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

Satwik Pasani,Shruthi Viswanath

doi:10.3390/life11111183

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 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.

Highlights

Integrative modeling or hybrid modeling is a method of determining macromolecular structures by combining data from complementary experiments, physical principles, statistical inference, and prior models [1,2,3]
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
A coarse-grained representation is defined for the system, and input information is translated into spatial restraints

Summary

Introduction

Integrative modeling or hybrid modeling is a method of determining macromolecular structures by combining data from complementary experiments, physical principles, statistical inference, and prior models [1,2,3]. It is used to determine structures of large macromolecular assemblies, which are refractory, to a single experimental method such as. All available information on the system is gathered. A coarse-grained representation is defined for the system, and input information is translated into spatial restraints. Structural models consistent with the input information are sampled using a sampling procedure that is necessarily stochastic, since exhaustively enumerating all structural degrees of freedom is infeasible for large assemblies. The sampled structures are analyzed and validated [1,3,9,10,11,12,13]

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