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Secondary structure dependence of amyloid‐β(1–40) on simulation techniques and force field parameters
Author(s) -
Caliskan Murat,
Mandaci Sunay Y.,
Uversky Vladimir N.,
CoskunerWeber Orkid
Publication year - 2021
Publication title -
chemical biology and drug design
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.59
H-Index - 77
eISSN - 1747-0285
pISSN - 1747-0277
DOI - 10.1111/cbdd.13830
Subject(s) - molecular dynamics , force field (fiction) , replica , radius of gyration , parallel tempering , protein secondary structure , statistical physics , field (mathematics) , chemistry , computational chemistry , materials science , physics , monte carlo method , mathematics , markov chain monte carlo , monte carlo molecular modeling , quantum mechanics , visual arts , art , biochemistry , statistics , organic chemistry , pure mathematics , polymer
Our recent studies revealed that none of the selected widely used force field parameters and molecular dynamics simulation techniques yield structural properties for the intrinsically disordered α‐synuclein that are in agreement with various experiments via testing different force field parameters. Here, we extend our studies on the secondary structure properties of the disordered amyloid‐β(1–40) peptide in aqueous solution. For these purposes, we conducted extensive replica exchange molecular dynamics simulations and obtained extensive molecular dynamics simulation trajectories from David E. Shaw group. Specifically, these molecular dynamics simulations were conducted using various force field parameters and obtained results are compared to our replica exchange molecular dynamics simulations and experiments. In this study, we calculated the secondary structure abundances and radius of gyration values for amyloid‐β(1–40) that were simulated using varying force field parameter sets and different simulation techniques. In addition, the intrinsic disorder propensity, as well as sequence‐based secondary structure predisposition of amyloid‐β(1–40) and compared the findings with the results obtained from molecular simulations using various force field parameters and different simulation techniques. Our studies clearly show that the epitope region identification of amyloid‐β(1–40) depends on the chosen simulation technique and chosen force field parameters. Based on comparison with experiments, we find that best computational results in agreement with experiments are obtained using the a99sb*‐ildn, charmm36m, and a99sb‐disp parameters for the amyloid‐β(1–40) peptide in molecular dynamics simulations without parallel tempering or via replica exchange molecular dynamics simulations.