scholarly journals Clustering Molecular Dynamics Trajectories for Optimizing Docking Experiments

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Renata De Paris ◽  
Christian V. Quevedo ◽  
Duncan D. Ruiz ◽  
Osmar Norberto de Souza ◽  
Rodrigo C. Barros
Keyword(s):  
Molecular Dynamics ◽  
Substrate Binding ◽  
Clustering Algorithms ◽  
Computational Cost ◽  
Structural Features ◽  
Dynamics Simulation ◽  
Rational Drug ◽  
Protein Receptors ◽  
Binding Cavity ◽  
Dynamics Simulations

Molecular dynamics simulations of protein receptors have become an attractive tool for rational drug discovery. However, the high computational cost of employing molecular dynamics trajectories in virtual screening of large repositories threats the feasibility of this task. Computational intelligence techniques have been applied in this context, with the ultimate goal of reducing the overall computational cost so the task can become feasible. Particularly, clustering algorithms have been widely used as a means to reduce the dimensionality of molecular dynamics trajectories. In this paper, we develop a novel methodology for clustering entire trajectories using structural features from the substrate-binding cavity of the receptor in order to optimize docking experiments on a cloud-based environment. The resulting partition was selected based on three clustering validity criteria, and it was further validated by analyzing the interactions between 20 ligands and a fully flexible receptor (FFR) model containing a 20 ns molecular dynamics simulation trajectory. Our proposed methodology shows that taking into account features of the substrate-binding cavity as input for thek-means algorithm is a promising technique for accurately selecting ensembles of representative structures tailored to a specific ligand.

scholarly journals Assessment of software methods for estimating protein-protein relative binding affinities

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0240573
Author(s):  
Tawny R. Gonzalez ◽  
Kyle P. Martin ◽  
Jonathan E. Barnes ◽  
Jagdish Suresh Patel ◽  
F. Marty Ytreberg
Keyword(s):  
Molecular Dynamics ◽  
Computational Cost ◽  
Structural Features ◽  
New Drugs ◽  
Binding Affinities ◽  
Evolutionary Mechanisms ◽  
Relative Binding ◽  
Amino Acid Mutations ◽  
Dynamics Simulations ◽  
The Impact

A growing number of computational tools have been developed to accurately and rapidly predict the impact of amino acid mutations on protein-protein relative binding affinities. Such tools have many applications, for example, designing new drugs and studying evolutionary mechanisms. In the search for accuracy, many of these methods employ expensive yet rigorous molecular dynamics simulations. By contrast, non-rigorous methods use less exhaustive statistical mechanics, allowing for more efficient calculations. However, it is unclear if such methods retain enough accuracy to replace rigorous methods in binding affinity calculations. This trade-off between accuracy and computational expense makes it difficult to determine the best method for a particular system or study. Here, eight non-rigorous computational methods were assessed using eight antibody-antigen and eight non-antibody-antigen complexes for their ability to accurately predict relative binding affinities (ΔΔG) for 654 single mutations. In addition to assessing accuracy, we analyzed the CPU cost and performance for each method using a variety of physico-chemical structural features. This allowed us to posit scenarios in which each method may be best utilized. Most methods performed worse when applied to antibody-antigen complexes compared to non-antibody-antigen complexes. Rosetta-based JayZ and EasyE methods classified mutations as destabilizing (ΔΔG < -0.5 kcal/mol) with high (83–98%) accuracy and a relatively low computational cost for non-antibody-antigen complexes. Some of the most accurate results for antibody-antigen systems came from combining molecular dynamics with FoldX with a correlation coefficient (r) of 0.46, but this was also the most computationally expensive method. Overall, our results suggest these methods can be used to quickly and accurately predict stabilizing versus destabilizing mutations but are less accurate at predicting actual binding affinities. This study highlights the need for continued development of reliable, accessible, and reproducible methods for predicting binding affinities in antibody-antigen proteins and provides a recipe for using current methods.

scholarly journals Assessment of Software Methods for Estimating Protein-Protein Relative Binding Affinities

2020 ◽  
Author(s):  
Tawny R. Gonzalez ◽  
Kyle P. Martin ◽  
Jonathan E. Barnes ◽  
Jagdish Suresh Patel ◽  
F. Marty Ytreberg
Keyword(s):  
Molecular Dynamics ◽  
Computational Cost ◽  
Structural Features ◽  
New Drugs ◽  
Binding Affinities ◽  
Evolutionary Mechanisms ◽  
Relative Binding ◽  
Amino Acid Mutations ◽  
Dynamics Simulations ◽  
The Impact

