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

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 c...

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Autores principales:	Satwik Pasani, Shruthi Viswanath
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	integrative modeling molecular simulations MCMC stochastic sampling derivative-free optimization Science Q
Acceso en línea:	https://doaj.org/article/146a89bf19c14d43b278ae360592ade9
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spelling	oai:doaj.org-article:146a89bf19c14d43b278ae360592ade92021-11-25T18:10:58ZA Framework for Stochastic Optimization of Parameters for Integrative Modeling of Macromolecular Assemblies10.3390/life111111832075-1729https://doaj.org/article/146a89bf19c14d43b278ae360592ade92021-11-01T00:00:00Zhttps://www.mdpi.com/2075-1729/11/11/1183https://doaj.org/toc/2075-1729Integrative 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 domains, metrics that they influence, and the target ranges of these metrics, StOP produces the optimal values of these parameters. StOP is adaptable to the available computing capacity and converges quickly, allowing for the simultaneous optimization of a large number of parameters. However, it is not efficient at high dimensions and not guaranteed to find optima in complex landscapes. We demonstrate its performance on several examples of random functions, as well as on two integrative modeling examples, showing that StOP enhances the efficiency of sampling the posterior distribution, resulting in more good-scoring models and better sampling precision.Satwik PasaniShruthi ViswanathMDPI AGarticleintegrative modelingmolecular simulationsMCMCstochastic samplingderivative-free optimizationScienceQENLife, Vol 11, Iss 1183, p 1183 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	integrative modeling molecular simulations MCMC stochastic sampling derivative-free optimization Science Q
spellingShingle	integrative modeling molecular simulations MCMC stochastic sampling derivative-free optimization Science Q Satwik Pasani Shruthi Viswanath A Framework for Stochastic Optimization of Parameters for Integrative Modeling of Macromolecular Assemblies
description	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 domains, metrics that they influence, and the target ranges of these metrics, StOP produces the optimal values of these parameters. StOP is adaptable to the available computing capacity and converges quickly, allowing for the simultaneous optimization of a large number of parameters. However, it is not efficient at high dimensions and not guaranteed to find optima in complex landscapes. We demonstrate its performance on several examples of random functions, as well as on two integrative modeling examples, showing that StOP enhances the efficiency of sampling the posterior distribution, resulting in more good-scoring models and better sampling precision.
format	article
author	Satwik Pasani Shruthi Viswanath
author_facet	Satwik Pasani Shruthi Viswanath
author_sort	Satwik Pasani
title	A Framework for Stochastic Optimization of Parameters for Integrative Modeling of Macromolecular Assemblies
title_short	A Framework for Stochastic Optimization of Parameters for Integrative Modeling of Macromolecular Assemblies
title_full	A Framework for Stochastic Optimization of Parameters for Integrative Modeling of Macromolecular Assemblies
title_fullStr	A Framework for Stochastic Optimization of Parameters for Integrative Modeling of Macromolecular Assemblies
title_full_unstemmed	A Framework for Stochastic Optimization of Parameters for Integrative Modeling of Macromolecular Assemblies
title_sort	framework for stochastic optimization of parameters for integrative modeling of macromolecular assemblies
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/146a89bf19c14d43b278ae360592ade9
work_keys_str_mv	AT satwikpasani aframeworkforstochasticoptimizationofparametersforintegrativemodelingofmacromolecularassemblies AT shruthiviswanath aframeworkforstochasticoptimizationofparametersforintegrativemodelingofmacromolecularassemblies AT satwikpasani frameworkforstochasticoptimizationofparametersforintegrativemodelingofmacromolecularassemblies AT shruthiviswanath frameworkforstochasticoptimizationofparametersforintegrativemodelingofmacromolecularassemblies
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A Framework for Stochastic Optimization of Parameters for Integrative Modeling of Macromolecular Assemblies

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