BioMagResBank

The preventive and therapeutic consumption of meat enriched with carob fruit extract, rich in phenolic compounds, improves colonic antioxidant status in late-stage T2DM rats

Oxidative stress is prevalent in Type 2 Diabetes Mellitus (T2DM) and has been associated with high meat consumption. Carob Fruit Extract (CFE) contains phenolic compounds, making it a suitable functional ingredient. Current study aims to evaluate the effect of CFE-enriched meat (CFE-meat) consumption on the antioxidant status of proximal and distal colon, and its relationship with fecal phenolic compounds in late-stage T2DM rats. Three groups of eight rats were studied: 1) D, fed control-meat; 2) ED, fed CFE-meat since the beginning of the study; 3) DE, fed CFE-meat after confirming T2DM. CFE-meat consumption reduces colonic oxidative stress mainly in the proximal section and helps to ameliorate glutathione metabolism and antioxidant score. Difference between ED and DE groups were associated with colon homeostasis and T2DM progression suggesting greater fermentation but lower absorption in the DE group. CFE appears as a promising tool to improve the antioxidant status observed in late-stage T2DM.

Solution-state methyl NMR spectroscopy of large non-deuterated proteins enabled by deep neural networks

Methyl-TROSY nuclear magnetic resonance (NMR) spectroscopy is a powerful technique for characterising large biomolecules in solution. However, preparing samples for these experiments is demanding and entails deuteration, limiting its use. Here we demonstrate that NMR spectra recorded on protonated, uniformly 13C labelled samples can be processed using deep neural networks to yield spectra that are of similar quality to typical deuterated methyl-TROSY spectra, potentially providing information for proteins that cannot be produced in bacterial systems. We validate the methodology experimentally on three proteins with molecular weights in the range 42-360 kDa. We further demonstrate the applicability of our methodology to 3D NOESY spectra of Escherichia coli Malate Synthase G (81 kDa), where observed NOE cross-peaks are in good agreement with the available structure. The method represents an advance in the field of using deep learning to analyse complex magnetic resonance data and could have an impact on the study of large biomolecules in years to come.

The Tautomeric State of N4-Hydroxycytidine within Base-Paired RNA

Antiviral nucleoside analogues (e.g., Molnupiravir, Remdesivir) played key roles in the treatment of COVID-19 by targeting SARS-CoV-2 RNA-dependent RNA polymerase (RdRp). The nucleoside of Molnupiravir, N4-hydroxycytidine (NHC), exists in two tautomeric forms that pair either with G or A within the RdRp active site, causing an accumulation of viral RNA mutations during replication. Detailed insights into the tautomeric states within base pairs and the structural influence of NHC in RNA are still missing. In this study, we investigate the properties of NHC:G and NHC:A base pairs in a self-complementary RNA duplex by UV thermal melting and NMR spectroscopy using atom-specifically 15N-labeled versions of NHC that were incorporated into oligonucleotides by solid-phase synthesis. NMR analysis revealed that NHC forms a Watson-Crick base pair with G via its amino form, whereas two equally populated conformations were detected for the NHC:A base pair: a weakly hydrogen-bonded Watson-Crick base pair with NHC in the imino form and another conformation with A shifted toward the minor groove. Moreover, we found a variable influence of NHC:G and NHC:A base pairs on the neighboring duplex environment. This study provides conclusive experimental evidence for the existence of two tautomeric forms of NHC within RNA base pairs.

α-Hydrazino Acid Insertion Governs Peptide Organization in Solution by Local Structure Ordering

In this work, we have applied the concept of α-hydrazino acid insertion in a peptide sequence as a means of structurally organizing a potential protein-protein interactions (PPI) inhibitor. Hydrazino peptides characterized by the incorporation of an α-hydrazino acid at specific positions introduce an additional nitrogen atom into their backbone. This modification leads to a change in the electrostatic properties of the peptide and induces the restructuring of its hydrogen bonding network, resulting in conformational changes toward more stable structural motifs. Despite the successful use of synthetic hydrazino oligomers in binding to nucleic acids, the structural changes due to the incorporation of α-hydrazino acid into short natural peptides in solution are still poorly understood. Based on NMR data, we report structural models of p53-derived hydrazino peptides with elements of localized peptide structuring in the form of an α-, β-, or γ-turn as a result of hydrazino modification in the peptide backbone. The modifications could potentially lead to the preorganization of a helical secondary peptide structure in a solution that is favorable for binding to a biological receptor. Spectroscopically, we observed that the ensemble averaged rapidly interconverting conformations, including isomerization of the E-Z hydrazide bond. This further increases the adaptability by expanding the conformational space of hydrazine peptides as potential protein-protein interaction antagonists.

