ArticleNature communications2023
Robust automated backbone triple resonance NMR assignments of proteins using Bayesian-based simulated annealing.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Harmonizing Peak Matching between Multidimensional NMR Spectra.Journal of chemical information and modeling · 2026Article
- Facilitating NMR Resonance Assignment with Metabolic Tampering.bioRxiv : the preprint server for biology · 2026Article
- Harmonizing Peak Matching Between Multidimensional NMR Spectra.bioRxiv : the preprint server for biology · 2026Article
- NMR data processing, visualization, analysis and structure calculation with NMRFx.Communications chemistry · 2025Article
- Towards site-specific information on PET degrading enzymes using NMR near operational temperature.Scientific reports · 2025Article
- Automated fibril structure calculations in Xplor-NIH.Structure (London, England : 1993) · 2025Article
- Virus-Glycan Interactions Studied by Solute NMR.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Backbone and methyl side-chain resonance assignments of the single chain Fab fragment of trastuzumab.Biomolecular NMR assignments · 2024Article
- Backbone and methyl side-chain resonance assignments of the Fab fragment of adalimumab.Biomolecular NMR assignments · 2024Article
- Protein NMR assignment by isotope pattern recognition.Science advances · 2024Article
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Authors and funding
5 authors.
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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.
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