Improving the Accuracy of Protein-Ligand Binding Mode Prediction Using a Molecular Dynamics-Based Pocket Generation Approach

2018 ◽  
Vol 39 (32) ◽  
pp. 2679-2689 ◽  
Author(s):  
Mitsugu Araki ◽  
Hiroaki Iwata ◽  
Biao Ma ◽  
Atsuto Fujita ◽  
Kei Terayama ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Ligand Binding ◽  
Binding Mode ◽  
Binding Mode Prediction

A boosted unbiased molecular dynamics method for predicting ligands binding mechanisms: probing the binding pathway of dasatinib to Src-kinase

2020 ◽  
Vol 36 (18) ◽  
pp. 4714-4720
Author(s):  
Farzin Sohraby ◽  
Mostafa Javaheri Moghadam ◽  
Masoud Aliyar ◽  
Hassan Aryapour
Keyword(s):  
Molecular Dynamics ◽  
Ligand Binding ◽  
Md Simulation ◽  
Src Kinase ◽  
Deep Understanding ◽  
Md Simulations ◽  
Binding Mode ◽  
Supplementary Information ◽  
Computational Resources ◽  
Binding Mechanisms

Abstract Summary Small molecules such as metabolites and drugs play essential roles in biological processes and pharmaceutical industry. Knowing their interactions with biomacromolecular targets demands a deep understanding of binding mechanisms. Dozens of papers have suggested that discovering of the binding event by means of conventional unbiased molecular dynamics (MD) simulation urges considerable amount of computational resources, therefore, only one who holds a cluster or a supercomputer can afford such extensive simulations. Thus, many researchers who do not own such resources are reluctant to take the benefits of running unbiased MD simulation, in full atomistic details, when studying a ligand binding pathway. Many researchers are impelled to be content with biased MD simulations which seek its validation due to its intrinsic preconceived framework. In this work, we have presented a workable stratagem to encourage everyone to perform unbiased (unguided) MD simulations, in this case a protein–ligand binding process, by typical desktop computers and so achieve valuable results in nanosecond time scale. Here, we have described a dynamical binding’s process of an anticancer drug, the dasatinib, to the c-Src kinase in full atomistic details for the first time, without applying any biasing force or potential which may lead the drug to artificial interactions with the protein. We have attained multiple independent binding events which occurred in the nanosecond time scales, surprisingly as little as ∼30 ns. Both the protonated and deprotonated forms of the dasatinib reached the crystallographic binding mode without having any major intermediate state during induction. Availability and implementation The links of the tutorial and technical documents are accessible in the article. Supplementary information Supplementary data are available at Bioinformatics online.

Fragment Pose Prediction Using Non-Equilibrium Candidate Monte Carlo and Molecular Dynamics Simulations

2019 ◽  
Author(s):  
Nathan M. Lim ◽  
Meghan Osato ◽  
Gregory L. Warren ◽  
David L. Mobley
Keyword(s):  
Crystal Structure ◽  
Molecular Dynamics ◽  
Monte Carlo ◽  
Ligand Binding ◽  
Md Simulations ◽  
Binding Mode ◽  
Protein Crystal ◽  
Pose Prediction ◽  
Binding Modes ◽  
Non Equilibrium

<div>Part of early stage drug discovery involves determining how molecules may bind to the target protein. Through understanding where and how molecules bind, chemists can begin to build ideas on how to design improvements to increase binding affinities. In this retrospective study, we compare how computational approaches like docking, molecular dynamics (MD) simulations, and a non-equilibrium candidate Monte Carlo (NCMC) based method (NCMC+MD) perform in predicting binding modes for a set of 12 fragment-like molecules which bind to soluble epoxide hydrolase. We evaluate each method's effectiveness in identifying the dominant binding mode and finding any additional binding modes (if any). Then, we compare our predicted binding modes to experimentally obtained X-ray crystal structures.</div><div>We dock each of the 12 small molecules into the apo-protein crystal structure and then run simulations up to 1 microsecond each. Small and fragment-like molecules likely have smaller energy barriers separating different binding modes by virtue of relatively fewer and weaker interactions relative to drug-like molecules, and thus likely undergo more rapid binding mode transitions. We expect, thus, to see more rapid transitions betweeen binding modes in our study. </div><div><br></div><div>Following this, we build Markov State Models (MSM) to define our stable ligand binding modes. We investigate if adequate sampling of ligand binding modes and transitions between them can occur at the microsecond timescale using traditional MD or a hybrid NCMC+MD simulation approach. Our findings suggest that even with small fragment-like molecules, we fail to sample all the crystallographic binding modes using microsecond MD simulations, but using NCMC+MD we have better success in sampling the crystal structure while obtaining the correct populations.</div>

