Biological Magnetic Resonance Data Bank Research Articles

Perspective: on the importance of extensive, high-quality and reliable deposition of biomolecular NMR data in the age of artificial intelligence.

Artificial intelligence (AI) models are revolutionising scientific data analysis but are reliant on large training data sets. While artificial training data can be used in the context of NMR processing and data analysis methods, relating NMR parameters back to protein sequence and structure requires experimental data. In this perspective we examine what the biological NMR community needs to do, in order to store and share its data better so that we can make effective use of AI methods to further our understanding of biological molecules. We argue, first, that the community should be depositing much more of its experimental data. In particular, we should be depositing more spectra and dynamics data. Second, the NMR data deposited needs to capture the full information content required to be able to use and validate it adequately. The NMR Exchange Format (NEF) was designed several years ago to do this. The widespread adoption of NEF combined with a new proposal for dynamics data specifications come at the right time for the community to expand its deposition of data. Third, we highlight the importance of expanding and safeguarding our experimental data repository, the Biological Magnetic Resonance Data Bank (BMRB), not only in the interests of NMR spectroscopists, but biological scientists more widely. With this article we invite others in the biological NMR community to champion increased (possibly mandatory) data deposition, to get involved in designing new NEF specifications, and to advocate on behalf of the BMRB within the wider scientific community.

Integrative Modeling of Protein-Polypeptide Complexes by Bayesian Model Selection using AlphaFold and NMR Chemical Shift Perturbation Data.

Protein-polypeptide interactions, including those involving intrinsically-disordered peptides and intrinsically-disordered regions of protein binding partners, are crucial for many biological functions. However, experimental structure determination of protein-peptide complexes can be challenging. Computational methods, while promising, generally require experimental data for validation and refinement. Here we present CSP_Rank, an integrated modeling approach to determine the structures of protein-peptide complexes. This method combines AlphaFold2 (AF2) enhanced sampling methods with a Bayesian conformational selection process based on experimental Nuclear Magnetic Resonance (NMR) Chemical Shift Perturbation (CSP) data and AF2 confidence metrics. Using a curated dataset of 108 protein-peptide complexes from the Biological Magnetic Resonance Data Bank (BMRB), we observe that while AF2 typically yields models with excellent consistency with experimental CSP data, applying enhanced sampling followed by data-guided conformational selection routinely results in ensembles of structures with improved agreement with NMR observables. For two systems, we cross-validate the CSP-selected models using independently acquired nuclear Overhauser effect (NOE) NMR data and demonstrate how CSP and NMR can be combined using our Bayesian framework for model selection. CSP_Rank is a novel method for integrative modeling of protein-peptide complexes and has broad implications for studies of protein-peptide interactions and aiding in understanding their biological functions.

Phytochemical analysis and the first report of decyl gallate and 4-ethoxycoumarin from marine seagrass Posidonia oceanica collected from Benghazi beach Libya

The goal of this research is to examine the chemical composition of the plant and identify the phytochemical constituents of the endemic seagrass species in the Mediterranean Sea, Posidonia oceanica, which has historically been used for a variety of purposes. After doing chemical screening, the existence of triterpenoid and phenolic proteins was discovered. Using preparative thin-layer chromatography and flash column chromatography techniques, seven phenolic compounds were isolated from the methanolic extracts of several plant parts. Namely decyl gallate, sinapinic acid, alpha-methyl cinnamic acid, acetosyringone, and chlorogenic acid from leaves, 4-ethoxycoumarin from rhizomes, and syringic acid from roots. The structures of these compounds were elucidated using spectroscopic methods such as ‘H and 13C-NMR. The spectra were analyzed and visualized using Mnova software and compared with standard spectra from the Willy spectra database, biological magnetic resonance data bank, and chemical book. This is the first report of decyl gallate and 4-ethoxycoumarin from the plant. This study contributes to the understanding of the phytochemical profile of Posidonia oceanica and provides valuable insights into its potential applications in various fields, including medicine, agriculture, and industry.

