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.

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 Markov State Models and Molecular Dynamics Simulations Provide Understanding of the Nucleotide-Dependent Dimerization-Based Activation of LRRK2 ROC Domain

Molecules ◽  
2021 ◽  
Vol 26 (18) ◽  
pp. 5647
Author(s):  
Xinyi Li ◽  
Zengxin Qi ◽  
Duan Ni ◽  
Shaoyong Lu ◽  
Liang Chen ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulations ◽  
Structural Rearrangement ◽  
Catalytic Triad ◽  
Gtpase Domain ◽  
Markov State ◽  
Markov State Models ◽  
Activated State ◽  
State Models ◽  
Dynamics Simulations

Mutations in leucine-rich repeat kinase 2 (LRRK2) are recognized as the most frequent cause of Parkinson’s disease (PD). As a multidomain ROCO protein, LRRK2 is characterized by the presence of both a Ras-of-complex (ROC) GTPase domain and a kinase domain connected through the C-terminal of an ROC domain (COR). The bienzymatic ROC–COR–kinase catalytic triad indicated the potential role of GTPase domain in regulating kinase activity. However, as a functional GTPase, the detailed intrinsic regulation of the ROC activation cycle remains poorly understood. Here, combining extensive molecular dynamics simulations and Markov state models, we disclosed the dynamic structural rearrangement of ROC’s homodimer during nucleotide turnover. Our study revealed the coupling between dimerization extent and nucleotide-binding state, indicating a nucleotide-dependent dimerization-based activation scheme adopted by ROC GTPase. Furthermore, inspired by the well-known R1441C/G/H PD-relevant mutations within the ROC domain, we illuminated the potential allosteric molecular mechanism for its pathogenetic effects through enabling faster interconversion between inactive and active states, thus trapping ROC in a prolonged activated state, while the implicated allostery could provide further guidance for identification of regulatory allosteric pockets on the ROC complex. Our investigations illuminated the thermodynamics and kinetics of ROC homodimer during nucleotide-dependent activation for the first time and provided guidance for further exploiting ROC as therapeutic targets for controlling LRRK2 functionality in PD treatment.

scholarly journals Laplacian score and genetic algorithm based automatic feature selection for Markov State Models in adaptive sampling based molecular dynamics

2020 ◽  
Vol 2 ◽  
pp. e9
Author(s):  
Anu George ◽  
Madhura Purnaprajna ◽  
Prashanth Athri
Keyword(s):  
Molecular Dynamics ◽  
Adaptive Sampling ◽  
Simulated Data ◽  
Md Simulations ◽  
Considerable Effect ◽  
Feature Subset ◽  
Markov State ◽  
Markov State Models ◽  
State Models ◽  
Laplacian Score

Adaptive sampling molecular dynamics based on Markov State Models use short parallel MD simulations to accelerate simulations, and are proven to identify hidden conformers. The accuracy of the predictions provided by it depends on the features extracted from the simulated data that is used to construct it. The identification of the most important features in the trajectories of the simulated system has a considerable effect on the results. Methods In this study, we use a combination of Laplacian scoring and genetic algorithms to obtain an optimized feature subset for the construction of the MSM. The approach is validated on simulations of three protein folding complexes, and two protein ligand binding complexes. Results Our experiments show that this approach produces better results when the number of samples is significantly lesser than the number of features extracted. We also observed that this method mitigates over fitting that occurs due to high dimensionality of large biosystems with shorter simulation times.

scholarly journals Ligand Binding Rate Constants in Heme Proteins Using Markov State Models and Molecular Dynamics Simulations

ChemPhysChem ◽  
2019 ◽  
Vol 20 (19) ◽  
pp. 2451-2460
Author(s):  
Mauro Bringas ◽  
Leandro E. Lombardi ◽  
F. Javier Luque ◽  
Darío A. Estrin ◽  
Luciana Capece
Keyword(s):  
Molecular Dynamics ◽  
Ligand Binding ◽  
Molecular Dynamics Simulations ◽  
Rate Constants ◽  
Heme Proteins ◽  
Markov State ◽  
Markov State Models ◽  
Binding Rate ◽  
State Models ◽  
Dynamics Simulations
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