Flavin oxidation state impacts on nitrofuran antibiotic binding orientation in nitroreductases

2021 ◽  
Vol 478 (18) ◽  
pp. 3423-3428
Author(s):  
Helen S. Toogood ◽  
Nigel S. Scrutton
Keyword(s):  
Molecular Dynamics ◽  
Ligand Binding ◽  
Molecular Dynamics Simulations ◽  
Oxidation State ◽  
Binding Mode ◽  
Kinetic Mechanism ◽  
Binding Orientation ◽  
Nitrofuran Antibiotics ◽  
Dynamics Simulations ◽  
Binding Orientations

Nitroreductases catalyse the NAD(P)H-dependent nitro reduction in nitrofuran antibiotics, which activates them into cytotoxic molecules leading to cell death. The design of new effective nitrofuran antibiotics relies on knowledge of the kinetic mechanism and nitrofuran binding mode of microbial nitroreductases NfsA and NfsB. This has been hampered by multiple co-crystallisation studies revealing ligand binding in non-electron transfer competent states. In a recent study by Day et al. (2021) the authors investigated the likely reaction mechanism and mode of nitrofurantoin binding to NfsA using potentiometry, global kinetics analysis, crystallography and molecular dynamics simulations. Their findings suggest nitrofurantoin reduction proceeds via a direct hydride transfer from reduced FMN, while the crystallographic binding orientation is an inhibitory complex. Molecular dynamics simulations suggest ligand binding orientations is dependent on the oxidation state of the FMN. This study highlights the importance of utilising computational studies alongside traditional crystallographic approaches, when multiple stable ligand binding orientations can occur.

Fragment Pose Prediction Using Non-Equilibrium Candidate Monte Carlo and Molecular Dynamics Simulations

2019 ◽  
Author(s):  
Nathan M. Lim ◽  
Meghan Osato ◽  
Gregory L. Warren ◽  
David L. Mobley
Keyword(s):  
Crystal Structure ◽  
Molecular Dynamics ◽  
Monte Carlo ◽  
Ligand Binding ◽  
Md Simulations ◽  
Binding Mode ◽  
Protein Crystal ◽  
Pose Prediction ◽  
Binding Modes ◽  
Non Equilibrium

<div>Part of early stage drug discovery involves determining how molecules may bind to the target protein. Through understanding where and how molecules bind, chemists can begin to build ideas on how to design improvements to increase binding affinities. In this retrospective study, we compare how computational approaches like docking, molecular dynamics (MD) simulations, and a non-equilibrium candidate Monte Carlo (NCMC) based method (NCMC+MD) perform in predicting binding modes for a set of 12 fragment-like molecules which bind to soluble epoxide hydrolase. We evaluate each method's effectiveness in identifying the dominant binding mode and finding any additional binding modes (if any). Then, we compare our predicted binding modes to experimentally obtained X-ray crystal structures.</div><div>We dock each of the 12 small molecules into the apo-protein crystal structure and then run simulations up to 1 microsecond each. Small and fragment-like molecules likely have smaller energy barriers separating different binding modes by virtue of relatively fewer and weaker interactions relative to drug-like molecules, and thus likely undergo more rapid binding mode transitions. We expect, thus, to see more rapid transitions betweeen binding modes in our study. </div><div><br></div><div>Following this, we build Markov State Models (MSM) to define our stable ligand binding modes. We investigate if adequate sampling of ligand binding modes and transitions between them can occur at the microsecond timescale using traditional MD or a hybrid NCMC+MD simulation approach. Our findings suggest that even with small fragment-like molecules, we fail to sample all the crystallographic binding modes using microsecond MD simulations, but using NCMC+MD we have better success in sampling the crystal structure while obtaining the correct populations.</div>

scholarly journals Can molecular dynamics simulations improve the structural accuracy and virtual screening performance of GPCR models?

