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

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 structure – solution-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 . 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

BaMORC: A Software Package for Accurate and Robust \u3csup\u3e13\u3c/sup\u3eC Reference Correction of Protein NMR Spectra

We describe Bayesian Model Optimized Reference Correction (BaMORC), a software package that performs 13C chemical shifts reference correction for either assigned or unassigned peak lists derived from protein NMR spectra. BaMORC provides an intuitive command line interface that allows non-nuclear magnetic resonance (NMR) experts to detect and correct 13C chemical shift referencing errors of unassigned peak lists at the very beginning of NMR data analysis, further lowering the bar of expertise required for effective protein NMR analysis. Furthermore, BaMORC provides an application programming interface for integration into sophisticated protein NMR data analysis pipelines, both before and after the protein resonance assignment step

A Fast and Efficient Python Library for Interfacing with the Biological Magnetic Resonance Data Bank

Background: The Biological Magnetic Resonance Data Bank (BMRB) is a public repository of Nuclear Magnetic Resonance (NMR) spectroscopic data of biological macromolecules. It is an important resource for many researchers using NMR to study structural, biophysical, and biochemical properties of biological macromolecules. It is primarily maintained and accessed in a flat file ASCII format known as NMR-STAR. While the format is human readable, the size of most BMRB entries makes computer readability and explicit representation a practical requirement for almost any rigorous systematic analysis. Results:To aid in the use of this public resource, we have developed a package called nmrstarlib in the popular open-source programming language Python. The nmrstarlib’s implementation is very efficient, both in design and execution. The library has facilities for reading and writing both NMR-STAR version 2.1 and 3.1 formatted files, parsing them into usable Python dictionary- and list-based data structures, making access and manipulation of the experimental data very natural within Python programs (i.e. “saveframe” and “loop” records represented as individual Python dictionary data structures). Another major advantage of this design is that data stored in original NMR-STAR can be easily converted into its equivalent JavaScript Object Notation (JSON) format, a lightweight data interchange format, facilitating data access and manipulation using Python and any other programming language that implements a JSON parser/generator (i.e., all popular programming languages). We have also developed tools to visualize assigned chemical shift values and to convert between NMR-STAR and JSONized NMR-STAR formatted files. Full API Reference Documentation, User Guide and Tutorial with code examples are also available. We have tested this new library on all current BMRB entries: 100% of all entries are parsed without any errors for both NMR-STAR version 2.1 and version 3.1 formatted files. We also compared our software to three currently available Python libraries for parsing NMR-STAR formatted files: PyStarLib, NMRPyStar, and PyNMRSTAR. Conclusions: The nmrstarlib package is a simple, fast, and efficient library for accessing data from the BMRB. The library provides an intuitive dictionary-based interface with which Python programs can read, edit, and write NMR-STAR formatted files and their equivalent JSONized NMR-STAR files. The nmrstarlib package can be used as a library for accessing and manipulating data stored in NMR-STAR files and as a command-line tool to convert from NMR-STAR file format into its equivalent JSON file format and vice versa, and to visualize chemical shift values. Furthermore, the nmrstarlib implementation provides a guide for effectively JSONizing other older scientific formats, improving the FAIRness of data in these formats

Detecting and Accounting for Multiple Sources of Positional Variance in Peak List Registration Analysis and Spin System Grouping

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

Registration and Grouping Algorithms in Protein NMR Derived Peak Lists and Their Application in Protein NMR Reference Correction

Nuclear magnetic resonance spectroscopy of proteins (protein NMR) is a powerful analytical technique for studying structure and dynamics of proteins. Almost all aspects of protein NMR have been accelerated by the development of software tools that enable the analysis of NMR spectral data and its utilization in studying protein structure and dynamics. This includes software for raw NMR processing, spectral visualization, protein resonance assignment, and structure determination. However, full automation of protein NMR data analysis is still a work in progress and data analysis still requires an expert NMR spectroscopist utilizing an array of software tools. While manual resonance assignment with spectral visualization software is tedious and can take a significant amount of time, a variety of automated and semi-automated assignment programs have been developed to facilitate the protein resonance assignment process, specifically for solution and solid-state NMR. But one of the historical problems that has limited the use of automated and semi-automated protein resonance assignment tools along with other analyses of NMR peak lists is the requirement that users specify uniform match tolerances to perform spin systems grouping and linking or rely on default uniform match tolerance values provided by the tool

Current issues around the pharmacotherapy of ADHD in children and adults

Background New drugs and new formulations enter the growing market for ADHD medication. The growing awareness of possible persistence of ADHD impairment beyond childhood and adolescence resulting in increased pharmacotherapy of ADHD in adults, is also a good reason for making an inventory of the what is generally known about pharmacotherapy in ADHD. Aim To discuss current issues in the possible pharmacotherapy treatment of ADHD in children, adolescents and adults with respect to the position of pharmacotherapy in ADHD treatment guidelines, the pharmacoepidemiological trends, and current concerns about the drugs used. Methods A search of the literature with an emphasis on the position of pharmacotherapy in ADHD treatment guidelines, the pharmacoepidemiological trends, and current concerns about the drugs used in pharmacotherapy. Results According to the guidelines, the treatment of ADHD in children consists of psychosocial interventions in combination with pharmacotherapy when needed. Stimulants are the first-choice drugs in the pharmacological treatment of ADHD in children despite a number of well known and frequently reported side effects like sleep disorders and loss of appetite. With regard to the treatment of adults, stimulant treatment was recommended as the first-choice pharmacotherapy in the single guideline available. Both in children and adults, there appears to be an additional though limited role for the nonadrenergic drug atomoxetine. The increase of ADHD medication use, in children, adolescents and in adults, can not only be interpreted as a sign of overdiagnosis of ADHD. Despite the frequent use of stimulants, there is still a lack of clarity on the effects of long-term use on growth and nutritional status of children. Cardiovascular effects of both stimulants and atomoxetine are rare but can be severe. The literature suggests that atomoxetine may be associated with suicidal ideation in children. Conclusion Although pharmacotherapy is increasing common in the treatment of ADHD in both children and adults, there are still a lot of questions about side effects and how best to counter them. This suggests an important role for close monitoring of children and adults treated with stimulants or atomoxetine