AbstractA growing number of computational tools have been developed to accurately and rapidly predict the impact of amino acid mutations on protein-protein relative binding affinities. Such tools have many applications, for example, designing new drugs and studying evolutionary mechanisms. In the search for accuracy, many of these methods employ expensive yet rigorous molecular dynamics simulations. By contrast, non-rigorous methods use less exhaustive statistical mechanics, allowing for more efficient calculations. However, it is unclear if such methods retain enough accuracy to replace rigorous methods in binding affinity calculations. This trade-off between accuracy and computational expense makes it difficult to determine the best method for a particular system or study. Here, eight non-rigorous computational methods were assessed using eight antibody-antigen and eight non-antibody-antigen complexes for their ability to accurately predict relative binding affinities (ΔΔG) for 654 single mutations. In addition to assessing accuracy, we analyzed the CPU cost and performance for each method using a variety of physico-chemical structural features. This allowed us to posit scenarios in which each method may be best utilized. Most methods performed worse when applied to antibody-antigen complexes compared to non-antibody-antigen complexes. Rosetta-based JayZ and EasyE methods classified mutations as destabilizing (ΔΔG < −0.5 kcal/mol) with high (83-98%) accuracy and a relatively low computational cost for non-antibody-antigen complexes. Some of the most accurate results for antibody-antigen systems came from combining molecular dynamics with FoldX with a correlation coefficient (r) of 0.46, but this was also the most computationally expensive method. Overall, our results suggest these methods can be used to quickly and accurately predict stabilizing versus destabilizing mutations but are less accurate at predicting actual binding affinities. This study highlights the need for continued development of reliable, accessible, and reproducible methods for predicting binding affinities in antibody-antigen proteins and provides a recipe for using current methods.

Parameterization of Divalent Cations for Coarse-Grained Simulations

2020 ◽  
Author(s):  
Florencia Klein ◽  
Daniela Cáceres-Rojas ◽  
Monica Carrasco ◽  
Juan Carlos Tapia ◽  
Julio Caballero ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Metal Ions ◽  
Molecular Dynamics Simulations ◽  
Divalent Cations ◽  
Computational Cost ◽  
Data Bank ◽  
Coarse Grained ◽  
Interaction Parameters ◽  
Dynamics Simulations ◽  
Dynamical Description

<p>Although molecular dynamics simulations allow for the study of interactions among virtually all biomolecular entities, metal ions still pose significant challenges to achieve an accurate structural and dynamical description of many biological assemblies. This is particularly the case for coarse-grained (CG) models. Although the reduced computational cost of CG methods often makes them the technique of choice for the study of large biomolecular systems, the parameterization of metal ions is still very crude or simply not available for the vast majority of CG- force fields. Here, we show that incorporating statistical data retrieved from the Protein Data Bank (PDB) to set specific Lennard-Jones interactions can produce structurally accurate CG molecular dynamics simulations. Using this simple approach, we provide a set of interaction parameters for Calcium, Magnesium, and Zinc ions, which cover more than 80% of the metal-bound structures reported on the PDB. Simulations performed using the SIRAH force field on several proteins and DNA systems show that using the present approach it is possible to obtain non-bonded interaction parameters that obviate the use of topological constraints. </p>

Active Learning Accelerates Ab Initio Molecular Dynamics on Pericyclic Reactive Energy Surfaces

2020 ◽  
Author(s):  
Shi Jun Ang ◽  
Wujie Wang ◽  
Daniel Schwalbe-Koda ◽  
Simon Axelrod ◽  
Rafael Gomez-Bombarelli
Keyword(s):  
Molecular Dynamics ◽  
Potential Energy ◽  
Transition State ◽  
Potential Energy Surface ◽  
Ab Initio ◽  
Energy Surface ◽  
Computational Cost ◽  
Reactive Trajectories ◽  
Dynamics Simulations ◽  
Energy Surfaces

