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Robust automated backbone triple resonance NMR assignments of proteins using Bayesian-based simulated annealing

Author

Listed:
  • Anthony C. Bishop

    (Texas A&M University)

  • Glorisé Torres-Montalvo

    (Texas A&M University)

  • Sravya Kotaru

    (University of Pennsylvania)

  • Kyle Mimun

    (Texas A&M University)

  • A. Joshua Wand

    (Texas A&M University
    University of Pennsylvania
    Texas A&M University
    Texas A&M University)

Abstract

Assignment of resonances of nuclear magnetic resonance (NMR) spectra to specific atoms within a protein remains a labor-intensive and challenging task. Automation of the assignment process often remains a bottleneck in the exploitation of solution NMR spectroscopy for the study of protein structure-dynamics-function relationships. We present an approach to the assignment of backbone triple resonance spectra of proteins. A Bayesian statistical analysis of predicted and observed chemical shifts is used in conjunction with inter-spin connectivities provided by triple resonance spectroscopy to calculate a pseudo-energy potential that drives a simulated annealing search for the most optimal set of resonance assignments. Termed Bayesian Assisted Assignments by Simulated Annealing (BARASA), a C++ program implementation is tested against systems ranging in size to over 450 amino acids including examples of intrinsically disordered proteins. BARASA is fast, robust, accommodates incomplete and incorrect information, and outperforms current algorithms – especially in cases of sparse data and is sufficiently fast to allow for real-time evaluation during data acquisition.

Suggested Citation

  • Anthony C. Bishop & Glorisé Torres-Montalvo & Sravya Kotaru & Kyle Mimun & A. Joshua Wand, 2023. "Robust automated backbone triple resonance NMR assignments of proteins using Bayesian-based simulated annealing," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-37219-z
    DOI: 10.1038/s41467-023-37219-z
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    References listed on IDEAS

    as
    1. Santrupti Nerli & Viviane S. Paula & Andrew C. McShan & Nikolaos G. Sgourakis, 2021. "Backbone-independent NMR resonance assignments of methyl probes in large proteins," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
    2. Iva Pritišanac & Julia M. Würz & T. Reid Alderson & Peter Güntert, 2019. "Automatic structure-based NMR methyl resonance assignment in large proteins," Nature Communications, Nature, vol. 10(1), pages 1-12, December.
    3. John Jumper & Richard Evans & Alexander Pritzel & Tim Green & Michael Figurnov & Olaf Ronneberger & Kathryn Tunyasuvunakool & Russ Bates & Augustin Žídek & Anna Potapenko & Alex Bridgland & Clemens Me, 2021. "Highly accurate protein structure prediction with AlphaFold," Nature, Nature, vol. 596(7873), pages 583-589, August.
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