Performance of Markov State Models and Transition Networks on Characterizing Amyloid Aggregation Pathways from MD Data

2020 ◽  
Vol 16 (12) ◽  
pp. 7825-7839 ◽  
Author(s):  
Alexander-Maurice Illig ◽  
Birgit Strodel
Keyword(s):  
Amyloid Aggregation ◽  
Markov State ◽  
Markov State Models ◽  
State Models ◽  
Transition Networks

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 Thermodynamics and kinetics of the amyloid-β peptide revealed by Markov state models based on MD data in agreement with experiment

2021 ◽  
Author(s):  
Arghadwip Paul ◽  
Suman Samantray ◽  
Marco Anteghini ◽  
Mohammed Khaled ◽  
Birgit Strodel
Keyword(s):  
Amyloid Β ◽  
Md Simulations ◽  
Amyloid Β Peptide ◽  
Intrinsically Disordered ◽  
Thermodynamics And Kinetics ◽  
Markov State ◽  
Markov State Models ◽  
State Models ◽  
Kinetics Of

The convergence of MD simulations is tested using varying measures for the intrinsically disordered amyloid-β peptide (Aβ). Markov state models show that 20–30 μs of MD is needed to reliably reproduce the thermodynamics and kinetics of Aβ.

scholarly journals Markov state models based on milestoning

2011 ◽  
Vol 134 (20) ◽  
pp. 204105 ◽  
Author(s):  
Christof Schütte ◽  
Frank Noé ◽  
Jianfeng Lu ◽  
Marco Sarich ◽  
Eric Vanden-Eijnden
Keyword(s):  
Markov State ◽  
Markov State Models ◽  
State Models
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