Biological Magnetic Resonance Data Bank.

The Biological Magnetic Resonance Data Bank (BMRB, https://bmrb.io) is the international open data repository for biomolecular nuclear magnetic resonance (NMR) data. Comprised of both empirical and derived data, BMRB has applications in the study of biomacromolecular structure and dynamics, biomolecular interactions, drug discovery, intrinsically disordered proteins, natural products, biomarkers, and metabolomics. Advances including GHz-class NMR instruments, national and trans-national NMR cyberinfrastructure, hybrid structural biology methods and machine learning are driving increases in the amount, type, and applications of NMR data in the biosciences. BMRB is a Core Archive and member of the World-wide Protein Data Bank (wwPDB).

Anomalous amide proton chemical shifts as signatures of hydrogen bonding to aromatic sidechains.

Hydrogen bonding between an amide group and the p-π cloud of an aromatic ring was first identified in a protein in the 1980s. Subsequent surveys of high-resolution X-ray crystal structures found multiple instances, but their preponderance was determined to be infrequent. Hydrogen atoms participating in a hydrogen bond to the p-π cloud of an aromatic ring are expected to experience an upfield chemical shift arising from a shielding ring current shift. We surveyed the Biological Magnetic Resonance Data Bank for amide hydrogens exhibiting unusual shifts as well as corroborating nuclear Overhauser effects between the amide protons and ring protons. We found evidence that Trp residues are more likely to be involved in p-π hydrogen bonds than other aromatic amino acids, whereas His residues are more likely to be involved in in-plane hydrogen bonds, with a ring nitrogen acting as the hydrogen acceptor. The p-π hydrogen bonds may be more abundant than previously believed. The inclusion in NMR structure refinement protocols of shift effects in amide protons from aromatic sidechains, or explicit hydrogen bond restraints between amides and aromatic rings, could improve the local accuracy of sidechain orientations in solution NMR protein structures, but their impact on global accuracy is likely be limited.

Merging NMR Data and Computation Facilitates Data-Centered Research.

The Biological Magnetic Resonance Data Bank (BMRB) has served the NMR structural biology community for 40 years, and has been instrumental in the development of many widely-used tools. It fosters the reuse of data resources in structural biology by embodying the FAIR data principles (Findable, Accessible, Inter-operable, and Re-usable). NMRbox is less than a decade old, but complements BMRB by providing NMR software and high-performance computing resources, facilitating the reuse of software resources. BMRB and NMRbox both facilitate reproducible research. NMRbox also fosters the development and deployment of complex meta-software. Combining BMRB and NMRbox helps speed and simplify workflows that utilize BMRB, and enables facile federation of BMRB with other data repositories. Utilization of BMRB and NMRbox in tandem will enable additional advances, such as machine learning, that are poised to become increasingly powerful.

Probabilistic identification of saccharide moieties in biomolecules and their protein complexes.

The chemical composition of saccharide complexes underlies their biomedical activities as biomarkers for cardiometabolic disease, various types of cancer, and other conditions. However, because these molecules may undergo major structural modifications, distinguishing between compounds of saccharide and non-saccharide origin becomes a challenging computational problem that hinders the aggregation of information about their bioactive moieties. We have developed an algorithm and software package called "Cheminformatics Tool for Probabilistic Identification of Carbohydrates" (CTPIC) that analyzes the covalent structure of a compound to yield a probabilistic measure for distinguishing saccharides and saccharide-derivatives from non-saccharides. CTPIC analysis of the RCSB Ligand Expo (database of small molecules found to bind proteins in the Protein Data Bank) led to a substantial increase in the number of ligands characterized as saccharides. CTPIC analysis of Protein Data Bank identified 7.7% of the proteins as saccharide-binding. CTPIC is freely available as a webservice at (http://ctpic.nmrfam.wisc.edu).

BioMagResBank (BMRB) as a Resource for Structural Biology.

