Detecting and accounting for multiple sources of positional variance in peak list registration analysis and spin system grouping

Andrey Smelter,Hunter N B Moseley,Eric C Rouchka

doi:10.1007/s10858-017-0126-5

Andrey Smelter, Hunter N B Moseley + Show 1 more

Open Access

https://doi.org/10.1007/s10858-017-0126-5

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Abstract

Peak lists derived from nuclear magnetic resonance (NMR) spectra are commonly used as input data for a variety of computer assisted and automated analyses. These include automated protein resonance assignment and protein structure calculation software tools. Prior to these analyses, peak lists must be aligned to each other and sets of related peaks must be grouped based on common chemical shift dimensions. Even when programs can perform peak grouping, they require the user to provide uniform match tolerances or use default values. However, peak grouping is further complicated by multiple sources of variance in peak position limiting the effectiveness of grouping methods that utilize uniform match tolerances. In addition, no method currently exists for deriving peak positional variances from single peak lists for grouping peaks into spin systems, i.e. spin system grouping within a single peak list. Therefore, we developed a complementary pair of peak list registration analysis and spin system grouping algorithms designed to overcome these limitations. We have implemented these algorithms into an approach that can identify multiple dimension-specific positional variances that exist in a single peak list and group peaks from a single peak list into spin systems. The resulting software tools generate a variety of useful statistics on both a single peak list and pairwise peak list alignment, especially for quality assessment of peak list datasets. We used a range of low and high quality experimental solution NMR and solid-state NMR peak lists to assess performance of our registration analysis and grouping algorithms. Analyses show that an algorithm using a single iteration and uniform match tolerances approach is only able to recover from 50 to 80% of the spin systems due to the presence of multiple sources of variance. Our algorithm recovers additional spin systems by reevaluating match tolerances in multiple iterations. To facilitate evaluation of the algorithms, we developed a peak list simulator within our nmrstarlib package that generates user-defined assigned peak lists from a given BMRB entry or database of entries. In addition, over 100,000 simulated peak lists with one or two sources of variance were generated to evaluate the performance and robustness of these new registration analysis and peak grouping algorithms.

Highlights

One of the prerequisite analyses for protein structure determination is the assignment of chemical shifts to specific nuclei in a protein structure
Since peak positions have multiple sources of variance which are difficult to handle with uniform match tolerances, we developed a new iterative grouping algorithm that combines our peak list registration analysis algorithm with an adaptation of the density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm normalized by dimension-specific peak position variances
We evaluated the performance of our combined registration analysis and grouping algorithm on manually assigned peak lists derived from solution and solid-state nuclear magnetic resonance (NMR) experiments

Summary

Introduction

One of the prerequisite analyses for protein structure determination is the assignment of chemical shifts to specific nuclei in a protein structure. Spin systems are mapped to individual amino acid residues in a protein sequence. We define a spin system as a collection of related resonances associated with specific atoms in a molecule that can be grouped within a single spectrum and across multiple spectra with common resonances. In the context of biopolymers such as proteins, spin systems often represent resonances associated with atoms within one, two, or even three bonded residues. 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 mapping those segments onto protein sequence

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