scholarly journals Insights into Rational Design of a New Class of Allosteric Effectors with Molecular Dynamics Markov State Models and Network Theory

ACS Omega ◽  
2022 ◽  
Author(s):  
In Sub M. Han ◽  
Dylan Abramson ◽  
Kelly M. Thayer
Keyword(s):  
Molecular Dynamics ◽  
Network Theory ◽  
Rational Design ◽  
New Class ◽  
Markov State ◽  
Markov State Models ◽  
Allosteric Effectors ◽  
State Models

scholarly journals Optimal control of molecular dynamics using Markov state models

2012 ◽  
Vol 134 (1) ◽  
pp. 259-282 ◽  
Author(s):  
Christof Schütte ◽  
Stefanie Winkelmann ◽  
Carsten Hartmann
Keyword(s):  
Molecular Dynamics ◽  
Optimal Control ◽  
Markov State ◽  
Markov State Models ◽  
State Models

scholarly journals Reconciling simulated ensembles of apomyoglobin with experimental HDX data using Bayesian inference and multi-ensemble Markov State Models

2019 ◽  
Author(s):  
Hongbin Wan ◽  
Yunhui Ge ◽  
Asghar Razavi ◽  
Vincent A. Voelz
Keyword(s):  
Molecular Dynamics ◽  
Bayesian Inference ◽  
Conformational Dynamics ◽  
Molecular Simulations ◽  
Dynamics Simulation ◽  
Trajectory Data ◽  
Protection Factor ◽  
Markov State ◽  
Markov State Models ◽  
State Models

AbstractHydrogen/deuterium exchange (HDX) is a powerful technique to investigate protein conformational dynamics at amino acid resolution. Because HDX provides a measurement of solvent exposure of backbone hydrogens, ensemble-averaged over potentially slow kinetic processes, it has been challenging to use HDX protection factors to refine structural ensembles obtained from molecular dynamics simulations. This entails two dual challenges: (1) identifying structural observables that best correlate with backbone amide protection from exchange, and (2) restraining these observables in molecular simulations to model ensembles consistent with experimental measurements. Here, we make significant progress on both fronts. First, we describe an improved predictor of HDX protection factors from structural observables in simulated ensembles, parameterized from ultra-long molecular dynamics simulation trajectory data, with a Bayesian inference approach used to retain the full posterior distribution of model parameters.We next present a new method for obtaining simulated ensembles in agreement with experimental HDX protection factors, in which molecular simulations are performed at various temperatures and restraint biases, and used to construct multi-ensemble Markov State Models (MSMs). Finally, the BICePs algorithm (Bayesian Inference of Conformational Populations) is then used with our HDX protection factor predictor to infer which thermodynamic ensemble agrees best with experiment, and estimate populations of each conformational state in the MSM. To illustrate the approach, we use a combination of HDX protection factor restraints and chemical shift restraints to model the conformational ensemble of apomyoglobin at pH 6. The resulting ensemble agrees well with experiment, and gives insight into the all-atom structure of disordered helices F and H in the absence of heme.Graphical TOC Entry

scholarly journals The mechanism of RNA base fraying: Molecular dynamics simulations analyzed with core-set Markov state models

2019 ◽  
Vol 150 (15) ◽  
pp. 154123 ◽  
Author(s):  
Giovanni Pinamonti ◽  
Fabian Paul ◽  
Frank Noé ◽  
Alex Rodriguez ◽  
Giovanni Bussi
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulations ◽  
Markov State ◽  
Markov State Models ◽  
Core Set ◽  
State Models ◽  
Dynamics Simulations

scholarly journals On metastability and Markov state models for non-stationary molecular dynamics

2016 ◽  
Vol 145 (17) ◽  
pp. 174103 ◽  
Author(s):  
Péter Koltai ◽  
Giovanni Ciccotti ◽  
Christof Schütte
Keyword(s):  
Molecular Dynamics ◽  
Markov State ◽  
Markov State Models ◽  
State Models

scholarly journals A new class of enhanced kinetic sampling methods for building Markov state models

2017 ◽  
Vol 147 (15) ◽  
pp. 152702 ◽  
Author(s):  
Arti Bhoutekar ◽  
Susmita Ghosh ◽  
Swati Bhattacharya ◽  
Abhijit Chatterjee
Keyword(s):  
Sampling Methods ◽  
New Class ◽  
Markov State ◽  
Markov State Models ◽  
State Models

scholarly journals Molecular dynamics simulations of protein aggregation: protocols for simulation setup and analysis with Markov state models and transition networks

2020 ◽  
Author(s):  
Suman Samantray ◽  
Wibke Schumann ◽  
Alexander-Maurice Illig ◽  
Martin Carballo-Pacheco ◽  
Arghadwip Paul ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Protein Aggregation ◽  
Amyloid Fibrils ◽  
Md Simulations ◽  
Aggregation Process ◽  
Markov State ◽  
Markov State Models ◽  
State Models ◽  
Dynamics Simulations ◽  
Transition Networks

AbstractProtein disorder and aggregation play significant roles in the pathogenesis of numerous neuro-degenerative diseases, such as Alzheimer’s and Parkinson’s disease. The end products of the aggregation process in these diseases are β-sheet rich amyloid fibrils. Though in most cases small, soluble oligomers formed during amyloid aggregation are the toxic species. A full understanding of the physicochemical forces behind the protein aggregation process is required if one aims to reveal the molecular basis of the various amyloid diseases. Among a multitude of biophysical and biochemical techniques that are employed for studying protein aggregation, molecular dynamics (MD) simulations at the atomic level provide the highest temporal and spatial resolution of this process, capturing key steps during the formation of amyloid oligomers. Here we provide a step-by-step guide for setting up, running, and analyzing MD simulations of aggregating peptides using GROMACS. For the analysis we provide the scripts that were developed in our lab, which allow to determine the oligomer size and inter-peptide contacts that drive the aggregation process. Moreover, we explain and provide the tools to derive Markov state models and transition networks from MD data of peptide aggregation.

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