The Biological Magnetic Resonance Data Bank (BioMagResBank or BMRB), founded in 1988, serves as the archive for data generated by nuclear magnetic resonance (NMR) spectroscopy of biological systems. NMR spectroscopy is unique among biophysical approaches in its ability to provide a broad range of atomic and higher-level information relevant to the structural, dynamic, and chemical properties of biological macromolecules, as well as report on metabolite and natural product concentrations in complex mixtures and their chemical structures. BMRB became a core member of the Worldwide Protein Data Bank (wwPDB) in 2007, and the BMRB archive is now a core archive of the wwPDB. Currently, about 10% of the structures deposited into the PDB archive are based on NMR spectroscopy. BMRB stores experimental and derived data from biomolecular NMR studies. Newer BMRB biopolymer depositions are divided about evenly between those associated with structure determinations (atomic coordinates and supporting information archived in the PDB) and those reporting experimental information on molecular dynamics, conformational transitions, ligand binding, assigned chemical shifts, or other results from NMR spectroscopy. BMRB also provides resources for NMR studies of metabolites and other small molecules that are often macromolecular ligands and/or nonstandard residues. This chapter is directed to the structural biology community rather than the metabolomics and natural products community. Our goal is to describe various BMRB services offered to structural biology researchers and how they can be accessed and utilized. These services can be classified into four main groups: (1) data deposition, (2) data retrieval, (3) data analysis, and (4) services for NMR spectroscopists and software developers. The chapter also describes the NMR-STAR data format used by BMRB and the tools provided to facilitate its use. For programmers, BMRB offers an application programming interface (API) and libraries in the Python and R languages that enable users to develop their own BMRB-based tools for data analysis, visualization, and manipulation of NMR-STAR formatted files. BMRB also provides users with direct access tools through the NMRbox platform.

Tools for Enhanced NMR-Based Metabolomics Analysis.

Metabolomics is the study of profiles of small molecules in biological fluids, cells, or organs. These profiles can be thought of as the "fingerprints" left behind from chemical processes occurring in biological systems. Because of its potential for groundbreaking applications in disease diagnostics, biomarker discovery, and systems biology, metabolomics has emerged as a rapidly growing area of research. Metabolomics investigations often, but not always, involve the identification and quantification of endogenous and exogenous metabolites in biological samples. Software tools and databases play a crucial role in advancing the rigor, robustness, reproducibility, and validation of these studies. Specifically, the establishment of a robust library of spectral signatures with unique compound descriptors and atom identities plays a key role in profiling studies based on data from nuclear magnetic resonance (NMR) spectroscopy. Here, we discuss developments leading to a rigorous basis for unique identification of compounds, reproducible numbering of atoms, the compact representation of NMR spectra of metabolites and small molecules, tools for improved compound identification, quantification and visualization, and approaches toward the goal of rigorous analysis of metabolomics data.

Automated evaluation of consistency within the PubChem Compound database.

Identification of discrepant data in aggregated databases is a key step in data curation and remediation. We have applied the ALATIS approach, which is based on the international chemical shift identifier (InChI) model, to the full PubChem Compound database to generate unique and reproducible compound and atom identifiers for all entries for which three-dimensional structures were available. This exercise also served to identify entries with discrepancies between structures and chemical formulas or InChI strings. The use of unique compound identifiers and atom nomenclature should support more rigorous links between small-molecule databases including those containing atom-specific information of the type available from crystallography and spectroscopy. The comprehensive results from this analysis are publicly available through our webserver [http://alatis.nmrfam.wisc.edu/].

NMR-STAR: comprehensive ontology for representing, archiving and exchanging data from nuclear magnetic resonance spectroscopic experiments.

The growth of the biological nuclear magnetic resonance (NMR) field and the development of new experimental technology have mandated the revision and enlargement of the NMR-STAR ontology used to represent experiments, spectral and derived data, and supporting metadata. We present here a brief description of the NMR-STAR ontology and software tools for manipulating NMR-STAR data files, editing the files, extracting selected data, and creating data visualizations. Detailed information on these is accessible from the links provided.

Applications of Parametrized NMR Spin Systems of Small Molecules.

