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@ARTICLE{Paul:904329,
author = {Paul, Arghadwip and Samantray, Suman and Anteghini, Marco
and Khaled, Mohammed and Strodel, Birgit},
title = {{T}hermodynamics and kinetics of the amyloid-β peptide
revealed by {M}arkov state models based on {MD} data in
agreement with experiment},
journal = {Chemical science},
volume = {12},
number = {19},
issn = {2041-6520},
address = {Cambridge},
publisher = {RSC},
reportid = {FZJ-2021-05899},
pages = {6652 - 6669},
year = {2021},
abstract = {The amlyoid-β peptide (Aβ) is closely linked to the
development of Alzheimer's disease. Molecular dynamics (MD)
simulations have become an indispensable tool for studying
the behavior of this peptide at the atomistic level. General
key aspects of MD simulations are the force field used for
modeling the peptide 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-μs MD simulations acquired for Aβ40
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 the different MD simulations, which provide
an unprecedented view of the thermodynamics and kinetics of
the amyloid-β peptide. This further allows us to provide
answers for pertinent questions, like: which force fields
are suitable for modeling Aβ? (a99SB-UCB and
a99SB-ILDN/TIP4P-D); what does Aβ peptide really look like?
(mostly extended and disordered) and; how long does it take
MD simulations of Aβ to attain equilibrium? (at least
20–30 μs). We believe the analyses presented in this
study will provide a useful reference guide for important
questions relating to the structure and dynamics of Aβ in
particular, and by extension other similar disordered
proteins.},
cin = {IBI-7},
ddc = {540},
cid = {I:(DE-Juel1)IBI-7-20200312},
pnm = {5244 - Information Processing in Neuronal Networks
(POF4-524)},
pid = {G:(DE-HGF)POF4-5244},
typ = {PUB:(DE-HGF)16},
pubmed = {pmid:34040740},
UT = {WOS:000640200800001},
doi = {10.1039/D0SC04657D},
url = {https://juser.fz-juelich.de/record/904329},
}