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Optimizing representations for integrative structural modeling using Bayesian model selection.

Shreyas Arvindekar1, Aditi S Pathak1, Kartik Majila1

  • 1National Center for Biological Sciences, Tata Institute of Fundamental Research, Bangalore, India 560065.

Biorxiv : the Preprint Server for Biology
|January 3, 2024
PubMed
Summary
This summary is machine-generated.

NestOR automates the selection of optimal coarse-grained representations for integrative structural modeling. This statistically rigorous method enhances accuracy and efficiency in characterizing complex macromolecular assemblies.

Keywords:
Bayes factorsBayesian model selectioncoarse-grained representationintegrative modelingmacromolecular assembliesmodel evidencenested sampling

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Area of Science:

  • Structural biology
  • Computational biology
  • Biophysics

Background:

  • Integrative structural modeling is crucial for characterizing complex macromolecular assemblies.
  • Manual selection of model representation is a bottleneck, impacting accuracy and efficiency.
  • Optimal representation choice is critical for accurate scoring, efficient sampling, and high-resolution analysis.

Approach:

  • Developed NestOR (Nested Sampling for Optimizing Representation), a fully automated method.
  • Employs Bayesian model selection to identify the best coarse-grained representation.
  • Evaluated NestOR on a benchmark of four diverse macromolecular assemblies.

Key Points:

  • NestOR automates the selection of optimal coarse-grained representations.
  • The method is statistically rigorous, based on Bayesian model selection.
  • NestOR considers model evidence and sampling efficiency for optimal representation choice.

Conclusions:

  • NestOR provides a robust, automated solution for a critical step in integrative modeling.
  • The method enhances the accuracy and efficiency of macromolecular assembly structure determination.
  • NestOR is implemented in the Integrative Modeling Platform and available open-source.