BICePs v2.0: Software for Ensemble Reweighting Using Bayesian Inference of Conformational Populations

Area of Science:

• Biophysics
• Computational Chemistry
• Structural Biology

Background:

• Understanding molecular conformations is crucial in various scientific disciplines.
• Existing methods for predicting conformational populations often require extensive experimental data.
• Bridging theoretical predictions with sparse or noisy experimental measurements remains a challenge.

Purpose of the Study:

• To introduce and describe the enhanced features of Bayesian Inference of Conformational Populations (BICePs) version 2.0.
• To provide a user-friendly and extensible tool for reweighting theoretical conformational populations.
• To facilitate the integration of diverse experimental NMR data with computational models.

Main Methods:

• BICePs v2.0 utilizes Bayesian inference to reweight theoretical conformational ensembles.
• The package supports multiple experimental Nuclear Magnetic Resonance (NMR) observables, including NOE distances, chemical shifts, J-coupling constants, and hydrogen-deuterium exchange protection factors.
• It incorporates automated data preparation, processing, and analysis of the posterior distribution, including visualization and convergence assessment.

Main Results:

• BICePs v2.0 demonstrates improved performance and expanded capabilities over its previous version.
• The package enables the integration of sparse and/or noisy experimental NMR data for refining theoretical predictions.
• Automated analysis features simplify the evaluation of statistical significance and sampling convergence.

Conclusions:

• BICePs v2.0 offers a powerful, user-friendly, and extensible solution for reweighting theoretical conformational populations.
• The enhanced support for NMR observables and automated analysis streamline the process of integrating experimental data with computational models.
• This tool advances the ability to accurately determine molecular conformational states from combined theoretical and experimental information.