Effects of Phosphorylation on Protein Backbone Dynamics and Conformational Preferences.

Phosphorylations are the most common and extensively studied post-translational modification (PTM) of proteins in eukaryotes. They constitute a major regulatory mechanism, modulating protein function, protein-protein interactions, as well as subcellular localization. Phosphorylation sites are preferably located in intrinsically disordered regions and have been shown to trigger structural rearrangements and order-to-disorder transitions. They can therefore have a significant effect on protein backbone dynamics or conformation, but only sparse experimental data are available. To obtain a more general description of how and when phosphorylations have a significant effect on protein behavior, molecular dynamics (MD) currently provides the only suitable framework to study these effects at a large scale in atomistic detail. This study develops a systematic MD simulation framework to explore the influence of phosphorylations on the local backbone dynamics and conformational propensities of proteins. Through a series of glycine-backbone peptides, we studied the effects of amino acid residues including the three most common phosphorylations (Ser, Thr, and Tyr), on local backbone dynamics and conformational propensities. We further extended our study to investigate the interactions of all such residues between position i to positions i + 1, i + 2, i + 3, and i + 4 in such peptides. The final data set comprises structural ensembles for 3393 sequences with more than 1 μs of sampling for each ensemble. To validate the relevance of the results, the structural and conformational properties extracted from the MD simulations are compared to NMR data from the Biological Magnetic Resonance Data Bank. The systematic nature of this study enables the projection of the gained knowledge onto any phosphorylation site in the proteome and provides a general framework for the study of further PTMs. The full data set is publicly available, as a training and reference set.

Metabolite fingerprinting and profiling of two locally cultivated edible plants by using nuclear magnetic resonance

Nuclear magnetic resonance (NMR) spectroscopy is a sensitive technique used to analyse the structure elucidation, dynamics, reaction state, and chemical environment of molecules. Abelmoschus esculentus and Lagenaria siceraria are edible plants used traditionally to treat jaundice, diabetes, weight loss, ulcer, hypertension, heart failure, skin diseases and reduced cholesterol. Therefore, based on the medicinal uses the study was designed to analyze fingerprinting of metabolites of the seeds of the selected plants. The dry seeds were powdered and the metabolites were extracted by socking method with a mixture of methanol/chloroform. The extracted metabolites from seeds were subjected to proton NMR using the noesygpprld pulse sequence. A total 18 peaks were obtained from each spectrum. Among the peaks, three peaks with the highest intensities were analyzed by utilizing NMR. The peak metabolites were determined with the correlation with the correct peak using in built Biological Magnetic Resonance Data Bank (BMRB). The results showed that the obtained data varied from known plant metabolites due to the contamination and interaction between the metabolites. In addition, variants in the metabolites from sample to sample may have been the result of errors or limitations in the study. The data generated from this experiment will be used to help to conduct the advanced research in the near future on the selected edible plant species which will be valuable for many different areas of the scientific community. Plant metabolomics has the potential benefit in the medical field, agricultural industry, and many other areas of our economy.

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).

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.

Nightshift: A Python program for plotting simulated NMR spectra from assigned chemical shifts from the Biological Magnetic Resonance Data Bank.

Nuclear magnetic resonance (NMR) provides site specific information on local environments through chemical shifts. NMR is widely used in the study of proteins, ranging from determination of three-dimensional (3D) structures to characterizing dynamics and binding of small molecules and other proteins or ligands. Assigned chemical shift data for the atoms within proteins is a treasure trove of information that can facilitate a broad range of biochemical and biophysical studies. The Biological Magnetic Resonance Data Bank (BMRB) is a publicly accessible database that contains a large number of assigned chemical shifts; however, translating this wealth of knowledge into a practical application is not straightforward. Herein we present nightshift: a Python command line utility and library for plotting simulated two-dimensional (2D) and 3D NMR spectra from assigned chemical shifts in the BMRB. This tool allows users to simulate routinely collected amide and methyl fingerprint spectra, backbone triple-resonance assignment spectra, and user-defined custom correlations, including ones that do not necessarily correspond to published experiments. This tool enables experienced NMR spectroscopists, those learning the craft, and interested scientists seeking to utilize NMR the ability to preview or examine a wide range of spectra for proteins whose assignments are deposited in the BMRB, irrespective of whether those experiments have been executed or reported. The tool applies equally to folded and intrinsically disordered proteins, limited only by the existence of a BMRB deposition. The features of nightshift are described along with applications that illustrate the ease with which complicated correlation spectra and binding events can be simulated.