On the Validation of Protein Force Fields Based on Structural Criteria

Molecular dynamics simulations depend critically on the quality of the force field used to describe the interatomic interactions and the extent to which it has been validated for use in a specific application. Using a curated test set of 52 high-resolution structures, 39 derived from X-ray diffraction and 13 solved using NMR, we consider the extent to which different parameter sets of the GROMOS protein force field can be distinguished based on comparing a range of structural criteria, including the number of backbone hydrogen bonds, the number of native hydrogen bonds, polar and nonpolar solvent-accessible surface area, radius of gyration, the prevalence of secondary structure elements, J-coupling constants, nuclear Overhauser effect (NOE) intensities, positional root-mean-square deviations (RMSD), and the distribution of backbone ϕ and ψ dihedral angles. It is shown that while statistically significant differences between the average values of individual metrics could be detected, these were in general small. Furthermore, improvements in agreement in one metric were often offset by loss of agreement in another. The work establishes a framework and test set against which protein force fields can be validated. It also highlights the danger of inferring the relative quality of a given force field based on a small range of structural properties or small number of proteins.

Investigating the Interactions of the Cucumber Mosaic Virus 2b Protein with the Viral 1a Replicase Component and the Cellular RNA Silencing Factor Argonaute 1

The cucumber mosaic virus (CMV) 2b protein is a suppressor of plant defenses and a pathogenicity determinant. Amongst the 2b protein's host targets is the RNA silencing factor Argonaute 1 (AGO1), which it binds to and inhibits. In Arabidopsis thaliana, if 2b-induced inhibition of AGO1 is too efficient, it induces reinforcement of antiviral silencing by AGO2 and triggers increased resistance against aphids, CMV's insect vectors. These effects would be deleterious to CMV replication and transmission, respectively, but are moderated by the CMV 1a protein, which sequesters sufficient 2b protein molecules into P-bodies to prevent excessive inhibition of AGO1. Mutant 2b protein variants were generated, and red and green fluorescent protein fusions were used to investigate subcellular colocalization with AGO1 and the 1a protein. The effects of mutations on complex formation with the 1a protein and AGO1 were investigated using bimolecular fluorescence complementation and co-immunoprecipitation assays. Although we found that residues 56-60 influenced the 2b protein's interactions with the 1a protein and AGO1, it appears unlikely that any single residue or sequence domain is solely responsible. In silico predictions of intrinsic disorder within the 2b protein secondary structure were supported by circular dichroism (CD) but not by nuclear magnetic resonance (NMR) spectroscopy. Intrinsic disorder provides a plausible model to explain the 2b protein's ability to interact with AGO1, the 1a protein, and other factors. However, the reasons for the conflicting conclusions provided by CD and NMR must first be resolved.

Identification of potential serum biomarkers associated with HbA1c levels in Indian type 2 diabetic subjects using NMR-based metabolomics

BACKGROUND

The prevalence of type 2 diabetes mellitus (T2DM), a progressive metabolic disorder characterized by chronic hyperglycemia and the development of insulin resistance, has increased globally, with worrying statistics coming from children, adolescents, and young adults from developing countries like India. Here, we investigated unique circulating metabolic signatures associated with prediabetes and T2DM in an Indian cohort using NMR-based metabolomics.

MATERIALS AND METHODS

The study subjects included healthy volunteers (N = 101), prediabetic subjects (N = 75), and T2DM patients (N = 108). Serum metabolic profiling was performed using 1H NMR spectroscopy and major perturbed metabolites were identified by multivariate analysis and receiver operating characteristic (ROC) modules.

RESULTS

Of the 36 aqueous abundant metabolites, 24 showed a statistically significant difference between healthy volunteers, prediabetics, and established T2DM subjects. On performing multivariate ROC curve analysis with 5 commonly dysregulated metabolites (namely, glucose, pyroglutamate, o-phosphocholine, serine, and methionine) in prediabetes and T2DM, AUC values obtained were 0.96 (95 % confidence interval (CI) = 0.93, 0.98) for T2DM; and 0.88 (95 % CI = 0.81, 0.93) for prediabetic subjects, respectively.

CONCLUSION

We propose that the identified metabolite panel can be used in the future as a biomarker for clinical diagnosis, patient surveillance, and for predicting individuals at risk for developing diabetes.

