CONSTRUCTING SURROGATE MODELS OF COMPLEX SYSTEMS WITH ENHANCED SPARSITY: QUANTIFYING THE INFLUENCE OF CONFORMATIONAL UNCERTAINTY IN BIOMOLECULAR SOLVATION

Multiscale Modeling & Simulation : a SIAM Interdisciplinary Journal
H LeiN A Baker

Abstract

Biomolecules exhibit conformational fluctuations near equilibrium states, inducing uncertainty in various biological properties in a dynamic way. We have developed a general method to quantify the uncertainty of target properties induced by conformational fluctuations. Using a generalized polynomial chaos (gPC) expansion, we construct a surrogate model of the target property with respect to varying conformational states. To alleviate the high-dimensionality of the corresponding stochastic space, we propose a method to increase the sparsity of the gPC expansion by defining a set of conformational "active space" random variables. With the increased sparsity, we employ the compressive sensing method to accurately construct the surrogate model. We demonstrate the performance of the surrogate model by evaluating fluctuation-induced uncertainty in solvent-accessible surface area for the bovine trypsin inhibitor protein system and show that the new approach offers more accurate statistical information than standard Monte Carlo approaches. Furthermore, the constructed surrogate model also enables us to directly evaluate the target property under various conformational states, yielding a more accurate response surface than standard spar...Continue Reading

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Citations

Dec 15, 2016·The Journal of Physical Chemistry. B·Luke J GosinkNathan A Baker
Dec 17, 2017·The Journal of Chemical Physics·Martin GoetheJ Miguel Rubi
Feb 11, 2020·Computer Methods in Applied Mechanics and Engineering·Huan LeiNathan A Baker
Sep 22, 2020·Journal of Computational Physics·Francesca GroganNathan A Baker
Jan 3, 2018·Journal of Chemical Theory and Computation·Xiu YangNathan A Baker
Jul 16, 2020·Journal of Chemical Information and Modeling·Peiyuan GaoAlexandre M Tartakovsky

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