NMR chemical shift assignments of RNA oligonucleotides to expand the RNA chemical shift database.

RNAs play myriad functional and regulatory roles in the cell. Despite their significance, three-dimensional structure elucidation of RNA molecules lags significantly behind that of proteins. NMR-based studies are often rate-limited by the assignment of chemical shifts. Automation of the chemical shift assignment process can greatly facilitate structural studies, however, accurate chemical shift predictions rely on a robust and complete chemical shift database for training. We searched the Biological Magnetic Resonance Data Bank (BMRB) to identify sequences that had no (or limited) chemical shift information. Here, we report the chemical shift assignments for 12 RNA hairpins designed specifically to help populate the BMRB.

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.

77Se NMR Probes the Protein Environment of Selenomethionine.

Sulfur is critical for the correct structure and proper function of proteins. Yet, lacking a sensitive enough isotope, nuclear magnetic resonance (NMR) experiments are unable to deliver for sulfur in proteins the usual wealth of chemical, dynamic, and structural information. This limitation can be circumvented by substituting sulfur with selenium, which has similar physicochemical properties and minimal impact on protein structures but possesses an NMR compatible isotope (77Se). Here we exploit the sensitivity of 77Se NMR to the nucleus' chemical milieu and use selenomethionine as a probe for its proteinaceous environment. However, such selenium NMR spectra of proteins currently resist a reliable interpretation because systematic connections between variations of system variables and changes in 77Se NMR parameters are still lacking. To start narrowing this knowledge gap, we report here on a biological 77Se magnetic resonance data bank based on a systematically designed library of GB1 variants in which a single selenomethionine was introduced at different locations within the protein. We recorded the resulting isotropic 77Se chemical shifts and relaxation times for six GB1 variants by solution-state 77Se NMR. For four of the GB1 variants we were also able to determine the chemical shift anisotropy tensor of SeM by solid-state 77Se NMR. To enable interpretation of the NMR data, the structures of five of the GB1 variants were solved by X-ray crystallography to a resolution of 1.2 Å, allowing us to unambiguously determine the conformation of the selenomethionine. Finally, we combine our solution- and solid-state NMR data with the structural information to arrive at general insights regarding the execution and interpretation of 77Se NMR experiments that exploit selenomethionine to probe proteins.

Structure⁻Activity Prediction of ACE Inhibitory/Bitter Dipeptides-A Chemometric Approach Based on Stepwise Regression.

Forward and backward stepwise regression (FR and BR, respectively) was applied for the structure–bioactivity prediction of angiotensin converting enzyme (ACE)-inhibitory/bitter-tasting dipeptides. The datasets used in this study consisted of 28 sequences and numerical variables reflecting dipeptides’ physicochemical nature. The data were acquired from the BIOPEP-UWM, Biological Magnetic Resonance Databank, ProtScale, and AAindex databases. The calculations were computed using STATISTICA®13.1. FR/BR models differed in R2 (0.91/0.76, respectively). The impact of C-atC(−) and N-Molw(+) on the dual function of dipeptides was observed. Positive (+) and negative (−) correlations with log IC50 are presented in parens. Moreover, C-Bur(+), N-atH(+), and N-Pol(−) were also found to be important in the FR model. The additional statistical significance of N-bul(−), N-Bur(−), and N-Hdr(+) was reported in the BR model. These attributes reflected the composition of the dipeptides. We report that the “ideal” bitter ACE inhibitor should be composed of P, Y, F (C-end) and G, V, I, L (N-end). Functions: log Rcaf. = f (observed log IC50) and log Rcaf. = f (predicted log IC50) revealed no direct relationships between ACE inhibition and the bitterness of the dipeptides. It probably resulted from some structural discrepancies between the ACE inhibitory/bitter peptides and/or the measure of activity describing one of the two bioactivities. Our protocol can be applicable for the structure–bioactivity prediction of other bioactivities peptides.