Cisplatin-Resistant Urothelial Bladder Cancer Cells Undergo Metabolic Reprogramming beyond the Warburg Effect

Advanced urothelial bladder cancer (UBC) patients are tagged by a dismal prognosis and high mortality rates, mostly due to their poor response to standard-of-care platinum-based therapy. Mediators of chemoresistance are not fully elucidated. This work aimed to study the metabolic profile of advanced UBC, in the context of cisplatin resistance. Three isogenic pairs of parental cell lines (T24, HT1376 and KU1919) and the matching cisplatin-resistant (R) sublines were used. A set of functional assays was used to perform a metabolic screening on the cells. In comparison to the parental sublines, a tendency was observed towards an exacerbated glycolytic metabolism in the cisplatin-resistant T24 and HT1376 cells; this glycolytic phenotype was particularly evident for the HT1376/HT1376R pair, for which the cisplatin resistance ratio was higher. HT1376R cells showed decreased basal respiration and oxygen consumption associated with ATP production; in accordance, the extracellular acidification rate was also higher in the resistant subline. Glycolytic rate assay confirmed that these cells presented higher basal glycolysis, with an increase in proton efflux. While the results of real-time metabolomics seem to substantiate the manifestation of the Warburg phenotype in HT1376R cells, a shift towards distinct metabolic pathways involving lactate uptake, lipid biosynthesis and glutamate metabolism occurred with time. On the other hand, KU1919R cells seem to engage in a metabolic rewiring, recovering their preference for oxidative phosphorylation. In conclusion, cisplatin-resistant UBC cells seem to display deep metabolic alterations surpassing the Warburg effect, which likely depend on the molecular signature of each cell line.

Automated nuclear magnetic resonance fingerprinting of mixtures

Nuclear magnetic resonance (NMR) spectroscopy is a powerful tool for qualitative and quantitative analysis. However, for complex mixtures, determining the speciation from NMR spectra can be tedious and sometimes even unfeasible. On the other hand, identifying and quantifying structural groups in a mixture from NMR spectra is much easier than doing the same for components. We call this group-based approach "NMR fingerprinting." In this work, we show that NMR fingerprinting can even be performed in an automated way, without expert knowledge, based only on standard NMR spectra, namely, 13C, 1H, and 13C DEPT NMR spectra. Our approach is based on the machine-learning method of support vector classification (SVC), which was trained here on thousands of labeled pure-component NMR spectra from open-source data banks. We demonstrate the applicability of the automated NMR fingerprinting using test mixtures, of which spectra were taken using a simple benchtop NMR spectrometer. The results from the NMR fingerprinting agree remarkably well with the ground truth, which was known from the gravimetric preparation of the samples. To facilitate the application of the method, we provide an interactive website (https://nmr-fingerprinting.de), where spectral information can be uploaded and which returns the NMR fingerprint. The NMR fingerprinting can be used in many ways, for example, for process monitoring or thermodynamic modeling using group-contribution methods-or simply as a first step in species analysis.

Structural analysis of a U-superfamily conotoxin containing a mini-granulin fold: Insights into key features that distinguish between the ICK and granulin folds

We are entering an exciting time in structural biology where artificial intelligence can be used to predict protein structures with greater accuracy than ever before. Extending this level of accuracy to the predictions of disulfide-rich peptide structures is likely to be more challenging, at least in the short term, given the tight packing of cysteine residues and the numerous ways that the disulfide bonds can potentially be linked. It has been previously shown in many cases that several disulfide bond connectivities can be accommodated by a single set of NMR-derived structural data without significant violations. Disulfide-rich peptides are prevalent throughout nature, and arguably the most well-known are those present in venoms from organisms such as cone snails. Here, we have determined the first three-dimensional structure and disulfide connectivity of a U-superfamily cone snail venom peptide, TxVIIB. TxVIIB has a VI/VII cysteine framework that is generally associated with an inhibitor cystine knot (ICK) fold; however, AlphaFold predicted that the peptide adopts a mini-granulin fold with a granulin disulfide connectivity. Our experimental studies using NMR spectroscopy and orthogonal protection of cysteine residues indicate that TxVIIB indeed adopts a mini-granulin fold but with the ICK disulfide connectivity. Our findings provide structural insight into the underlying features that govern formation of the mini-granulin fold rather than the ICK fold and will provide fundamental information for prediction algorithms, as the subtle complexity of disulfide isomers may be not adequately addressed by the current prediction algorithms.

