Article

Coarse Master Equations for Peptide Folding Dynamics †

Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland 20892-0520, USA.
The Journal of Physical Chemistry B (Impact Factor: 3.3). 06/2008; 112(19):6057-69. DOI: 10.1021/jp0761665
Source: PubMed

ABSTRACT

We construct coarse master equations for peptide folding dynamics from atomistic molecular dynamics simulations. A maximum-likelihood propagator-based method allows us to extract accurate rates for the transitions between the different conformational states of the small helix-forming peptide Ala5. Assigning the conformational states by using transition paths instead of instantaneous molecular coordinates suppresses the effects of fast non-Markovian dynamics. The resulting master equations are validated by comparing their analytical correlation functions with those obtained directly from the molecular dynamics simulations. We find that the master equations properly capture the character and relaxation times of the entire spectrum of conformational relaxation processes. By using the eigenvectors of the transition rate matrix, we are able to systematically coarse-grain the system. We find that a two-state description, with a folded and an unfolded state, roughly captures the slow conformational dynamics. A four-state model, with two folded and two unfolded states, accurately recovers the three slowest relaxation process with time scales between 1.5 and 7 ns. The master equation models not only give access to the slow conformational dynamics but also shed light on the molecular mechanisms of the helix-coil transition.

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    • "The development of methods that allow the systematic and even automatic clustering is an actively developing area [25,52–57]. Here, we present a method based on the eigenvalue and eigenvector analysis of rate matrices [25] [58] that has the promise to offer a general framework that may be extended from the analysis of relatively small peptides to the folding of larger, more complex proteins. "

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    • "For pharmaceutical applications, effects of cyclization on preferred conformations were explored for the enkephalin DPDPE[86]and for RGD peptidomimetics and prodrugs[87,88]. More recently, microsecond-length MD simulations of short peptides have become possible, leading to characterization of the full conformational landscapes of larger systems, such as Ala 5[89,90]. MD trajectories of 2 μs length were generated for angiotensins I and II, short peptides involved in blood-pressure regulation[91]. "
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    ABSTRACT: This article presents a review of the field of molecular modeling of peptides. The main focus is on atomistic modeling with molecular mechanics potentials. The description of peptide conformations and solvation through potentials is discussed. Several important computer simulation methods are briefly introduced, including molecular dynamics, accelerated sampling approaches such as replica-exchange and metadynamics, free energy simulations and kinetic network models like Milestoning. Examples of recent applications for predictions of structure, kinetics, and interactions of peptides with complex environments are described. The reliability of current simulation methods is analyzed by comparison of computational predictions obtained using different models with each other and with experimental data. A brief discussion of coarse-grained modeling and future directions is also presented.
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    • "One of these approach is Markov State Modelling in which the kinetics of a molecular system is described by a Markov jump process or Markov chain with the dominant metastable conformations of a molecular system as Markov states [17] [18] [15]. In recent years Markov State Modelling has been applied with striking success to many different molecular systems like peptides including time-resolved spectroscopic experiments [3] [14] [9], proteins and protein folding [5] [11] [2], DNA [8], and ligand-receptor interaction in drug design [7] [4] and more complicated multivalent scenarios [23] [19]. "
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