2021 ◽  
Vol 17 (5) ◽  
pp. e1008936
Author(s):  
Jon Kapla ◽  
Ismael Rodriguez Espigares ◽  
Flavio Ballante ◽  
Jana Selent ◽  
Jens Carlsson
Keyword(s):  
Crystal Structure ◽  
Molecular Dynamics ◽  
Virtual Screening ◽  
Ligand Binding ◽  
Md Simulations ◽  
Binding Mode ◽  
Extracellular Loop ◽  
Loop Region ◽  
Screening Performance ◽  
Virtual Screening Performance

The determination of G protein-coupled receptor (GPCR) structures at atomic resolution has improved understanding of cellular signaling and will accelerate the development of new drug candidates. However, experimental structures still remain unavailable for a majority of the GPCR family. GPCR structures and their interactions with ligands can also be modelled computationally, but such predictions have limited accuracy. In this work, we explored if molecular dynamics (MD) simulations could be used to refine the accuracy of in silico models of receptor-ligand complexes that were submitted to a community-wide assessment of GPCR structure prediction (GPCR Dock). Two simulation protocols were used to refine 30 models of the D3 dopamine receptor (D3R) in complex with an antagonist. Close to 60 μs of simulation time was generated and the resulting MD refined models were compared to a D3R crystal structure. In the MD simulations, the transmembrane helix region of the models generally drifted further away from the crystal structure conformation. However, MD refinement was able to improve the accuracy of the ligand binding mode and the second extracellular loop region. The best refinement protocol improved agreement with the experimentally observed ligand binding mode for a majority of the models. Receptor structures with improved virtual screening performance, which was assessed by molecular docking of ligands and decoys, could also be identified among the MD refined models. Application of weak restraints to the transmembrane helixes in the MD simulations further improved predictions of the ligand binding mode and second extracellular loop. These results provide guidelines for application of MD refinement in prediction of GPCR-ligand complexes and directions for further method development.

scholarly journals A boosted unbiased molecular dynamics method for predicting ligands binding mechanisms: Probing the binding pathway of dasatinib to Src-kinase

2019 ◽  
Author(s):  
Farzin Sohraby ◽  
Mostafa Javaheri Moghadam ◽  
Masoud Aliyar ◽  
Hassan Aryapour
Keyword(s):  
Molecular Dynamics ◽  
Ligand Binding ◽  
Molecular Dynamics Simulations ◽  
Src Kinase ◽  
Deep Understanding ◽  
Binding Mode ◽  
Dynamics Simulation ◽  
Dynamics Simulations ◽  
Computational Resources ◽  
Binding Mechanisms

AbstractSmall molecules such as metabolites and drugs play essential roles in biological processes and pharmaceutical industry. Knowing their interactions with biomacromolecular targets demands a deep understanding of binding mechanisms. Dozens of papers have suggested that discovering of the binding event by means of conventional unbiased molecular dynamics (MD) simulation urges considerable amount of computational resources, therefore, only one who holds a cluster or a supercomputer can afford such extensive simulations. Thus, many researchers who do not own such resources are reluctant to take the benefits of running unbiased molecular dynamics simulation, in full atomistic details, when studying a ligand binding pathway. Many researchers are impelled to be content with biased molecular dynamics simulations which seek its validation due to its intrinsic preconceived framework. In this work, we have presented a workable stratagem to encourage everyone to perform unbiased (unguided) molecular dynamics simulations, in this case a protein-ligand binding process, by typical desktop computers and so achieve valuable results in nanosecond time scale. Here, we have described a dynamical binding’s process of an anticancer drug, the dasatinib, to the c-Src kinase in full atomistic details for the first time, without applying any biasing force or potential which may lead the drug to artificial interactions with the protein. We have attained multiple independent binding events which occurred in the nano-second timescales, surprisingly as little as ∼30 ns. Both the protonated and deprotonated forms of the dasatinib reached the crystallographic binding mode without having any major intermediate state during induction.

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