Comprehensive Comparison and Critical Assessment of RNA-Specific Force Fields

Molecular dynamics simulations play a pivotal role in elucidating the dynamic behaviors of RNA structures, offering a valuable complement to traditional methods such as nuclear magnetic resonance or X-ray. Despite this, the current precision of RNA force fields lags behind that of protein force fields. In this work, we systematically compared the performance of four RNA force fields (ff99bsc0χOL3, AMBERDES, ff99OL3_CMAP1, AMBERMaxEnt) across diverse RNA structures. Our findings highlight significant challenges in maintaining stability, particularly with regard to cross-strand and cross-loop hydrogen bonds. Furthermore, we observed the limitations in accurately describing the conformations of nonhelical structural motif, terminal nucleotides, and also base pairing and base stacking interactions by the tested RNA force fields. The identified deficiencies in existing RNA force fields provide valuable insights for subsequent force field development. Concurrently, these findings offer recommendations for selecting appropriate force fields in RNA simulations.

NMR measurements of transient low-populated tautomeric and anionic Watson-Crick-like G·T/U in RNA:DNA hybrids: implications for the fidelity of transcription and CRISPR/Cas9 gene editing

Many biochemical processes use the Watson-Crick geometry to distinguish correct from incorrect base pairing. However, on rare occasions, mismatches such as G·T/U can transiently adopt Watson-Crick-like conformations through tautomerization or ionization of the bases, giving rise to replicative and translational errors. The propensities to form Watson-Crick-like mismatches in RNA:DNA hybrids remain unknown, making it unclear whether they can also contribute to errors during processes such as transcription and CRISPR/Cas editing. Here, using NMR R1ρ experiments, we show that dG·rU and dT·rG mismatches in two RNA:DNA hybrids transiently form tautomeric (Genol·T/U $ \mathbin{\lower.3ex\hbox{$\buildrel\textstyle\rightarrow\over {\smash{\leftarrow}\vphantom{_{\vbox to.5ex{\vss}}}}$}}$ G·Tenol/Uenol) and anionic (G·T-/U-) Watson-Crick-like conformations. The tautomerization dynamics were like those measured in A-RNA and B-DNA duplexes. However, anionic dG·rU- formed with a ten-fold higher propensity relative to dT-·rG and dG·dT- and this could be attributed to the lower pKa (ΔpKa ∼0.4-0.9) of U versus T. Our findings suggest plausible roles for Watson-Crick-like G·T/U mismatches in transcriptional errors and CRISPR/Cas9 off-target gene editing, uncover a crucial difference between the chemical dynamics of G·U versus G·T, and indicate that anionic Watson-Crick-like G·U- could play a significant role evading Watson-Crick fidelity checkpoints in RNA:DNA hybrids and RNA duplexes.

IHMCIF: An Extension of the PDBx/mmCIF Data Standard for Integrative Structure Determination Methods

IHMCIF (github.com/ihmwg/IHMCIF) is a data information framework that supports archiving and disseminating macromolecular structures determined by integrative or hybrid modeling (IHM), and making them Findable, Accessible, Interoperable, and Reusable (FAIR). IHMCIF is an extension of the Protein Data Bank Exchange/macromolecular Crystallographic Information Framework (PDBx/mmCIF) that serves as the framework for the Protein Data Bank (PDB) to archive experimentally determined atomic structures of biological macromolecules and their complexes with one another and small molecule ligands (e.g., enzyme cofactors and drugs). IHMCIF serves as the foundational data standard for the PDB-Dev prototype system, developed for archiving and disseminating integrative structures. It utilizes a flexible data representation to describe integrative structures that span multiple spatiotemporal scales and structural states with definitions for restraints from a variety of experimental methods contributing to integrative structural biology. The IHMCIF extension was created with the benefit of considerable community input and recommendations gathered by the Worldwide Protein Data Bank (wwPDB) Task Force for Integrative or Hybrid Methods (wwpdb.org/task/hybrid). Herein, we describe the development of IHMCIF to support evolving methodologies and ongoing advancements in integrative structural biology. Ultimately, IHMCIF will facilitate the unification of PDB-Dev data and tools with the PDB archive so that integrative structures can be archived and disseminated through PDB.

