BICePs - Bayesian Inference of Conformational Populations

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The BICePs algorithm (Bayesian Inference of Conformational Populations) is a statistically rigorous Bayesian inference method to reconcile theoretical predictions of conformational state populations with sparse and/or noisy experimental measurements and objectively compare different models. Supported experimental observables include:

• NMR nuclear Overhauser effect (NOE)
• NMR chemical shifts (HA, NH, CA and N)
• J couplings (both small molecules and amino acids)
• Hydrogen–deuterium exchange (HDX)

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• Installation
  • Dependencies
  • Testing Your Installation
  • Versions
• Theory
  • Bayesian inference
  • Reference potentials
  • BICePs scores for quantitative model selection
  • Summary of the key advantages of BICePs.
  • References
• Tutorials & Examples
  • The workflow
    • Preparation
    • Ensemble
    • PosteriorSampler
    • Analysis
  • Additional tools
    • Convergence
    • Multiprocessing Lambda Values
    • Toolbox
  • Full examples
    • Cineromycin B
    • Albocycline
    • Apomyoglobin
• API Reference
  • Preparation
    • Preparation
  • Ensemble
    • Ensemble
      • Ensemble.initialize_restraints()
      • Ensemble.to_list()
  • Restraint
    • Restraint
    • Restraint_cs.init_restraint()
    • Restraint_J.init_restraint()
    • Restraint_noe.init_restraint()
    • Restraint_pf.init_restraint()
  • PosteriorSampler
    • PosteriorSampler
      • PosteriorSampler.neglogP()
      • PosteriorSampler.sample()
    • PosteriorSamplingTrajectory
      • PosteriorSamplingTrajectory.process_results()
  • Analysis
    • Analysis
      • Analysis.plot()
  • Convergence
    • exponential_fit()
    • compute_autocorrelation_curves()
    • g()
    • compute_autocorrelation_time()
    • get_blocks()
    • compute_JSD()
    • Convergence
      • Convergence.plot_traces()
      • Convergence.plot_auto_curve()
      • Convergence.plot_block_avg()
      • Convergence.get_autocorrelation_curves()
      • Convergence.process()
  • toolbox
    • sort_data()
    • get_files()
    • list_res()
    • list_possible_restraints()
    • list_extensions()
    • list_possible_extensions()
    • npz_to_DataFrame()
    • save_object()

Citation

Please apply BICePs in your research and cite it in any scientific publications.

@article{raddi2022biceps,
  title={BICePs v2. 0: Software for Ensemble Reweighting using Bayesian Inference of Conformational Populations},
  author={Raddi, Robert and Ge, Yunhui and Voelz, Vincent},
  year={2022}
}

@article{VAV-2018,
    title = {Model selection using BICePs: A Bayesian approach to forcefield validation and parameterization},
    author = {Yunhui Ge and Vincent A. Voelz},
    journal = {Journal of Physical Chemistry B},
    volume = {122},
    number = {21},
    pages = {5610 -- 5622},
    year = {2018},
    doi = {doi:10.1021/acs.jpcb.7b11871}
}

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