A New Algorithm for Reliable and General NMR Resonance Assignment

Abstract

The new FLYA automated resonance assignment algorithm determines NMR chemical shift assignments on the basis of peak lists from any combination of multidimensional through-bond or through-space NMR experiments for proteins. Backbone and side-chain assignments can be determined. All experimental data are used simultaneously, thereby exploiting optimally the redundancy present in the input peak lists and circumventing potential pitfalls of assignment strategies in which results obtained in a given step remain fixed input data for subsequent steps. Instead of prescribing a specific assignment strategy, the FLYA resonance assignment algorithm requires only experimental peak lists and the primary structure of the protein, from which the peaks expected in a given spectrum can be generated by applying a set of rules, defined in a straightforward way by specifying through-bond or through-space magnetization transfer pathways. The algorithm determines the resonance assignment by finding an optimal mapping between the set of expected peaks that are assigned by definition but have unknown positions and the set of measured peaks in the input peak lists that are initially unassigned but have a known position in the spectrum. Using peak lists obtained by purely automated peak picking from the experimental spectra of three proteins, FLYA assigned correctly 96-99% of the backbone and 90-91% of all resonances that could be assigned manually. Systematic studies quantified the impact of various factors on the assignment accuracy, namely the extent of missing real peaks and the amount of additional artifact peaks in the input peak lists, as well as the accuracy of the peak positions. Comparing the resonance assignments from FLYA with those obtained from two other existing algorithms showed that using identical experimental input data these other algorithms yielded significantly (40-142%) more erroneous assignments than FLYA. The FLYA resonance assignment algorithm thus has the reliability and flexibility to replace most manual and semi-automatic assignment procedures for NMR studies of proteins.

Solid-state NMR sequential assignments of the N-terminal domain of HpDnaB helicase

“…Solid-state FLYA calculations (Schmidt et al 2013;Schmidt and Güntert 2012) were performed with CYANA version 3.97. The tolerance value for chemical shift matching was set to 0.55 ppm for 13 C and 15 N. The calculations are based on experimental peak lists as obtained from the manual assignment procedure.…”

Systematic Assessment of the Effects of an All-Atom Force Field and the Implicit Solvent Model on the Refinement of NMR Structures with Subsets of Distance Restraints

“…Owing to the current state of computational power, several studies have reported that fully automatic structure calculations from processed NMR data were feasible without any manual interpretation. [2][3][4][5] However, the improvements and refinements of NMR structures are still believed to be largely dependent on the skills and experiences of the researchers interpreting and calculating the structures.…”

Algorithms for automated assignment of solution-state and solid-state protein NMR spectra.

“…Genetic algorithm [37], [38], [39] approaches are related to Monte Carlo methods and try to identify the optimal resonance assignments through evolution of set of initial random individual solutions. The optimal solution is deduced through multiple cycles of mutation and recombination.…”

“…This approach was developed on the basis of FLYA algorithm for automated assignment of solutionstate NMR peak lists within same CYANA software [39]. The ssFLYA algorithm is able to handle more standard 2D and 3D solid-state NMR peak lists such as 3D NCACB, 3D…”

“…Manual resonance assignment is tedious and can take a significant amount of time. Therefore, a variety of automated and semi-automated assignment programs have been developed to facilitate the protein resonance assignment process, specifically for solution [84], [85] and solidstate NMR [58], [59]. The process of automated resonance assignment typically involves several major steps: grouping peaks across peak lists into spin systems, classification of those spin systems by possible amino acid type, linking neighboring spin systems into segments, and then mapping those segments onto protein sequence.…”