistry ◽  
2009 ◽  
Vol 48 (23) ◽  
pp. 5303-5312 ◽  
Author(s):  
Maoqing Dong ◽  
Polo C.-H. Lam ◽  
Delia I. Pinon ◽  
Ruben Abagyan ◽  
Laurence J. Miller
Keyword(s):  
Molecular Modeling ◽  
Molecular Basis ◽  
Photoaffinity Labeling ◽  
Site Specific ◽  
Peptide Docking

Improving Structure-Based Virtual Screening with Ensemble Docking and Machine Learning

2021 ◽  
Author(s):  
Joel Ricci-Lopez ◽  
Sergio A. Aguila ◽  
Michael K. Gilson ◽  
Carlos A. Brizuela
Keyword(s):  
Machine Learning ◽  
Virtual Screening ◽  
Ensemble Docking

scholarly journals A selective method for optimizing ensemble docking-based experiments on an InhA Fully-Flexible receptor model

2018 ◽  
Vol 19 (1) ◽  
Author(s):  
Renata De Paris ◽  
Christian Vahl Quevedo ◽  
Duncan D. Ruiz ◽  
Furia Gargano ◽  
Osmar Norberto de Souza
Keyword(s):  
Receptor Model ◽  
Selective Method ◽  
Ensemble Docking

scholarly journals Prediction of GluN2B-CT1290-1310/DAPK1 Interaction by Protein–Peptide Docking and Molecular Dynamics Simulation

Molecules ◽  
2018 ◽  
Vol 23 (11) ◽  
pp. 3018 ◽  
Author(s):  
Gao Tu ◽  
Tingting Fu ◽  
Fengyuan Yang ◽  
Lixia Yao ◽  
Weiwei Xue ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Free Energy ◽  
Electrostatic Interactions ◽  
Binding Free Energy ◽  
Computational Simulation ◽  
Critical Role ◽  
Dynamics Simulation ◽  
Peptide Docking ◽  
C Terminus ◽  
Free Energy Decomposition

The interaction of death-associated protein kinase 1 (DAPK1) with the 2B subunit (GluN2B) C-terminus of N-methyl-D-aspartate receptor (NMDAR) plays a critical role in the pathophysiology of depression and is considered a potential target for the structure-based discovery of new antidepressants. However, the 3D structures of C-terminus residues 1290–1310 of GluN2B (GluN2B-CT1290-1310) remain elusive and the interaction between GluN2B-CT1290-1310 and DAPK1 is unknown. In this study, the mechanism of interaction between DAPK1 and GluN2B-CT1290-1310 was predicted by computational simulation methods including protein–peptide docking and molecular dynamics (MD) simulation. Based on the equilibrated MD trajectory, the total binding free energy between GluN2B-CT1290-1310 and DAPK1 was computed by the mechanics generalized born surface area (MM/GBSA) approach. The simulation results showed that hydrophobic, van der Waals, and electrostatic interactions are responsible for the binding of GluN2B-CT1290–1310/DAPK1. Moreover, through per-residue free energy decomposition and in silico alanine scanning analysis, hotspot residues between GluN2B-CT1290-1310 and DAPK1 interface were identified. In conclusion, this work predicted the binding mode and quantitatively characterized the protein–peptide interface, which will aid in the discovery of novel drugs targeting the GluN2B-CT1290-1310 and DAPK1 interface.

scholarly journals dMM-PBSA: A New HADDOCK Scoring Function for Protein-Peptide Docking

2016 ◽  
Vol 3 ◽  
Author(s):  
Dimitrios Spiliotopoulos ◽  
Panagiotis L. Kastritis ◽  
Adrien S. J. Melquiond ◽  
Alexandre M. J. J. Bonvin ◽  
Giovanna Musco ◽  
...  
Keyword(s):  
Scoring Function ◽  
Peptide Docking
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