Literature DB >> 26599419

Sampling of conformational ensemble for virtual screening using molecular dynamics simulations and normal mode analysis.

Gautier Moroy1,2, Olivier Sperandio1,2, Shakti Rielland1,2, Saurabh Khemka3, Karen Druart1,2, Divij Goyal3, David Perahia3, Maria A Miteva1,2.   

Abstract

AIM: Molecular dynamics simulations and normal mode analysis are well-established approaches to generate receptor conformational ensembles (RCEs) for ligand docking and virtual screening. Here, we report new fast molecular dynamics-based and normal mode analysis-based protocols combined with conformational pocket classifications to efficiently generate RCEs. MATERIALS &
METHODS: We assessed our protocols on two well-characterized protein targets showing local active site flexibility, dihydrofolate reductase and large collective movements, CDK2. The performance of the RCEs was validated by distinguishing known ligands of dihydrofolate reductase and CDK2 among a dataset of diverse chemical decoys. RESULTS & DISCUSSION: Our results show that different simulation protocols can be efficient for generation of RCEs depending on different kind of protein flexibility.

Entities:  

Keywords:  CDK2; DHFR; molecular dynamics simulations; normal mode analysis; protein conformational ensemble; virtual screening

Mesh:

Substances:

Year:  2015        PMID: 26599419     DOI: 10.4155/fmc.15.150

Source DB:  PubMed          Journal:  Future Med Chem        ISSN: 1756-8919            Impact factor:   3.808


  9 in total

Review 1.  Towards gaining sight of multiscale events: utilizing network models and normal modes in hybrid methods.

Authors:  James M Krieger; Pemra Doruker; Ana Ligia Scott; David Perahia; Ivet Bahar
Journal:  Curr Opin Struct Biol       Date:  2020-07-01       Impact factor: 6.809

2.  Efficiency of Stratification for Ensemble Docking Using Reduced Ensembles.

Authors:  Bing Xie; John D Clark; David D L Minh
Journal:  J Chem Inf Model       Date:  2018-08-29       Impact factor: 4.956

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

Authors:  Joel Ricci-Lopez; Sergio A Aguila; Michael K Gilson; Carlos A Brizuela
Journal:  J Chem Inf Model       Date:  2021-10-15       Impact factor: 4.956

4.  Virtual screening for potential inhibitors of Mcl-1 conformations sampled by normal modes, molecular dynamics, and nuclear magnetic resonance.

Authors:  Yitav Glantz-Gashai; Tomer Meirson; Eli Reuveni; Abraham O Samson
Journal:  Drug Des Devel Ther       Date:  2017-06-19       Impact factor: 4.162

5.  Online structure-based screening of purchasable approved drugs and natural compounds: retrospective examples of drug repositioning on cancer targets.

Authors:  Nathalie Lagarde; Julien Rey; Aram Gyulkhandanyan; Pierre Tufféry; Maria A Miteva; Bruno O Villoutreix
Journal:  Oncotarget       Date:  2018-08-17

6.  re-TAMD: exploring interactions between H3 peptide and YEATS domain using enhanced sampling.

Authors:  Gilles Lamothe; Thérèse E Malliavin
Journal:  BMC Struct Biol       Date:  2018-04-03

7.  Insights into molecular mechanisms of drug metabolism dysfunction of human CYP2C9*30.

Authors:  Maxime Louet; Céline M Labbé; Charline Fagnen; Cassiano M Aono; Paula Homem-de-Mello; Bruno O Villoutreix; Maria A Miteva
Journal:  PLoS One       Date:  2018-05-10       Impact factor: 3.240

8.  A Free Web-Based Protocol to Assist Structure-Based Virtual Screening Experiments.

Authors:  Nathalie Lagarde; Elodie Goldwaser; Tania Pencheva; Dessislava Jereva; Ilza Pajeva; Julien Rey; Pierre Tuffery; Bruno O Villoutreix; Maria A Miteva
Journal:  Int J Mol Sci       Date:  2019-09-19       Impact factor: 5.923

9.  Insights into the substrate binding mechanism of SULT1A1 through molecular dynamics with excited normal modes simulations.

Authors:  Balint Dudas; Daniel Toth; David Perahia; Arnaud B Nicot; Erika Balog; Maria A Miteva
Journal:  Sci Rep       Date:  2021-06-23       Impact factor: 4.379

  9 in total

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