Optimizing representations for integrative structural modeling using Bayesian model selection.

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ID: 278320
2024
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Abstract
Integrative structural modeling combines data from experiments, physical principles, statistics of previous structures, and prior models to obtain structures of macromolecular assemblies that are challenging to characterize experimentally. The choice of model representation is a key decision in integrative modeling, as it dictates the accuracy of scoring, efficiency of sampling, and resolution of analysis. But currently, the choice is usually made ad hoc, manually.Here, we report NestOR (Nested Sampling for Optimizing Representation), a fully automated, statistically rigorous method based on Bayesian model selection to identify the optimal coarse-grained representation for a given integrative modeling setup. Given an integrative modeling setup, it determines the optimal representations from given candidate representations based on their model evidence and sampling efficiency. The performance of NestOR was evaluated on a benchmark of four macromolecular assemblies.NestOR is implemented in the Integrative Modeling Platform (https://integrativemodeling.org) and is available at https://github.com/isblab/nestor. Data for the benchmark is at https://www.doi.org/10.5281/zenodo.10360718.
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arvindekar2024optimizingbioinformatics Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Arvindekar, Shreyas;Pathak, Aditi S;Majila, Kartik;Viswanath, Shruthi;
Journal Bioinformatics
Year 2024
DOI
btae106
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Keywords
software genome assembly genbank genome contamination genome quality refseq

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