A fast and efficient python library for interfacing with the Biological Magnetic Resonance Data Bank

Smelter, Andrey; Astra, Morgan; Moseley, Hunter N. B.

doi:10.1186/s12859-017-1580-5

Cited by 10 publications

(14 citation statements)

References 6 publications

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“…To address these and related NMR-STAR file utilization problems, we have developed the nmrstarlib package (Smelter et al 2017 ), a new open source library that can be used to extract experimental peak list data from BMRB entries and convert them into peak lists of appropriate format (e.g. Sparky, AutoAssign, Xeasy).…”

Section: Introductionmentioning

confidence: 99%

“…In addition, we developed a peak list simulator that can create peak lists of different types using the entire BMRB, allowing the creation of large numbers of simulated assigned peak lists that includes dimension-specific noise from multiple sources of variance as specified by the user. This new peak list simulator is also part of the nmrstarlib package (Smelter et al 2017 ).…”

Section: Introductionmentioning

confidence: 99%

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

2017

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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.Electronic supplementary materialThe online version of this article (doi:10.1007/s10858-017-0126-5) contains supplementary material, which is available to authorized users.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

2017

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“…This algorithm is implemented as a peak list simulator submodule within the nmrstarlib Python package [94], which facilitates the reading and writing of NMR-STAR formatted files, especially entry files maintained by BMRB. This algorithm uses the nmrstarlib functionality to access assigned chemical shift values for H, C and N resonances for each residue in a protein chain and then saves them as a peak list file in different formats (e.g.…”

Section: Peak List Simulation Algorithmmentioning

confidence: 99%

“…In addition, a peak list simulator that can create peak lists of different types using the entire BMRB was implemented, allowing the creation of large number of simulated assigned peak lists that includes dimension-specific noise from multiple sources of variance as specified by the user. This new peak list simulator is part of the nmrstarlib package [94].…”

Section: Algorithm For Generating Simulated Peak Listsmentioning

confidence: 99%

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

Smelter¹

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Protein nuclear magnetic resonance spectroscopy (Protein NMR) is an invaluable analytical technique for studying protein structure, function, and dynamics. There are two major types of NMR spectroscopy that are used for investigation of protein structuresolution-state and solid-state NMR. Solution-based NMR spectroscopy is typically applied to proteins of small and medium size that are soluble in water. Solid-state NMR spectroscopy is amenable for proteins that are insoluble in water.In the vast majority NMR-based protein studies, the first step after experiment optimization is the assignment of protein resonances via the association of chemical shift values to specific atoms in a protein macromolecule. Depending on the quality of the spectra, a manual protein resonance assignment process often requires a considerable amount of time, from weeks to months-worth of effort even, by an experienced NMR spectroscopist.vii The resonance assignment processes for solution-state and solid-state protein NMR studies are conceptually similar, but have distinct differences due to the utilization of different NMR experiments and to the use of different resonances for grouping peaks into spin systems.Currently, there is a shortage of robust, effective software tools that can perform solid-state protein resonance assignment and there is no general software that can perform both solution-state and solid-state protein resonance assignment in a reliable, automated fashion. Hence, the motivation of this research is to design and implement algorithms and software tools that will automate the resonance assignment problem.As a result of this research, several algorithms and software packages that aid several important steps in the protein resonance assignment process were developed. For example, the nmrstarlib software package can access and utilize data deposited in the NMR-STAR format; the core of this library is the lexical analyzer for NMR-STAR syntax that acts as a generator-based state-machine for token processing. The jpredapi software package provides an easy-to-use API to submit and retrieve results from secondary structure prediction server. The single peak list and pairwise peak list registration algorithms address the problem of multiple sources of variance within single peak list and between different peak lists and is capable of calculating the match tolerance values necessary for spin system grouping. The single peak list and pairwise peak list grouping algorithms are based on the well-known DBSCAN clustering algorithm and are designed to group peaks into spin systems within single peak list as well as between different peak lists.viii

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Metabolomics

Pushparaj¹

2019

Essentials of Bioinformatics, Volume I

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A fast and efficient python library for interfacing with the Biological Magnetic Resonance Data Bank

Cited by 10 publications

References 6 publications

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

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

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

Metabolomics

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