Identification of Drug Binding Sites and Action Mechanisms with Molecular Dynamics Simulations

2019 ◽  
Vol 18 (27) ◽  
pp. 2268-2277 ◽  
Author(s):  
Yang Wang ◽  
Cecylia Severin Lupala ◽  
Haiguang Liu ◽  
Xubo Lin
Keyword(s):  
Molecular Dynamics ◽  
Drug Discovery ◽  
Binding Sites ◽  
Lipid Membrane ◽  
Md Simulations ◽  
Drug Action ◽  
Drug Binding ◽  
Action Mechanisms ◽  
Drug Binding Sites ◽  
Dynamics Simulations

Identifying drug binding sites and elucidating drug action mechanisms are important components in a drug discovery process. In this review, we briefly compared three different approaches (sequence- based methods, structure-based methods and probe-based molecular dynamics (MD) methods) to identifying drug binding sites, and concluded that probe-based MD methods are much more advantageous in dealing with flexible target macromolecules and digging out druggable macromolecule conformations for subsequent drug screening. The applications of MD simulation to studying drug-target interactions were demonstrated with different types of target molecules, including lipid membrane, protein and DNA. The results indicate that MD simulations with enhanced sampling methods provide a powerful tool to determine free energy profiles/surfaces and identify important intermediate states, which are essential for the elucidation of drug action mechanisms. The future development of methods in MD simulations will benefit and speed up the drug discovery processes.

Use of Molecular Dynamics Simulations in Structure-Based Drug Discovery

2019 ◽  
Vol 25 (31) ◽  
pp. 3339-3349 ◽  
Author(s):  
Indrani Bera ◽  
Pavan V. Payghan
Keyword(s):  
Molecular Dynamics ◽  
Drug Discovery ◽  
Molecular Dynamics Simulations ◽  
Drug Target ◽  
Structural Information ◽  
Drug Binding ◽  
Structure Based Drug Design ◽  
Drug Designing ◽  
Target Binding ◽  
Dynamics Simulations

Background: Traditional drug discovery is a lengthy process which involves a huge amount of resources. Modern-day drug discovers various multidisciplinary approaches amongst which, computational ligand and structure-based drug designing methods contribute significantly. Structure-based drug designing techniques require the knowledge of structural information of drug target and drug-target complexes. Proper understanding of drug-target binding requires the flexibility of both ligand and receptor to be incorporated. Molecular docking refers to the static picture of the drug-target complex(es). Molecular dynamics, on the other hand, introduces flexibility to understand the drug binding process. Objective: The aim of the present study is to provide a systematic review on the usage of molecular dynamics simulations to aid the process of structure-based drug design. Method: This review discussed findings from various research articles and review papers on the use of molecular dynamics in drug discovery. All efforts highlight the practical grounds for which molecular dynamics simulations are used in drug designing program. In summary, various aspects of the use of molecular dynamics simulations that underline the basis of studying drug-target complexes were thoroughly explained. Results: This review is the result of reviewing more than a hundred papers. It summarizes various problems that use molecular dynamics simulations. Conclusion: The findings of this review highlight how molecular dynamics simulations have been successfully implemented to study the structure-function details of specific drug-target complexes. It also identifies the key areas such as stability of drug-target complexes, ligand binding kinetics and identification of allosteric sites which have been elucidated using molecular dynamics simulations.

scholarly journals Structure and Dynamics of Glycosphingolipids in Lipid Bilayers: Insights from Molecular Dynamics Simulations

2011 ◽  
Vol 2011 ◽  
pp. 1-9 ◽  
Author(s):  
Ronak Y. Patel ◽  
Petety V. Balaji
Keyword(s):  
Molecular Dynamics ◽  
Lipid Bilayers ◽  
Membrane Structure ◽  
Lipid Membrane ◽  
Biological Membranes ◽  
Md Simulations ◽  
Atomic Level ◽  
Structure And Dynamics ◽  
Hydrogen Bonding Interactions ◽  
Dynamics Simulations

Glycolipids are important constituents of biological membranes, and understanding their structure and dynamics in lipid bilayers provides insights into their physiological and pathological roles. Experimental techniques have provided details into their behavior at model and biological membranes; however, computer simulations are needed to gain atomic level insights. This paper summarizes the insights obtained from MD simulations into the conformational and orientational dynamics of glycosphingolipids and their exposure, hydration, and hydrogen-bonding interactions in membrane environment. The organization of glycosphingolipids in raft-like membranes and their modulation of lipid membrane structure are also reviewed.