77Se-NMR Probes the Protein Environment of Selenomethionine

77Se NMR is highly sensitive to its chemical environment and can report on the interactions and degrees of freedom available for selenium-containing amino acids in biological systems. Particularly, selenomethionine can be used as a probe for methionine environment, an amino acid which is often found in protein-protein interfaces and in ligand binding sites. Yet, for these larger and more complex biological systems, reliable data interpretation that systematically connects variations of system variables to changes in the NMR spectra is still lacking. To build a biological 77Se magnetic resonance databank, we thus constructed a library of GB1 variants with a single selenomethionine at different locations within the protein. The structure of five of such GB1 variants was solved to 1.2 Å, allowing the conformation of the selenomethionine to be unambiguously determined. We recorded the isotropic 77Se chemical shift for six GB1 variants along with their T1 relaxation times and line width. For four of the GB1 variants we were able to determine the chemical shielding tensor of SeM by 77Se solid-state NMR. Finally, we combine solution and solid-state NMR with the structural information to arrive at general recommendations for the use of selenomethionine as NMR probe in biological systems.

Structural biology data archiving - where we are and what lies ahead.

For almost 50 years, structural biology has endeavoured to conserve and share its experimental data and their interpretations (usually, atomistic models) through global public archives such as the Protein Data Bank, Electron Microscopy Data Bank and Biological Magnetic Resonance Data Bank (BMRB). These archives are treasure troves of freely accessible data that document our quest for molecular or atomic understanding of biological function and processes in health and disease. They have prepared the field to tackle new archiving challenges as more and more (combinations of) techniques are being utilized to elucidate structure at ever increasing length scales. Furthermore, the field has made substantial efforts to develop validation methods that help users to assess the reliability of structures and to identify the most appropriate data for their needs. In this Review, we present an overview of public data archives in structural biology and discuss the importance of validation for users and producers of structural data. Finally, we sketch our efforts to integrate structural data with bioimaging data and with other sources of biological data. This will make relevant structural information available and more easily discoverable for a wide range of scientists.

Structural characteristics of food protein-originating di- and tripeptides using principal component analysis

This paper presents the use of principal component analysis (PCA) to study the effect of specific physicochemical attributes on bitterness of di- and tripeptides originating from food proteins. Peptide sequences were derived from the BIOPEP-UWM database of sensory peptides and amino acids. Descriptors defining the physicochemical properties of amino acids forming the analyzed peptides were study variables. They were derived from ProtScale program and Biological Magnetic Resonance Data Bank. Finally, PCA was carried out for 51 dipeptides/12 variables, and 51 tripeptides/18 variables using STATISTICA®13.1 software. PCA allowed reducing the input datasets to 4 principal components (PCs) for dipeptides and to 5 PCs for tripeptides. The impact of the following properties on the bitterness of peptides was observed: relatively high molecular weight, bulkiness, increasing number of carbon and hydrogen atoms of amino acids forming the sequences. These properties characterized the N- (negative correlations) and C-terminal residue (positive correlations) of both di- and tripeptides. An additional property affecting peptide bitterness was amino acids’ hydrophobicity. Our results were consistent with scientific reports on structure–bitterness of peptides. Thus, we find PCA a chemometric approach helpful in broadening the knowledge about the function of peptides resulting from their chemical nature.

Understanding the nature of bitter-taste di- and tripeptides derived from food proteins based on chemometric analysis.

