Biology subjects

Samantray, S.

Publications and source records attributed to Samantray, S..

3 recordsLinked to original sources

bioRxiv2020

Different force fields give rise to different amyloid aggregation pathways in molecular dynamics simulations

The progress towards understanding the molecular basis of Alzheimerss disease is strongly connected to elucidating the early aggregation events of the amyloid-{beta} (A{beta}) peptide. Molecular dynamics (MD) simulations provide a viable technique to study the aggregation of A{beta} into oligomers with high spatial and temporal resolution. However, the results of an MD simulation can only be as good as the underlying force field. A recent study by our group showed that none of the force fields tested can distinguish between aggregation-prone and non-aggregating peptide sequences, producing the same and in most cases too fast aggregation kinetics for all peptides. Since then, new force fields specially designed for intrinsically disordered proteins such as A{beta} were developed. Here, we assess the applicability of these new force fields to studying peptide aggregation using the A{beta}16-22 peptide and mutations of it as test case. We investigate their performance in modeling the monomeric state, the aggregation into oligomers, and the stability of the aggregation end product, i.e., the fibrillar state. A main finding is that changing the force field has a stronger effect on the simulated aggregation pathway than changing the peptide sequence. Also the new force fields are not able to reproduce the experimental aggregation propensity order of the peptides. Dissecting the various energy contributions shows that AMBER99SB-disp overestimates the interactions between the peptides and water, thereby inhibiting peptide aggregation. More promising results are obtained with CHARMM36m and especially its version with increased protein-water interactions. It is thus recommended to use this force field for peptide aggregation simulations and base future reparameterizations on it.

biophysics↗

bioRxiv2020

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

The amlyoid-{beta} peptide (A{beta}) is closely linked to the development of Alzheimers disease. Molecular dynamics (MD) simulations have become an indispensable tool for studying the behavior of this peptide at the (sub)molecular level, thereby providing insight into the molecular basis of Alzheimers disease. General key aspects of MD simulations are the force field used for modeling the peptide or protein and its environment, which is important for accurate modeling of the system of interest, and the length of the simulations, which determines whether or not equilibrium is reached. In this study we address these points by analyzing 30-{micro}s MD simulations acquired for A{beta}40 using seven different force fields. We assess the convergence of these simulations based on the convergence of various structural properties and of NMR and fluorescence spectroscopic observables. Moreover, we calculate Markov state models for each of the seven MD simulations, which provide an unprecedented view of the thermodynamics and kinetics of the amyloid-{beta} peptide. This further allows us to provide answers for pertinent questions, like: Which force fields are suitable for modeling A{beta}? (a99SB-UCB and a99SB-ILDN/TIP4P-D); What does A{beta} peptide really look like? (mostly extended and disordered) and; How long does it take MD simulations of A{beta} to attain equilibrium? (20-30 {micro}s). We believe the analyses presented in this study will provide a useful reference guide for important questions relating to the structure and dynamics of A{beta}in particular, and by extension other similar disordered peptides.

biophysics↗

bioRxiv2020

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

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.

biophysics↗