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.

The comprehensive assignment of individual resonances of the nuclear magnetic resonance spectrum of a protein to specific atoms remains a labor-intensive and often debilitating task -especially for proteins larger than 30 kDa. Recently, there have been tremendous advances in our empirical knowledge of the relationship between the structural context of a nuclear spin and its observed resonance frequency. Indeed, the expansion in the database of determined high-resolution protein structures and recent advances in structure prediction provide an enormous resource in this respect. Robust automation of the resonance assignment process nevertheless often remains a bottleneck in the exploitation of solution NMR spectroscopy for the study of protein structure-dynamics-function relationships. Here we present a new approach for the assignment of backbone triple resonance spectra of proteins. A Bayesian statistical analysis of predicted and observed chemical shifts is used to provide a pseudo-energy potential to drive the search for the most optimal set of resonance assignments. This approach has been implemented in the C++ program Bayllagio and tested against protein systems ranging in size to over 450 amino acids. Bayllagio makes almost no errors, accommodates incomplete information, is sufficiently fast to allow for real-time evaluation of data acquisition, and greatly outperforms currently employed deterministic algorithms.