Structural Consequences of Introducing Bioactive Domains to Designer β-Sheet Peptide Self-Assemblies

We applied solid- and solution-state nuclear magnetic resonance spectroscopy to examine the structure of multidomain peptides composed of self-assembling β-sheet domains linked to bioactive domains. Bioactive domains can be selected to stimulate specific biological responses (e.g., via receptor binding), while the β-sheets provide the desirable nanoscale properties. Although previous work has established the efficacy of multidomain peptides, molecular-level characterization is lacking. The bioactive domains are intended to remain solvent-accessible without being incorporated into the β-sheet structure. We tested for three possible anticipated molecular-level consequences of introducing bioactive domains to β-sheet-forming peptides: (1) the bioactive domain has no effect on the self-assembling peptide structure; (2) the bioactive domain is incorporated into the β-sheet nanofiber; and (3) the bioactive domain interferes with self-assembly such that nanofibers are not formed. The peptides involved in this study incorporated self-assembling domains based on the (SL)6 motif and bioactive domains including a VEGF-A mimic (QK), an IGF-mimic (IGF-1c), and a de novo SARS-CoV-2 binding peptide (SBP3). We observed all three of the anticipated outcomes from our examination of peptides, illustrating the unintended structural effects that could adversely affect the desired biofunctionality and biomaterial properties of the resulting peptide hydrogel. This work is the first attempt to evaluate the structural effects of incorporating bioactive domains into a set of peptides unified by a similar self-assembling peptide domain. These structural insights reveal unmet challenges in the design of highly tunable bioactive self-assembling peptide hydrogels.

The Importance, Challenges, and Possible Solutions for Sharing Proteomics Data While Safeguarding Individuals' Privacy

Proteomics data sharing has profound benefits at the individual level as well as at the community level. While data sharing has increased over the years, mostly due to journal and funding agency requirements, the reluctance of researchers with regard to data sharing is evident as many shares only the bare minimum dataset required to publish an article. In many cases, proper metadata is missing, essentially making the dataset useless. This behavior can be explained by a lack of incentives, insufficient awareness, or a lack of clarity surrounding ethical issues. Through adequate training at research institutes, researchers can realize the benefits associated with data sharing and can accelerate the norm of data sharing for the field of proteomics, as has been the standard in genomics for decades. In this article, we have put together various repository options available for proteomics data. We have also added pros and cons of those repositories to facilitate researchers in selecting the repository most suitable for their data submission. It is also important to note that a few types of proteomics data have the potential to re-identify an individual in certain scenarios. In such cases, extra caution should be taken to remove any personal identifiers before sharing on public repositories. Data sets that will be useless without personal identifiers need to be shared in a controlled access repository so that only authorized researchers can access the data and personal identifiers are kept safe.

Interactions between the protein barnase and co-solutes studied by NMR

Protein solubility and stability depend on the co-solutes present. There is little theoretical basis for selection of suitable co-solutes. Some guidance is provided by the Hofmeister series, an empirical ordering of anions according to their effect on solubility and stability; and by osmolytes, which are small organic molecules produced by cells to allow them to function in stressful environments. Here, NMR titrations of the protein barnase with Hofmeister anions and osmolytes are used to measure and locate binding, and thus to separate binding and bulk solvent effects. We describe a rationalisation of Hofmeister (and inverse Hofmeister) effects, which is similar to the traditional chaotrope/kosmotrope idea but based on solvent fluctuation rather than water withdrawal, and characterise how co-solutes affect protein stability and solubility, based on solvent fluctuations. This provides a coherent explanation for solute effects, and points towards a more rational basis for choice of excipients.

Modeling blood metabolite homeostatic levels reduces sample heterogeneity across cohorts

Blood metabolite levels are affected by numerous factors, including preanalytical factors such as collection methods and geographical sites. These perturbations have caused deleterious consequences for many metabolomics studies and represent a major challenge in the metabolomics field. It is important to understand these factors and develop models to reduce their perturbations. However, to date, the lack of suitable mathematical models for blood metabolite levels under homeostasis has hindered progress. In this study, we develop quantitative models of blood metabolite levels in healthy adults based on multisite sample cohorts that mimic the current challenge. Five cohorts of samples obtained across four geographically distinct sites were investigated, focusing on approximately 50 metabolites that were quantified using 1H NMR spectroscopy. More than one-third of the variation in these metabolite profiles is due to cross-cohort variation. A dramatic reduction in the variation of metabolite levels (90%), especially their site-to-site variation (95%), was achieved by modeling each metabolite using demographic and clinical factors and especially other metabolites, as observed in the top principal components. The results also reveal that several metabolites contribute disproportionately to such variation, which could be explained by their association with biological pathways including biosynthesis and degradation. The study demonstrates an intriguing network effect of metabolites that can be utilized to better define homeostatic metabolite levels, which may have implications for improved health monitoring. As an example of the potential utility of the approach, we show that modeling gender-related metabolic differences retains the interesting variance while reducing unwanted (site-related) variance.