GPCR Drug Discovery Through the Exploitation of Allosteric Drug Binding Sites

2002 ◽  
Vol 8 (5-6) ◽  
pp. 261-268 ◽  
Author(s):  
Stephen Rees ◽  
Dwight Morrow ◽  
Terry Kenakin
Keyword(s):  
Drug Discovery ◽  
Binding Sites ◽  
Drug Binding ◽  
Drug Binding Sites

scholarly journals Insights from Molecular Dynamics Simulations for GPCR Drug Discovery

2019 ◽  
Author(s):  
Ye Zou ◽  
John Ewalt ◽  
Ho-Leung Ng
Keyword(s):  
Molecular Dynamics ◽  
Drug Discovery ◽  
G Protein ◽  
Drug Targets ◽  
Conformational Dynamics ◽  
Md Simulations ◽  
Protein Activation ◽  
Downstream Signaling ◽  
Dynamics Simulations ◽  
G Protein Coupled

G protein-coupled receptors (GPCRs) are critical drug targets. GPCRs convey signals from the extracellular to the intracellular environment through G proteins. There is evidence that some ligands that bind to the GPCRs activate different downstream signaling pathways. G protein activation or -arrestin biased signaling involves ligands binding to receptors and stabilizing conformations that trigger a specific pathway. Molecular dynamics (MD) simulations are especially valuable for obtaining detailed mechanistic information, including identification of allosteric sites and understanding modulators' interactions between receptors and ligands. Here, we highlight recent simulation studies and methods used to study biased G protein-coupled receptor signaling and their conformational dynamics. We also highlight applications of MD simulations to drug discovery.

GPCR Drug Discovery Through the Exploitation of Allosteric Drug Binding Sites

2002 ◽  
Vol 8 (5-6) ◽  
pp. 261-268 ◽  
Author(s):  
Stephen Rees ◽  
Dwight Morrow ◽  
Terry Kenakin
Keyword(s):  
Drug Discovery ◽  
Binding Sites ◽  
Drug Binding ◽  
Drug Binding Sites

Extracting atomic contributions to binding free energy from molecular dynamics simulations with mixed solvents (MDmix)

2021 ◽  
Vol 19 ◽  
Author(s):  
Daniel Alvarez- Garcia ◽  
Peter Schmidtke ◽  
Elena Cubero ◽  
Xavier Barril
Keyword(s):  
Molecular Dynamics ◽  
Free Energy ◽  
Drug Discovery ◽  
Drug Design ◽  
Binding Free Energy ◽  
Mixed Solvents ◽  
Md Simulations ◽  
Molecular Binding ◽  
Dynamics Simulations ◽  
Energy Maps

Background: Mixed solvents MD simulations have proved to be a useful and increasingly accepted technique with several applications in structure-based drug discovery Method: Mixed solvents MD simulations have proved to be a useful and increasingly accepted technique with several applications in structure-based drug discovery Result: As such, they are hardly transferable to different molecules. Conclusion: To achieve transferable energies, we present here a method for decomposing the molecular binding free energy into accurate atomic contributions and we demonstrate with two qualitative visual examples how the corrected energy maps better match known binding hotspots and how they can reveal hidden hotspots with actual drug design potential.

scholarly journals Neural relational inference to learn allosteric long-range interactions in proteins from molecular dynamics simulations

2021 ◽  
Author(s):  
Jingxuan Zhu ◽  
Juexin Wang ◽  
Weiwei Han ◽  
Dong Xu
Keyword(s):  
Molecular Dynamics ◽  
Long Range ◽  
Binding Sites ◽  
Md Simulation ◽  
Dynamic Networks ◽  
Md Simulations ◽  
Amino Acid Mutation ◽  
Long Range Interactions ◽  
Decoder Architecture ◽  
Dynamics Simulations

AbstractProtein allostery is a biological process facilitated by spatially long-range intra-protein communication, whereby ligand binding or amino acid mutation at a distant site affects the active site remotely. Molecular dynamics (MD) simulation provides a powerful computational approach to probe the allostery effect. However, current MD simulations cannot reach the time scales of whole allostery processes. The advent of deep learning made it possible to evaluate both spatially short and long-range communications for understanding allostery. For this purpose, we applied a neural relational inference (NRI) model based on a graph neural network (GNN), which adopts an encoder-decoder architecture to simultaneously infer latent interactions to probe protein allosteric processes as dynamic networks of interacting residues. From the MD trajectories, this model successfully learned the long-range interactions and pathways that can mediate the allosteric communications between the two distant binding sites in the Pin1, SOD1, and MEK1 systems.

Quantitative Ranking of Ligand Binding Kinetics with a Multiscale Milestoning Simulation Approach

2018 ◽  
Author(s):  
Benjamin R. Jagger ◽  
Christoper T. Lee ◽  
Rommie Amaro
Keyword(s):  
Molecular Dynamics ◽  
Drug Discovery ◽  
Small Molecule ◽  
Brownian Dynamics ◽  
Model Simulation ◽  
Computational Cost ◽  
Lead Selection ◽  
Delta G ◽  
Dynamics Simulations

<p>The ranking of small molecule binders by their kinetic (kon and koff) and thermodynamic (delta G) properties can be a valuable metric for lead selection and optimization in a drug discovery campaign, as these quantities are often indicators of in vivo efficacy. Efficient and accurate predictions of these quantities can aid the in drug discovery effort, acting as a screening step. We have previously described a hybrid molecular dynamics, Brownian dynamics, and milestoning model, Simulation Enabled Estimation of Kinetic Rates (SEEKR), that can predict kon’s, koff’s, and G’s. Here we demonstrate the effectiveness of this approach for ranking a series of seven small molecule compounds for the model system, -cyclodextrin, based on predicted kon’s and koff’s. We compare our results using SEEKR to experimentally determined rates as well as rates calculated using long-timescale molecular dynamics simulations and show that SEEKR can effectively rank the compounds by koff and G with reduced computational cost. We also provide a discussion of convergence properties and sensitivities of calculations with SEEKR to establish “best practices” for its future use.</p>

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