Characterizing the Sequence Landscape of Peptide Fibrillization with a Bottom-Up Coarse-Grained Model

Abstract

Molecular insight into amyloid aggregation is crucial for understanding the details of protein fibril nucleation and growth, which play a significant role in a wide range of proteinopathies. The length and time scales for fibrillization make its computational study an intrinsically multiscale problem, necessitating the use of coarse-grained modeling. A wide variety of coarse-grained models for peptides have been proposed, often parametrized with a combination of top-down and bottom-up approaches. Here, we present a predictive, sequence-transferable bottom-up coarse-grained model, systematically developed using only information from atomistic simulations by applying an extended-ensemble relative entropy minimization technique. The resulting model is capable of accurately recovering conformational properties of peptides constructed from a reduced alphabet of amino acids, of predicting secondary structures of isolated and interacting peptides from their sequences alone, and of simulating aggregation of peptides that have been experimentally characterized as amyloidogenic. Finally, we couple such coarse-grained simulations with a genetic algorithm to characterize the sequence space of the reduced alphabet and identify features of sequences for which ordered fibrillar states are both thermodynamically favorable and kinetically accessible.