HR-MAS NMR Metabolomics Profile of Vero Cells under the Influence of Virus Infection and nsP2 Inhibitor: A Chikungunya Case Study

The arbovirus Chikungunya (CHIKV) is transmitted by Aedes mosquitoes in urban environments, and in humans, it triggers debilitating symptoms involving long-term complications, including arthritis and Guillain-Barré syndrome. The development of antiviral therapies is relevant, as no efficacious vaccine or drug has yet been approved for clinical application. As a detailed map of molecules underlying the viral infection can be obtained from the metabolome, we validated the metabolic signatures of Vero E6 cells prior to infection (CC), following CHIKV infection (CV) and also upon the inclusion of the nsP2 protease inhibitor wedelolactone (CWV), a coumestan which inhibits viral replication processes. The metabolome groups evidenced significant changes in the levels of lactate, myo-inositol, phosphocholine, glucose, betaine and a few specific amino acids. This study forms a preliminary basis for identifying metabolites through HR-MAS NMR (High Resolution Magic Angle Spinning Nuclear Magnetic Ressonance Spectroscopy) and proposing the affected metabolic pathways of cells following viral infection and upon incorporation of putative antiviral molecules.

COLMARppm: A Web Server Tool for the Accurate and Rapid Prediction of 1H and 13C NMR Chemical Shifts of Organic Molecules and Metabolites

Despite rapid progress in metabolomics research, a major bottleneck is the large number of metabolites whose chemical structures are unknown or whose spectra have not been deposited in metabolomics databases. Nuclear magnetic resonance (NMR) spectroscopy has a long history of elucidating chemical structures from experimentally measured 1H and 13C chemical shifts. One approach to characterizing the chemical structures of an unknown metabolite is to predict the 1H and 13C chemical shifts of candidate compounds (e.g., metabolites from the Human Metabolome Database (HMDB)) and compare them with chemical shifts of the unknown. However, accurate prediction of NMR chemical shifts in aqueous solution is challenging due to limitations of experimental chemical shift libraries and the high computational cost of quantum chemical methods. To improve NMR prediction accuracy and applicability, an empirical prediction strategy is introduced here to provide an accurately predicted chemical shift for organic molecules and metabolites within seconds. Unique features of COLMARppm include (i) the training library exclusively consisting of high quality NMR spectra measured under standard conditions in aqueous solution, (ii) utilization of NMR motif information, and (iii) leveraging of the improved prediction accuracy for the automated assignment of experimental chemical shifts for candidate structures. COLMARppm is demonstrated in terms of accuracy and speed for a set of 20 compounds taken from the HMDB for chemical shift prediction and resonance assignment. COLMARppm is applicable to a wide range of small molecules and can be directly incorporated into metabolomics workflows.

NMRium: Teaching nuclear magnetic resonance spectra interpretation in an online platform

NMRium is the first web-based software that allows displaying, processing, interpretation, and teaching of 1D and 2D NMR data in a user-friendly interface. It can import the most common data formats (e.g., JCAMP-DX, Bruker, Varian, and Jeol). While the scope for the use of NMRium encompasses a variety of applications such as being a component in data repositories or electronic lab notebooks (ELN), performing structure elucidation or preparing raw spectral data for publication, it also excels in enhancing teaching of NMR interpretation. In this paper, we present some current possibilities of this new tool. Several series of exercises are already provided on https://www.nmrium.org/teaching.

Atomistic molecular simulations of Aβ-Zn conformational ensembles

The amyloid-forming Aβ peptide is able to interact with metal cations to form very stable complexes that influence fibril formation and contribute to the onset of Alzheimer's disease. Multiple structures of peptides derived from Aβ in complex with different metals have been resolved experimentally to provide an atomic-level description of the metal-protein interactions. However, Aβ is intrinsically disordered, and hence more amenable to an ensemble description. Molecular dynamics simulations can now reach the timescales needed to generate ensembles for these type of complexes. However, this requires accurate force fields both for the protein and the protein-metal interactions. Here we use state-of-the-art methods to generate force field parameters for the Zn(II) cations in a set of complexes formed by different Aβ variants and combine them with the Amber99SB*-ILDN optimized force field. Upon comparison of NMR experiments with the simulation results, further optimized with a Bayesian/Maximum entropy approach, we provide an accurate description of the molecular ensembles for most Aβ-metal complexes. We find that the resulting conformational ensembles are more heterogeneous than the NMR models deposited in the Protein Data Bank.

