Application of Molecular-Dynamics Based Markov State Models to Functional Proteins

Malmstrom, Robert D.; Lee, Christopher T.; Wart, Adam T. Van; Amaro, Rommie E.

doi:10.1021/ct5002363

Cited by 105 publications

(89 citation statements)

References 63 publications

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“…Aiming to improve upon these methods, Markov State Modelling (MSM) based sampling approaches have emerged as an exciting method for sampling protein dynamics on millisecond timescales 2,[35][36][37][38][39][40][41] . These techniques may be used to sample conformational space using a series of relatively short MD trajectories.…”

Section: Introductionmentioning

confidence: 99%

Dynamic design: manipulation of millisecond timescale motions on the energy landscape of Cyclophilin A

Juárez-Jiménez

Gupta

Karunanithy

et al. 2018

Preprint

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Proteins need to interconvert between many conformations in order to function, many of which are formed transiently, and sparsely populated. Particularly when the lifetimes of these states approach the millisecond timescale, identifying the relevant structures and the mechanism by which they inter-convert remains a tremendous challenge. Here we introduce a novel combination of accelerated MD (aMD) simulations and Markov State modelling (MSM) to explore these 'excited' conformational states . Applying this to the highly dynamic protein CypA, a protein involved in immune response and associated with HIV infection, we identify five principally populated conformational states and the atomistic mechanism by which they interconvert. A rational design strategy predicted that the mutant D66A should stabilise the minor conformations and substantially alter the dynamics whereas the similar mutant H70A should leave the landscape broadly unchanged. These predictions are confirmed using CPMG and R1ρ solution state NMR measurements. By accurately and reliably exploring functionally relevant, but sparsely populated conformations with milli-second lifetimes in silico, our aMD/MSM method has tremendous promise for the design of dynamic protein free energy landscapes for both protein engineering and drug discovery.

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Section: Introductionmentioning

confidence: 99%

Dynamic design: manipulation of millisecond timescale motions on the energy landscape of Cyclophilin A

Juárez-Jiménez

Gupta

Karunanithy

et al. 2018

Preprint

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“…Evaluations of the relative merits of different force fields have also been conducted by comparing order parameters from simulations to experiments . Such comparisons should also be a valuable means of testing sampling methods …”

Section: Introductionmentioning

confidence: 99%

Accurately modeling nanosecond protein dynamics requires at least microseconds of simulation

Bowman

2015

J Comput Chem

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Advances in hardware and algorithms have greatly extended the timescales accessible to molecular simulation. This article assesses whether such long timescale simulations improve our ability to calculate order parameters that describe conformational heterogeneity on ps-ns timescales or if force fields are now a limiting factor. Order parameters from experiment are compared with order parameters calculated in three different ways from simulations ranging from 10 ns to 100 μs in length. Importantly, bootstrapping is employed to assess the variability in results for each simulation length. The results of 10-100 ns timescale simulations are highly variable, possibly explaining the variation in levels of agreement between simulation and experiment in published works examining different proteins. Fortunately, microsecond timescale simulations improve both the accuracy and precision of calculated order parameters, at least so long as the full exponential fit or truncated average approximation is used instead of the common long-time limit approximation. The improved precision of these long timescale simulations allows a statistically sound comparison of a number of modern force fields, such as Amber03, Amber99sb-ILDN, and Charmm27. While there is some variation between these force fields, they generally give very similar results for sufficiently long simulations. The fact that so much simulation is required to precisely capture ps-ns timescale processes suggests that extremely extensive simulations are required for slower processes. Advanced sampling techniques could aid greatly, however, such methods will need to maintain accurate kinetics if they are to be of value for calculating dynamical properties like order parameters. © 2015 Wiley Periodicals, Inc.

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“…These models were generated by complementarily employing principal component and Markov clustering analysis 25,28,32,42,48,49 .…”

Section: Resultsmentioning

confidence: 99%

“…Extensive molecular dynamics (MD) simulations were integrated using Markov state model theory to explore PKA’s conformational space and long-timescale dynamics. Markov state models depict the interaction dynamics of discrete interconnected states as a transition probability matrix, at a fixed lag time, assuming that the transitions between states are independent of previous transitions (i.e., Markovian) 25–28 . By assigning individual frames extracted from MD trajectories to discrete conformational states, sampling from many separate trajectories can be integrated into one coherent framework that captures the kinetics and thermodynamics of the conformational ensemble at atomic resolution.…”

Section: Introductionmentioning

confidence: 99%

Allostery through the computational microscope: cAMP activation of a canonical signalling domain

et al. 2015

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Ligand-induced protein allostery plays a central role in modulating cellular signaling pathways. Here, using the conserved cyclic-nucleotide binding domain of protein kinase A’s (PKA) regulatory subunit as a prototype signaling unit, we combine long-timescale, all-atom molecular dynamics simulations with Markov state models to elucidate the conformational ensembles of PKA’s cyclic-nucleotide binding domain A for the cAMP-free (apo) and cAMP-bound states. We find that both systems exhibit shallow free-energy landscapes that link functional states through multiple transition pathways. This observation suggests conformational selection as the general mechanism of allostery in this canonical signaling domain. Further, we expose the propagation of the allosteric signal through key structural motifs in the cyclic-nucleotide binding domain and explore the role of kinetics in its function. Our approach integrates disparate lines of experimental data into one cohesive framework to understand structure, dynamics, and function in complex biological systems.

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Application of Molecular-Dynamics Based Markov State Models to Functional Proteins

Cited by 105 publications

References 63 publications

Dynamic design: manipulation of millisecond timescale motions on the energy landscape of Cyclophilin A

Dynamic design: manipulation of millisecond timescale motions on the energy landscape of Cyclophilin A

Accurately modeling nanosecond protein dynamics requires at least microseconds of simulation

Allostery through the computational microscope: cAMP activation of a canonical signalling domain

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