Literature DB >> 34888727

Creating Maps of the Ligand Binding Landscape for Kinetics-Based Drug Discovery.

Tom Dixon1,2, Samuel D Lotz1,3, Alex Dickson4,5.   

Abstract

Simulations of ligand-protein interactions can be very useful for drug design and to gain biological insight. Full pathways of ligand-protein binding can be used to get information about ligand binding transition states, which form the rate-limiting step of the binding and release processes. However, these simulations are typically limited by the presence of large energy barriers that separate stable poses of interest. Here we describe a simulation protocol for exploring and analyzing landscapes of ligand-protein interactions that makes use of molecular docking, enhanced molecular simulation with the weighted ensemble algorithm, and network analysis. It can be accomplished using a modest cluster of graphics processing units and freely accessible software. This protocol focuses on the construction and analysis of a network model of ligand binding poses and provides links to resources that describe the other steps in more detail. The end result of this protocol is a map of the ligand-protein binding landscape that identifies transition states of the ligand binding pathway, as well as alternative bound poses that could be stabilized with modifications to the ligand.
© 2022. Springer Science+Business Media, LLC, part of Springer Nature.

Entities:  

Keywords:  Kinetics; Ligand binding; Molecular dynamics; Network analysis; Transition paths; Weighted ensemble

Mesh:

Substances:

Year:  2022        PMID: 34888727     DOI: 10.1007/978-1-0716-1767-0_15

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  19 in total

Review 1.  The drug-target residence time model: a 10-year retrospective.

Authors:  Robert A Copeland
Journal:  Nat Rev Drug Discov       Date:  2015-12-18       Impact factor: 84.694

2.  Weighted-ensemble Brownian dynamics simulations for protein association reactions.

Authors:  G A Huber; S Kim
Journal:  Biophys J       Date:  1996-01       Impact factor: 4.033

3.  Exhaustive Search of Ligand Binding Pathways via Volume-Based Metadynamics.

Authors:  Riccardo Capelli; Paolo Carloni; Michele Parrinello
Journal:  J Phys Chem Lett       Date:  2019-06-12       Impact factor: 6.475

4.  Unbinding Kinetics of a p38 MAP Kinase Type II Inhibitor from Metadynamics Simulations.

Authors:  Rodrigo Casasnovas; Vittorio Limongelli; Pratyush Tiwary; Paolo Carloni; Michele Parrinello
Journal:  J Am Chem Soc       Date:  2017-03-24       Impact factor: 15.419

Review 5.  Molecular determinants of drug-receptor binding kinetics.

Authors:  Albert C Pan; David W Borhani; Ron O Dror; David E Shaw
Journal:  Drug Discov Today       Date:  2013-02-27       Impact factor: 7.851

6.  Long-Range Changes in Neurolysin Dynamics Upon Inhibitor Binding.

Authors:  A Uyar; V T Karamyan; A Dickson
Journal:  J Chem Theory Comput       Date:  2017-12-08       Impact factor: 6.006

7.  Unbiased Molecular Dynamics of 11 min Timescale Drug Unbinding Reveals Transition State Stabilizing Interactions.

Authors:  Samuel D Lotz; Alex Dickson
Journal:  J Am Chem Soc       Date:  2018-01-05       Impact factor: 15.419

8.  Estimation of Drug-Target Residence Times by τ-Random Acceleration Molecular Dynamics Simulations.

Authors:  Daria B Kokh; Marta Amaral; Joerg Bomke; Ulrich Grädler; Djordje Musil; Hans-Peter Buchstaller; Matthias K Dreyer; Matthias Frech; Maryse Lowinski; Francois Vallee; Marc Bianciotto; Alexey Rak; Rebecca C Wade
Journal:  J Chem Theory Comput       Date:  2018-06-04       Impact factor: 6.006

9.  The free energy landscape of small molecule unbinding.

Authors:  Danzhi Huang; Amedeo Caflisch
Journal:  PLoS Comput Biol       Date:  2011-02-03       Impact factor: 4.475

10.  TSPO ligand residence time: a new parameter to predict compound neurosteroidogenic efficacy.

Authors:  Barbara Costa; Eleonora Da Pozzo; Chiara Giacomelli; Elisabetta Barresi; Sabrina Taliani; Federico Da Settimo; Claudia Martini
Journal:  Sci Rep       Date:  2016-01-11       Impact factor: 4.379

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