(233b) An Extended-Ensemble Relative Entropy Approach to Sequence-Specific Coarse-Grained Models for Peptide Aggregation
AIChE Annual Meeting
2022
2022 Annual Meeting
Computational Molecular Science and Engineering Forum
Recent Advances in Multiscale Methodologies
Tuesday, November 15, 2022 - 8:22am to 8:38am
Here, we present a novel approach to developing bottom-up coarse-grained protein models using relative entropy optimization. This “extended ensemble†method allows models to be optimized simultaneously against multiple atomistic reference systems, each containing multiple interacting polypeptide chains, over a range of temperatures. The interactions in the resulting models are thus transferable to new sequences. We demonstrate this method first on simplified reduced-alphabet systems containing representative amino acids, showing how the temperatures of the reference systems can be chosen to optimize the balance of disordered configurations and ordered secondary structures in the reference ensemble. The sequence-specific coarse-grained models made with this strategy accurately predict secondary structures as well as end-to-end distance distributions, even for sequences outside of the training set. Next, we apply the method to aggregation-prone fragments of tau protein that form parts of the ordered cores of many disease-associated fibrillar tau structures, specifically the PHF6 hexapeptide and the hairpin-forming HP1 19-mer. Models of these systems can assemble large numbers of monomers into paired helical aggregates known to be characteristic of experimentally observed fibrils. Overall, we demonstrate that this approach provides a robust, systematic bottom-up path to sequence-specific coarse-grained models useful for studying protein aggregation and assembly.