Extended automated quantification algorithm (AQuA) for targeted 1H NMR metabolomics of highly complex samples: application to plant root exudates

INTRODUCTION

The Automated Quantification Algorithm (AQuA) is a rapid and efficient method for targeted NMR-based metabolomics, currently optimised for blood plasma. AQuA quantifies metabolites from 1D-1H NMR spectra based on the height of only one signal per metabolite, which minimises the computational time and workload of the method without compromising the quantification accuracy.

OBJECTIVES

To develop a fast and computationally efficient extension of AQuA for quantification of selected metabolites in highly complex samples, with minimal prior sample preparation. In particular, the method should be capable of handling interferences caused by broad background signals.

METHODS

An automatic baseline correction function was combined with AQuA into an automated workflow, the extended AQuA, for quantification of metabolites in plant root exudate NMR spectra that contained broad background signals and baseline distortions. The approach was evaluated using simulations as well as a spike-in experiment in which known metabolite amounts were added to a complex sample matrix.

RESULTS

The extended AQuA enables accurate quantification of metabolites in 1D-1H NMR spectra with varying complexity. The method is very fast (< 1 s per spectrum) and can be fully automated.

CONCLUSIONS

The extended AQuA is an automated quantification method intended for 1D-1H NMR spectra containing broad background signals and baseline distortions. Although the method was developed for plant root exudates, it should be readily applicable to any NMR spectra displaying similar issues as it is purely computational and applied to NMR spectra post-acquisition.

An RNA excited conformational state at atomic resolution

Sparse and short-lived excited RNA conformational states are essential players in cell physiology, disease, and therapeutic development, yet determining their 3D structures remains challenging. Combining mutagenesis, NMR spectroscopy, and computational modeling, we determined the 3D structural ensemble formed by a short-lived (lifetime ~2.1 ms) lowly-populated (~0.4%) conformational state in HIV-1 TAR RNA. Through a strand register shift, the excited conformational state completely remodels the 3D structure of the ground state (RMSD from the ground state = 7.2 ± 0.9 Å), forming a surprisingly more ordered conformational ensemble rich in non-canonical mismatches. The structure impedes the formation of the motifs recognized by Tat and the super elongation complex, explaining why this alternative TAR conformation cannot activate HIV-1 transcription. The ability to determine the 3D structures of fleeting RNA states using the presented methodology holds great promise for our understanding of RNA biology, disease mechanisms, and the development of RNA-targeting therapeutics.

The metabolic effects of intermittent versus continuous feeding in critically ill patients

Intermittent (or bolus) feeding regimens in critically ill patients have been of increasing interest to clinicians and scientists. Changes in amino acid, fat and carbohydrate metabolites over time might yet deliver other benefits (e.g. modulation of the circadian rhythm and sleep, and impacts on ghrelin secretion, insulin resistance and autophagy). We set out to characterise these changes in metabolite concentration. The Intermittent versus Continuous Feeding in Critically Ill paitents study (NCT02358512) was an eight-centre single-blinded randomised controlled trial. Patients were randomised to received a continuous (control arm) or intermittent (6x/day, intervention arm) enteral feeding regimen. Blood samples were taken on trial days 1, 7 and 10 immediately before and 30 min after intermittent feeds, and at equivalent timepoints in the control arm. A pre-planned targeted metabolomic analysis was performend using Nuclear Resonance Spectroscopy. Five hundred and ninety four samples were analysed from 75 patients. A total of 24 amino acid-, 19 lipid based-, and 44 small molecule metabolite features. Across the main two axes of variation (40-60% and 6-8% of variance), no broad patterns distinguished between intermittent or continuous feeding arms, across intra-day sampling times or over the 10 days from initial ICU admission. Logfold decreases in abundance were seen in metabolites related to amino acids (Glutamine - 0.682; Alanine - 0.594), ketone body metabolism (Acetone - 0.64; 3-Hydroxybutyric Acid - 0.632; Acetonacetic Acid - 0.586), fatty acid (carnitine - 0.509) and carbohydrate metabolism ( Maltose - 0.510; Citric Acid - 0.485). 2-3 Butanediol, a by-product of sugar-fermenting microbial metabolism also decreased (- 0.489). No correlation was seen with change in quadriceps muscle mass for any of the 20 metabolites varying with time (all p > 0.05). Increasing severity of organ failure was related to increasing ketone body metabolism (3 Hydroxybutyric Acid-1 and - 3; p = 0.056 and p = 0.014), carnitine deficiency (p = 0.002) and alanine abundancy (p - 0.005). A 6-times a day intermittent feeding regimen did not alter metabolite patterns across time compared to continuous feeding in critically ill patients, either within a 24 h period or across 10 days of intervention. Future research on intermittent feeding regimens should focus on clinical process benefits, or extended gut rest and fasting.

