Molecular dynamics simulations of protein aggregation: protocols for simulation setup and analysis with Markov state models and transition networks
Samantray, S.; Schumann, W.; Illig, A.-M.; Paul, A.; Barz, B.; Strodel, B.
Show abstract
Protein disorder and aggregation play significant roles in the pathogenesis of numerous neuro-degenerative diseases, such as Alzheimers and Parkinsons disease. The end products of the aggregation process in these diseases are {beta}-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.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
- Protocols for Multi-Scale Molecular Dynamics Simulations: A Comparative Study for Intrinsically Disordered Amyloid Beta in Amber & Gromacs on CPU & GPU 98%
- Conformational Space of the Translocation Domain of Botulinum Toxin: Atomistic Modeling and Mesoscopic Description of the Coiled-Coil Helix Bundle 96%
- Protein-protein docking with large-scale backbone flexibility using coarse-grained Monte-Carlo simulations 96%
Similar papers in this journal
- Differentiable molecular simulation can learn all the parameters in a coarse-grained force field for proteins 96%
- AlphaFold2 modeling and molecular dynamics simulations of an intrinsically disordered protein 94%
- Assessment of Software Methods for Estimating Protein-Protein Relative Binding Affinities 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.