We have developed technology for producing accurate spectral fingerprints of small molecules through modeling of NMR spin system matrices to encapsulate their chemical shifts and scalar couplings. We describe here how libraries of these spin systems utilizing unique and reproducible atom numbering can be used to improve NMR-based ligand screening and metabolomics studies. We introduce new Web services that facilitate the analysis of NMR spectra of mixtures of small molecules to yield their identification and quantification. The library of parametrized compounds has been expanded to cover simulations of 1H NMR spectra at a variety of magnetic fields of more than 1100 compounds, included are many common metabolites and a library of drug-like molecular fragments used in ligand screening. The compound library and related Web services are freely available from http://gissmo.nmrfam.wisc.edu/ .

Spin System Modeling of Nuclear Magnetic Resonance Spectra for Applications in Metabolomics and Small Molecule Screening.

The exceptionally rich information content of nuclear magnetic resonance (NMR) spectra is routinely used to identify and characterize molecules and molecular interactions in a wide range of applications, including clinical biomarker discovery, drug discovery, environmental chemistry, and metabolomics. The set of peak positions and intensities from a reference NMR spectrum generally serves as the identifying signature for a compound. Reference spectra normally are collected under specific conditions of pH, temperature, and magnetic field strength, because changes in conditions can distort the identifying signatures of compounds. A spin system matrix that parametrizes chemical shifts and coupling constants among spins provides a much richer feature set for a compound than a spectral signature based on peak positions and intensities. Spin system matrices expand the applicability of NMR spectral libraries beyond the specific conditions under which data were collected. In addition to being able to simulate spectra at any field strength, spin parameters can be adjusted to systematically explore alterations in chemical shift patterns due to variations in other experimental conditions, such as compound concentration, pH, or temperature. We present methodology and software for efficient interactive optimization of spin parameters against experimental 1D-1H NMR spectra of small molecules. We have used the software to generate spin system matrices for a set of key mammalian metabolites and are also using the software to parametrize spectra of small molecules used in NMR-based ligand screening. The software, along with optimized spin system matrix data for a growing number of compounds, is available from http://gissmo.nmrfam.wisc.edu/ .

MolProbity for the masses-of data.

MolProbity is a powerful software program for validating structures of proteins and nucleic acids. Although MolProbity includes scripts for batch analysis of structures, because these scripts analyze structures one at a time, they are not well suited for the validation of a large dataset of structures. We have created a version of MolProbity (MolProbity-HTC) that circumvents these limitations and takes advantage of a high-throughput computing cluster by using the HTCondor software. MolProbity-HTC enables the longitudinal analysis of large sets of structures, such as those deposited in the PDB or generated through theoretical computation-tasks that would have been extremely time-consuming using previous versions of MolProbity. We have used MolProbity-HTC to validate the entire PDB, and have developed a new visual chart for the BioMagResBank website that enables users to easily ascertain the quality of each model in an NMR ensemble and to compare the quality of those models to the rest of the PDB.

The NMR restraints grid at BMRB for 5,266 protein and nucleic acid PDB entries.

Several pilot experiments have indicated that improvements in older NMR structures can be expected by applying modern software and new protocols (Nabuurs et al. in Proteins 55:483-186, 2004; Nederveen et al. in Proteins 59:662-672, 2005; Saccenti and Rosato in J Biomol NMR 40:251-261, 2008). A recent large scale X-ray study also has shown that modern software can significantly improve the quality of X-ray structures that were deposited more than a few years ago (Joosten et al. in J. Appl Crystallogr 42:376-384, 2009; Sanderson in Nature 459:1038-1039, 2009). Recalculation of three-dimensional coordinates requires that the original experimental data are available and complete, and are semantically and syntactically correct, or are at least correct enough to be reconstructed. For multiple reasons, including a lack of standards, the heterogeneity of the experimental data and the many NMR experiment types, it has not been practical to parse a large proportion of the originally deposited NMR experimental data files related to protein NMR structures. This has made impractical the automatic recalculation, and thus improvement, of the three dimensional coordinates of these structures. We here describe a large-scale international collaborative effort to make all deposited experimental NMR data semantically and syntactically homogeneous, and thus useful for further research. A total of 4,014 out of 5,266 entries were 'cleaned' in this process. For 1,387 entries, human intervention was needed. Continuous efforts in automating the parsing of both old, and newly deposited files is steadily decreasing this fraction. The cleaned data files are available from the NMR restraints grid at http://restraintsgrid.bmrb.wisc.edu .