Due to fast aggregation processes of many disordered proteins in neurodegenerative diseases, it is difficult to study their epitope regions at the monomeric and oligomeric levels. Computer simulations complement experiments and have been used to identify the epitope regions of proteins. Residues that adopt β‐sheet conformation play a central role in the oligomerization and aggregation mechanisms of such proteins, including α‐synuclein, which is at the center of Parkinson's and Alzheimer's diseases. In this study, we simulated the monomeric α‐synuclein protein in an aqueous environment to evaluate its secondary structure properties, including β‐sheet propensity, and radius of gyration by replica exchange molecular dynamics simulations. We also obtained the molecular dynamics simulation trajectories of α‐synuclein that were conducted using various force field parameters by the David E. Shaw group. Using these trajectories, we calculated the impacts of force field parameters on α‐synuclein secondary structure properties and radius of gyration values and obtained results are compared with our data from REMD simulations. This study shows that the chosen force field parameters and computer simulation techniques effect the predicted secondary structure properties and radius of gyration values of α‐synuclein in water. Herewith, we illustrate the challenges in epitope region identification of intrinsically disordered proteins in neurodegenerative diseases by current computer simulations.
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.
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