title: CONSTRUCTING SURROGATE MODELS OF COMPLEX SYSTEMS WITH ENHANCED SPARSITY: QUANTIFYING THE INFLUENCE OF CONFORMATIONAL UNCERTAINTY IN BIOMOLECULAR SOLVATION.
abstract: Biomolecules exhibit conformational fluctuations near equilibrium states, inducing uncertainty in various biological properties in adynamicway. 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 todirectlyevaluate the target property under various conformational states, yielding a more accurate response surface than standard sparse grid collocation methods. In particular, the new method provides higher accuracy in high-dimensional systems, such as biomolecules, where sparse grid performance is limited by the accuracy of the computed quantity of interest. Our new framework is generalizable and can be used to investigate the uncertainty of a wide variety of target properties in biomolecular systems.
authors.affiliation: Pacific Northwest National Laboratory, Richland, Washington WA 99352, USA.
authors.forename: H
authors.initials: H
authors.lastname: Lei
authors.affiliation: Pacific Northwest National Laboratory, Richland, Washington WA 99352, USA.
authors.forename: X
authors.initials: X
authors.lastname: Yang
authors.affiliation: Pacific Northwest National Laboratory, Richland, Washington WA 99352, USA.
authors.forename: B
authors.initials: B
authors.lastname: Zheng
authors.affiliation: Department of Mathematics, Purdue University, West Lafayette, IN 47906, USA.
authors.forename: G
authors.initials: G
authors.lastname: Lin
authors.affiliation: Pacific Northwest National Laboratory, Richland, Washington WA 99352, USA.
authors.forename: N A
authors.initials: NA
authors.lastname: Baker
keywords: biomolecular conformation fluctuation; compressive sensing method; model reduction; polynomial chaos; uncertainty quantification