Simulations of nucleation and elongation of amyloid fibrils.
We present a coarse-grained model for the growth kinetics of amyloid fibrils from solutions of peptides and address the fundamental mechanism of nucleation and elongation by using a lattice Monte Carlo procedure. We reproduce the three main characteristics of nucleation of amyloid fibrils: (1) existence of lag time, (2) occurrence of a critical concentration, and (3) seeding. We find the nucleation of amyloid fibrils to require a quasi-two-dimensional configuration, where a second layer of beta sheet must be formed adjunct to a first layer, which in turn leads to a highly cooperative nucleation barrier. The elongation stage is found to involve the Ostwald ripening (evaporation-condensation) mechanism, whereby bigger fibrils grow at the expense of smaller ones. This new mechanism reconciles the debate as to whether protofibrils are precursors or monomer reservoirs. We have systematically investigated the roles of time, peptide concentration, temperature, and seed size. In general, we find that there are two kinds of lag time arising from two different mechanisms. For higher temperatures or low enough concentrations close to the disassembly boundary, the fibrillization follows the nucleation mechanism. However, for low temperatures, where the nucleation time is sufficiently short, there still exists an apparent lag time due to slow Ostwald ripening mechanism. Consequently, the lag time is nonmonotonic with temperature, with the shortest lag time occurring at intermediate temperatures, which in turn depend on the peptide concentration. While the nucleation dominated regime can be controlled by seeding, the Ostwald ripening regime is insensitive to seeding. Simulation results from our coarse-grained model on the fibril size, lag time, elongation rate, and solubility are consistent with available experimental observations on many specific amyloid systems.
Zhang
Jianing
J
Department of Polymer Science and Engineering, Materials Research Science and Engineering Center, University of Massachusetts, Amherst, Massachusetts 01003, USA.
Muthukumar
M
M
D000327:Adsorption; D000465:Algorithms; D000682:Amyloid / Q000737:chemistry*; D001703:Biophysics / Q000379:methods; D003198:Computer Simulation; D006801:Humans; D007700:Kinetics; D008962:Models, Theoretical; D009010:Monte Carlo Method; D010455:Peptides / Q000737:chemistry; D012995:Solubility; D013696:Temperature; D013997:Time Factors
D000682:Amyloid; D010455:Peptides