A Framework for Stochastic Optimization of Parameters for Integrative Modeling of Macromolecular Assemblies.

ABSTRACT: Integrative modeling of macromolecular assemblies requires stochastic sampling, for example, via MCMC (Markov Chain Monte Carlo), since exhaustively enumerating all structural degrees of freedom is infeasible. MCMC-based methods usually require tuning several parameters, such as the move sizes for coarse-grained beads and rigid bodies, for sampling to be efficient and accurate. Currently, these parameters are tuned manually. To automate this process, we developed a general heuristic for derivative-free, global, stochastic, parallel, multiobjective optimization, termed StOP (Stochastic Optimization of Parameters) and applied it to optimize sampling-related parameters for the Integrative Modeling Platform (IMP). Given an integrative modeling setup, list of parameters to optimize, their domai

SUBMITTER: Pasani S 

PROVIDER: S-EPMC8618978 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature