Technical advances in molecular simulation since the 1980s

Field, Martin J.

doi:10.1016/j.abb.2015.03.005

Cited by 13 publications

(12 citation statements)

References 69 publications

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“…Molecular simulations applied to biology are a powerful technique to integrate experimental studies, as attested by the Nobel Prize in Chemistry awarded to Martin Karplus, Micheal Levitt, and Arieh Warshel in 2013 for “the development of multiscale models for complex chemical systems.” Among the large array of simulation techniques that can cover the definition of a multiscale approach, I will here focus on atomistic classical molecular dynamics (MD) approaches. Other approaches that are important component of a multiscale approach are extensively covered in many review articles in the field (such as Rapaport, 1998 ; Koehl and Levitt, 1999 ; Warshel, 2003 ; Snow et al, 2004 ; Xia and Levitt, 2004 ; Karplus and Kuriyan, 2005 ; Sherwood et al, 2008 ; Schlick, 2010 ; Dror et al, 2012 ; Saunders and Voth, 2013 ; Salvatella, 2014 ; Field, 2015 ) and are not the focus of this perspective article.…”

Section: An Atom-scale View On Protein Structure and Dynamics By Molementioning

confidence: 99%

Integrating atomistic molecular dynamics simulations, experiments, and network analysis to study protein dynamics: strength in unity

Papaleo

2015

Front. Mol. Biosci.

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In the last years, we have been observing remarkable improvements in the field of protein dynamics. Indeed, we can now study protein dynamics in atomistic details over several timescales with a rich portfolio of experimental and computational techniques. On one side, this provides us with the possibility to validate simulation methods and physical models against a broad range of experimental observables. On the other side, it also allows a complementary and comprehensive view on protein structure and dynamics. What is needed now is a better understanding of the link between the dynamic properties that we observe and the functional properties of these important cellular machines. To make progresses in this direction, we need to improve the physical models used to describe proteins and solvent in molecular dynamics, as well as to strengthen the integration of experiments and simulations to overcome their own limitations. Moreover, now that we have the means to study protein dynamics in great details, we need new tools to understand the information embedded in the protein ensembles and in their dynamic signature. With this aim in mind, we should enrich the current tools for analysis of biomolecular simulations with attention to the effects that can be propagated over long distances and are often associated to important biological functions. In this context, approaches inspired by network analysis can make an important contribution to the analysis of molecular dynamics simulations.

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Section: An Atom-scale View On Protein Structure and Dynamics By Molementioning

confidence: 99%

Integrating atomistic molecular dynamics simulations, experiments, and network analysis to study protein dynamics: strength in unity

Papaleo

2015

Front. Mol. Biosci.

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“…Providing information about molecular properties of drugs (ADME/T, PK/PD model, chemical space analysis and drug-likeness evaluation) [1][2][3][4][32][33][34][35][36] Identification of drug-target interaction Predicting and evaluating drug/compound-target interaction (molecular docking, molecular dynamics simulation, machine learning and similarity analysis) [37][38][39][40][41][42] Network level Drug-target network analysis…”

Section: Molecular Level Evaluation Of Molecular Characteristicsmentioning

confidence: 99%

Computational methods and applications for quantitative systems pharmacology

Xie

2019

Quant. Biol.

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Background: Quantitative systems pharmacology (QSP) is an emerging discipline that integrates diverse data to quantitatively explore the interactions between drugs and multi-scale systems including small compounds, nucleic acids, proteins, pathways, cells, organs and disease processes. Results: Various computational methods such as ADME/T evaluation, molecular modeling, logical modeling, network modeling, pathway analysis, multi-scale systems pharmacology platforms and virtual patient for QSP have been developed. We reviewed the major progresses and broad applications in medical guidance, drug discovery and exploration of pharmacodynamic material basis and mechanism of traditional Chinese medicine. Conclusion: QSP has significant achievements in recent years and is a promising approach for quantitative evaluation of drug efficacy and systematic exploration of mechanisms of action of drugs.Author summary: Quantitative systems pharmacology (QSP) is an emerging discipline that integrates diverse data to quantitatively explore the interactions between drugs and multi-scale systems including small compounds, nucleic acids, proteins, pathways, cells, organs and disease processes. This review is an attempt to introduce the computational methods for QSP, including ADME/T (absorption, distribution, metabolism, excretion and toxicity) evaluation, molecular modeling, logical modeling, network modeling, pathway analysis, multi-scale systems pharmacology platforms and virtual patient as well as their applications in medical guidance, drug discovery and explorations of pharmacodynamics material basis and mechanism of traditional Chinese medicine.

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“…In particular, molecular dynamics (MD) methods have facilitated the study of systems consisting of millions of particles 8 and timescales up to milliseconds 9 . These milestones in MD simulations have been achieved by promoting two facts: the intrinsic highly parallel environment enabled by the MD algorithm and the development of new optimized algorithms for several MD systems [10][11][12][13][14] . Out of equilibrium (inhomogeneous) and multiscale simulations constitute recent milestones in molecular simulations of soft matter which have driven in the past few years the development of new methods aiming to provide a physically consistent treatment, highly accurate and computationally efficient molecular modeling.…”

Section: Introductionmentioning

confidence: 99%

Scalable and fast heterogeneous molecular simulation with predictive parallelization schemes

et al. 2017

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Multiscale and inhomogeneous molecular systems are challenging topics in the field of molecular simulation. In particular, modeling biological systems in the context of multiscale simulations and exploring material properties are driving a permanent development of new simulation methods and optimization algorithms. In computational terms, those methods require parallelization schemes that make a productive use of computational resources for each simulation and from its genesis. Here, we introduce the heterogeneous domain decomposition approach which is a combination of an heterogeneity sensitive spatial domain decomposition with an a priori rearrangement of subdomainwalls. Within this approach, the theoretical modeling and scaling-laws for the force computation time are proposed and studied as a function of the number of particles and the spatial resolution ratio. We also show the new approach capabilities, by comparing it to both static domain decomposition algorithms and dynamic load balancing schemes. Specifically, two representative molecular systems have been simulated and compared to the heterogeneous domain decomposition proposed in this work. These two systems comprise an adaptive resolution simulation of a biomolecule solvated in water and a phase separated binary Lennard-Jones fluid.

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Technical advances in molecular simulation since the 1980s

Cited by 13 publications

References 69 publications

Integrating atomistic molecular dynamics simulations, experiments, and network analysis to study protein dynamics: strength in unity

Integrating atomistic molecular dynamics simulations, experiments, and network analysis to study protein dynamics: strength in unity

Computational methods and applications for quantitative systems pharmacology

Scalable and fast heterogeneous molecular simulation with predictive parallelization schemes

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