Dynamic properties of force fields

Vitalini, Francesca; Mey, Antonia S. J. S.; Noé, Frank; Keller, Bettina G.

doi:10.1063/1.4909549

Cited by 86 publications

(102 citation statements)

References 80 publications

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“…Additionally, with recent advances in specialized computational architectures like Anton, simulation timescales on the order of microseconds to milliseconds in a single trajectory are within reach (7,(12)(13)(14).…”

Section: Introductionmentioning

confidence: 99%

Capturing Invisible Motions in the Transition from Ground to Rare Excited States of T4 Lysozyme L99A

Schiffer

Feher

Malmstrom

et al. 2016

Biophysical Journal

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Proteins commonly sample a number of conformational states to carry out their biological function, often requiring transitions from the ground state to higher-energy states. Characterizing the mechanisms that guide these transitions at the atomic level promises to impact our understanding of functional protein dynamics and energy landscapes. The leucine-99-toalanine (L99A) mutant of T4 lysozyme is a model system that has an experimentally well characterized excited sparsely populated state as well as a ground state. Despite the exhaustive study of L99A protein dynamics, the conformational changes that permit transitioning to the experimentally detected excited state (~3%, DG~2 kcal/mol) remain unclear. Here, we describe the transitions from the ground state to this sparsely populated excited state of L99A as observed through a single molecular dynamics (MD) trajectory on the Anton supercomputer. Aside from detailing the ground-to-excited-state transition, the trajectory samples multiple metastates and an intermediate state en route to the excited state. Dynamic motions between these states enable cavity surface openings large enough to admit benzene on timescales congruent with known rates for benzene binding. Thus, these fluctuations between rare protein states provide an atomic description of the concerted motions that illuminate potential path(s) for ligand binding. These results reveal, to our knowledge, a new level of complexity in the dynamics of buried cavities and their role in creating mobile defects that affect protein dynamics and ligand binding.

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Section: Introductionmentioning

confidence: 99%

Capturing Invisible Motions in the Transition from Ground to Rare Excited States of T4 Lysozyme L99A

Schiffer

Feher

Malmstrom

et al. 2016

Biophysical Journal

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“…There are several recent studies on new methods for analyzing conformational sampling and dynamics of proteins via MD simulations which imply the importance of this field (46)(47)(48)(49)(50)(51)(52)(53), however, there is long pace remains to the attainment of the perfection (54).…”

Section: Discussionmentioning

confidence: 99%

Variability of the Cyclin-Dependent Kinase 2 Flexibility Without Significant Change in the Initial Conformation of the Protein or Its Environment; a Computational Study

2016

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Protein flexibility probably is a phenomenon that can make the existence of the many protein conformations possible in contrast to a limited number. Many conformations in a protein are needed for doing many functions. For instance, based on searching IntAct PPI database with the UniprotKB accession number of hCDK2: P24941, hCDK2 has at least 200 different interactions with other proteins in addition to interactions by itself (1). As a structural characteristic, protein flexibility plays many functional roles with respect to different aspects of the proteins as well. Examples in this regard are enzyme catalysis and ligand-receptor interactions, substrate and ligand orientation, the turnover rate of the substrates, and reduction of the free energy barrier in the active sites (2).In structural bioinformatics tools, there are many instances regarding protein flexibility applications and usefulness; as an example, improvement of the small ligand-receptor docking (3,4 Background: Protein flexibility, which has been referred as a dynamic behavior has various roles in proteins' functions. Furthermore, for some developed tools in bioinformatics, such as protein-protein docking software, considering the protein flexibility, causes a higher degree of accuracy. Through undertaking the present work, we have accomplished the quantification plus analysis of the variations in the human Cyclin Dependent Kinase 2 (hCDK2) protein flexibility without affecting a significant change in its initial environment or the protein per se. Objectives:The main goal of the present research was to calculate variations in the flexibility for each residue of the hCDK2, analysis of their flexibility variations through clustering, and to investigate the functional aspects of the residues with high flexibility variations. Materials and Methods: Using Gromacs package (version 4.5.4), three independent molecular dynamics (MD) simulations of the hCDK2 protein (PDB ID: 1HCL) was accomplished with no significant changes in their initial environments, structures, or conformations, followed by Root Mean Square Fluctuations (RMSF) calculation of these MD trajectories. The amount of variations in these three curves of RMSF was calculated using two formulas.Results: More than 50% of the variation in the flexibility (the distance between the maximum and the minimum amount of the RMSF) was found at the region of Val-154. As well, there are other major flexibility fluctuations in other residues. These residues were mostly positioned in the vicinity of the functional residues. The subsequent works were done, as followed by clustering all hCDK2 residues into four groups considering the amount of their variability with respect to flexibility and their position in the RMSF curves. Conclusions:This work has introduced a new class of flexibility aspect of the proteins' residues. It could also help designing and engineering proteins, with introducing a new dynamic aspect of hCDK2, and accordingly, for the other similar globular proteins. In addition, it could provide a...

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“…The influence of a change in the force constant on the dynamics is particularly easy to study. The Girsanov reweighting method can therefore be applied to improve the dynamical properties of force fields 37 , by for example tuning the force constants to match an experimentally measured correlation time. Similarly, one can use Girsanov reweighting to understand the influence of restraining potentials 50,51 on the dynamics of the system.…”

Section: Discussionmentioning

confidence: 99%

“…6.C. The first eigenvector shows the typical equilibrium distribution in the Ramachandran plane 37 . The second eigenvector represents a torsion around the φ angle and corresponds to a kinetic exchange between the L α -minimum (φ > 0) and the α-helix and β-sheet minima (φ < 0).…”

Section: Many-body System In Three-dimensional Spacementioning

confidence: 99%

Girsanov reweighting for path ensembles and Markov state models

Donati

Hartmann

Keller

2017

The Journal of Chemical Physics

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The sensitivity of molecular dynamics on changes in the potential energy function plays an important role in understanding the dynamics and function of complex molecules. We present a method to obtain path ensemble averages of a perturbed dynamics from a set of paths generated by a reference dynamics. It is based on the concept of path probability measure and the Girsanov theorem, a result from stochastic analysis to estimate a change of measure of a path ensemble. Since Markov state models (MSM) of the molecular dynamics can be formulated as a combined phasespace and path ensemble average, the method can be extended to reweight MSMs by combining it with a reweighting of the Boltzmann distribution. We demonstrate how to efficiently implement the Girsanov reweighting in a molecular dynamics simulation program by calculating parts of the reweighting factor "on the fly" during the simulation, and we benchmark the method on test systems ranging from a two-dimensional diffusion process to an artificial many-body system and alanine dipeptide and valine dipeptide in implicit and explicit water. The method can be used to study the sensitivity of molecular dynamics on external perturbations as well as to reweight trajectories generated by enhanced sampling schemes to the original dynamics.

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Dynamic properties of force fields

Cited by 86 publications

References 80 publications

Capturing Invisible Motions in the Transition from Ground to Rare Excited States of T4 Lysozyme L99A

Capturing Invisible Motions in the Transition from Ground to Rare Excited States of T4 Lysozyme L99A

Variability of the Cyclin-Dependent Kinase 2 Flexibility Without Significant Change in the Initial Conformation of the Protein or Its Environment; a Computational Study

Girsanov reweighting for path ensembles and Markov state models

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