<div>Modeling dynamical effects in chemical reactions, such as post-transition state bifurcation, requires <i>ab initio</i> molecular dynamics simulations due to the breakdown of simpler static models like transition state theory. However, these simulations tend to be restricted to lower-accuracy electronic structure methods and scarce sampling because of their high computational cost. Here, we report the use of statistical learning to accelerate reactive molecular dynamics simulations by combining high-throughput ab initio calculations, graph-convolution interatomic potentials and active learning. This pipeline was demonstrated on an ambimodal trispericyclic reaction involving 8,8-dicyanoheptafulvene and 6,6-dimethylfulvene. With a dataset size of approximately</div><div>31,000 M062X/def2-SVP quantum mechanical calculations, the computational cost of exploring the reactive potential energy surface was reduced by an order of magnitude. Thousands of virtually costless picosecond-long reactive trajectories suggest that post-transition state bifurcation plays a minor role for the reaction in vacuum. Furthermore, a transfer-learning strategy effectively upgraded the potential energy surface to higher</div><div>levels of theory ((SMD-)M06-2X/def2-TZVPD in vacuum and three other solvents, as well as the more accurate DLPNO-DSD-PBEP86 D3BJ/def2-TZVPD) using about 10% additional calculations for each surface. Since the larger basis set and the dynamic correlation capture intramolecular non-covalent interactions more accurately, they uncover longer lifetimes for the charge-separated intermediate on the more accurate potential energy surfaces. The character of the intermediate switches from entropic to thermodynamic upon including implicit solvation effects, with lifetimes increasing with solvent polarity. Analysis of 2,000 reactive trajectories on the chloroform PES shows a qualitative agreement with the experimentally-reported periselectivity for this reaction. This overall approach is broadly applicable and opens a door to the study of dynamical effects in larger, previously-intractable reactive systems.</div>

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>

Multicanonical Molecular Dynamics Simulation Study of the Liquid-Solid and Solid-Solid Transitions in Lennard-Jones Clusters

2011 ◽  
Author(s):  
Toshihiro Kaneko ◽  
Kenji Yasuoka ◽  
Ayori Mitsutake ◽  
Xiao Cheng Zeng
Keyword(s):  
Molecular Dynamics ◽  
Heat Capacity ◽  
Transition Temperature ◽  
Peak Position ◽  
Temperature Curve ◽  
Dynamics Simulation ◽  
Lennard Jones ◽  
Dynamics Simulations ◽  
First Time ◽  
Good Agreement

Multicanonical molecular dynamics simulations are applied, for the first time, to study the liquid-solid and solid-solid transitions in Lennard-Jones (LJ) clusters. The transition temperatures are estimated based on the peak position in the heat capacity versus temperature curve. For LJ31, LJ58 and LJ98, our results on the solid-solid transition temperature are in good agreement with previous ones. For LJ309, the predicted liquid-solid transition temperature is also in agreement with previous result.

scholarly journals Reactive molecular dynamics simulation of the high-temperature pyrolysis of 2,2′,2′′,4,4′,4′′,6,6′,6′′-nonanitro-1,1′:3′,1′′-terphenyl (NONA)

RSC Advances ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 5507-5515
Author(s):  
Liang Song ◽  
Feng-Qi Zhao ◽  
Si-Yu Xu ◽  
Xue-Hai Ju
Keyword(s):  
Molecular Dynamics ◽  
High Temperature ◽  
Molecular Dynamics Simulation ◽  
Molecular Dynamics Simulations ◽  
Dynamics Simulation ◽  
Reactive Molecular Dynamics ◽  
Ring Compounds ◽  
Fused Ring ◽  
Dynamics Simulations

The bimolecular and fused ring compounds are found in the high-temperature pyrolysis of NONA using ReaxFF molecular dynamics simulations.

Molecular Dynamics Simulation Study of Lecithin Inhibiting Hydrate-Dissociation

2017 ◽  
Vol 890 ◽  
pp. 252-259
Author(s):  
Le Wang ◽  
Guan Cheng Jiang ◽  
Xin Lin ◽  
Xian Min Zhang ◽  
Qi Hui Jiang
Keyword(s):  
Molecular Dynamics ◽  
Water Movement ◽  
Simulation Analysis ◽  
Mass Density ◽  
Dynamics Simulation ◽  
Dissociation Process ◽  
Hydrate Dissociation ◽  
Hydrocarbon Chains ◽  
Dynamics Simulations ◽  
Mass Density Profile

Molecular dynamics simulations are used to study the dissociation inhibiting mechanism of lecithin for structure I hydrates. Adsorption characteristics of lecithin and PVP (poly (N-vinylpyrrolidine)) on the hydrate surfaces were performed in the NVT ensemble at temperatures of 277K and the hydrate dissociation process were simulated in the NPT ensemble at same temperature. The results show that hydrate surfaces with lecithin is more stable than the ones with PVP for the lower potential energy. The conformation of lecithin changes constantly after the balanced state is reached while the PVP molecular dose not. Lecithin molecule has interaction with lecithin nearby and hydrocarbon-chains of lecithin molecules will form a network to prevent the diffusion of water and methane molecules, which will narrow the available space for hydrate methane and water movement. Compared with PVP-hydrate simulation, analysis results (snapshots and mass density profile) of the dissociation simulations show that lecithin-hydrate dissociates more slowly.

Sign in / Sign up

Export Citation Format

Share Document