BEES: Bayesian Ensemble Estimation from SAS

Published

August 6, 2019

Author(s)

Samuel Bowerman, Joseph E. Curtis, Joseph Clayton, Emre H. Brookes, Jeff Wereszczynski

Abstract

Many biomolecular complexes exhibit a flexible ensemble of conformations in solution in order to conduct their biological function. Small angle scattering (SAS) measurements are a popular method for characterizing these flexible molecules due to their relative ease of use and ability to simultaneously probe the full ensemble of states. However, SAS data is typically low-dimensional and difficult to interpret without the assistance of additional structural models. In theory, experimental SAS curves can be reconstituted from a linear combination of theoretical models, though this procedure carries significant risk of overfitting the inherently low-dimensional SAS data. Previously, we developed a Bayesian-based method for fitting ensembles of model structures to experimental SAS data that rigorously avoids overfitting. However, we have found that these methods can be difficult to incorporate into typical SAS modeling workflows, especially for users that are not experts in computational modeling. To this end, we present the "Bayesian Ensemble Estimator for SAS" (BEES) moduel for the SASSIE webserver. BEES allows users to exhaustively sample ensemble models constructed from a library of theoretical states and to interactively analyze and compare each model's performance. The fitting routine also allows for a secondary data sets to be supplied, thereby simultaneously fitting models to both SAS data as well as orthogonal information. The flexible ensemble of K63-linked ubiquitin trimers is presented as an example of the module capabilities.

Citation

Biophysical Journal

Volume

117

Issue

3

Pub Type

Journals

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Keywords

Small angle neutron scattering, modeling, analysis, bayesian, ensemble

Citation

Bowerman, S. , Curtis, J. , Clayton, J. , Brookes, E. and Wereszczynski, J. (2019), BEES: Bayesian Ensemble Estimation from SAS, Biophysical Journal, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=926632 (Accessed December 26, 2024)