Multiple linear regression (MLR) models were constructed to explain the bitter taste of di- and tripeptides based on their chemical nature (structure). Sequences (51 di- and 51 tripeptides) were derived from the BIOPEP-UWM database of sensory peptides and amino acids. The measure of their bitterness was Rcaf. , that is, bitterness relative to that of 1 mM caffeine solution (Rcaf. = 1.0). The variables were the indices describing properties of a single residue forming a peptide structure taken from ProtScale and Biological Magnetic Resonance Data Bank. MLR was made for two separate data sets by use of Statistica 13.1. We found that the presence of branched side residues or ring in a di- or tripeptide sequence (as in L, I, V, Y, F) affected its bitterness. Another variable affecting the bitter taste of di- and tripeptides was the hydrophobicity of amino acids. Using the commonly available statistical tools as well as chemical information reflecting the nature of peptides may be helpful in understanding the structure-taste relationship in food peptides. PRACTICAL APPLICATIONS: Our approach takes account of bioinformatic and cheminformatic techniques of data mining to analyze structure-bitterness of di- and tripeptides derived from food protein sources. Data on bitter peptides available in databases of biological and chemical information can be useful in creating models which help understanding the relationship between the role of structural properties of a molecule (e.g., peptide) and its function (e.g., taste). The bitterness of a peptide resulting from the presence of specific residues in its sequence, which represent different physicochemical properties may contribute to extending the knowledge about their taste-forming role in food systems. Such knowledge may be useful in designing food products with improved properties like taste which can be either enhanced or masked (considered as unwanted when thinking about the sensory value of foods). Our research strategy is universal and can also be applied to study structure-function relationships of peptides with other activities.

Structure and interaction of Corynebacterium pseudotuberculosis cold shock protein A with Y-box single-stranded DNA fragment.

Resonance assignment and structural data are available in the Biological Magnetic Resonance Data Bank and Protein Data Bank under accession number 26802 and 5O6F, respectively.

Biomolecular NMR: Past and future

The editors of this special volume suggested this topic, presumably because of the perspective lent by our combined >90-year association with biomolecular NMR. What follows is our personal experience with the evolution of the field, which we hope will illustrate the trajectory of change over the years. As for the future, one can confidently predict that it will involve unexpected advances. Our narrative is colored by our experience in using the NMR Facility for Biomedical Studies at Carnegie-Mellon University (Pittsburgh) and in developing similar facilities at Purdue (1977–1984) and the University of Wisconsin-Madison (1984-). We have enjoyed developing NMR technology and making it available to collaborators and users of these facilities. Our group's association with the Biological Magnetic Resonance data Bank (BMRB) and with the Worldwide Protein Data Bank (wwPDB) has also been rewarding. Of course, many groups contributed to the early growth and development of biomolecular NMR, and our brief personal account certainly omits many important milestones.

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

BackgroundThe 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.ResultsTo 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.ConclusionsThe 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.

Stereospecific assignment of the asparagine and glutamine sidechain amide protons in proteins from chemical shift analysis.

Side chain amide protons of asparagine and glutamine residues in random-coil peptides are characterized by large chemical shift differences and can be stereospecifically assigned on the basis of their chemical shift values only. The bimodal chemical shift distributions stored in the biological magnetic resonance data bank (BMRB) do not allow such an assignment. However, an analysis of the BMRB shows, that a substantial part of all stored stereospecific assignments is not correct. We show here that in most cases stereospecific assignment can also be done for folded proteins using an unbiased artificial chemical shift data base (UACSB). For a separation of the chemical shifts of the two amide resonance lines with differences ≥0.40ppm for asparagine and differences ≥0.42ppm for glutamine, the downfield shifted resonance lines can be assigned to Hδ21 and Hε21, respectively, at a confidence level >95%. A classifier derived from UASCB can also be used to correct the BMRB data. The program tool AssignmentChecker implemented in AUREMOL calculates the Bayesian probability for a given stereospecific assignment and automatically corrects the assignments for a given list of chemical shifts.