CAMELOT: A machine learning approach for coarse-grained simulations of aggregation of block-copolymeric protein sequences.

ABSTRACT: We report the development and deployment of a coarse-graining method that is well suited for computer simulations of aggregation and phase separation of protein sequences with block-copolymeric architectures. Our algorithm, named CAMELOT for Coarse-grained simulations Aided by MachinE Learning Optimization and Training, leverages information from converged all atom simulations that is used to determine a suitable resolution and parameterize the coarse-grained model. To parameterize a system-specific coarse-grained model, we use a combination of Boltzmann inversion, non-linear regression, and a Gaussian process Bayesian optimization approach. The accuracy of the coarse-grained model is demonstrated through direct comparisons to results from all atom simulations. We demonstrate the utility o

SUBMITTER: Ruff KM 

PROVIDER: S-EPMC4644154 | biostudies-literature | 2015 Dec

REPOSITORIES: biostudies-literature