Identification of variety-specific metabolites of basil by high performance thin layer chromatography-assisted metabolic profiling techniques

The technological advances of analytical instrumentation and techniques has laid the ground for the rapid expansion of metabolomics or in a wider sense, untargeted analysis applied to life sciences themes. However, the objective of identifying all existing metabolites within organisms remains a daunting challenge. All analytical techniques exhibit varying degrees of sensitivity and versatility for the detection of metabolites and none of the existing analytical platforms can be expected to be ideal for exhaustive chemical profiling. Planar liquid chromatography, and in particular, high performance thin layer chromatography (HPTLC), has been used for chemical profiling of natural products in conjunction with metabolomics. HPTLC has specific advantages which include its ability to generate reliable chemical fingerprinting data and facilitate preparative work for metabolite isolation during later stages of metabolomics analysis. In this study, we investigated the chemical profiles of four commercially available basil cultivars, namely Dolly, Emily, Keira, and Rosie. We used HPTLC as the primary analytical tool for the separation of basil cultivars based on detected metabolites, and then compared the results with those obtained with other analytical platforms. We identified the characteristic marker compounds of each basil cultivar from the HPTLC plates and validated their potential using LC-MS and GC-MS analyses as a metabolomics tool. Firstly, we compared the HPTLC data of the four cultivars, obtained with two systems that used silica gel 60 and two mobile phases composed of toluene-EtOAc (8:2, v/v) and EtOAc-formic acid-acetic acid-water (100:11:11:27, v/v), with 1H NMR data to evaluate their separation power. Despite providing lower resolution and detecting fewer compounds, the HPTLC separation power was comparable, and in some cases even better than that of 1H NMR. Additionally, we investigated the potential of HPTLC as a tool for chemical fingerprinting and demonstrated its suitability for preparative purposes that are essential for identifying metabolites in mixture analysis. Metabolites were easily isolated from sample mixtures, and identified with the assistance of GC-MS, LC-MS, and TLC-densitometry.. Several marker compounds were thus identified, including 2,4 di-tertbutylphenol, palmitic acid, hexadecanamide, 9-octadecenamide, squalene, hentriacontane, methyl 3-(3,5-ditert‑butyl‑4-hydroxyphenyl)propanoic acid, sagerinic acid, and cyanidin-3-O-sophoroside.

Glucose metabolism sustains heme-induced Trypanosoma cruzi epimastigote growth in vitro

Chagas disease is caused by the protozoan parasite, Trypanosoma cruzi. This parasite alternates between an insect vector and a mammalian host. T. cruzi epimastigotes reside in the insect vector and coexist with the blood components of the vertebrate host. The metabolic profile of T. cruzi has been extensively studied; however, changes in its metabolism in response to signaling molecules present in the vector are poorly understood. Heme acts as a physiological oxidant that triggers intense epimastigote proliferation and upregulates the expression of genes related to glycolysis and aerobic fermentation in vitro. Here, heme-cultured epimastigotes increased D-glucose consumption. In fact, heme-cultured parasites secreted more succinate (the end product of the so-called succinic fermentation) followed by glucose intake. Increased succinate levels reduced the extracellular pH, leading to acidification of the supernatant. However, the acidification and proliferation stimulated by heme was impaired when glycolysis was inhibited. Otherwise, when glucose amount is enhanced in supernatant, heme-cultured parasites increased its growth whereas the glucose depletion caused a delay in proliferation. Heme supplementation increased epimastigote electron transport system-related O2 consumption rates, while glucose addition reduced both the electron transport system-related O2 consumption rates and spare respiratory capacity, indicating a Crabtree-like effect. These results show that glycolysis predominated in heme-cultured epimastigotes over oxidative phosphorylation for energy supply when glucose is present to sustain its high proliferation in vitro. Furthermore, it provided an insight into the parasite biology in the vector environment that supply glucose and the digestion of blood generates free heme that can lead to the growth of T. cruzi epimastigotes.

ARID3a from the ARID family: structure, role in autoimmune diseases and drug discovery

The AT-rich interaction domain (ARID) family of DNA-binding proteins is a group of transcription factors and chromatin regulators with a highly conserved ARID domain that recognizes specific AT-rich DNA sequences. Dysfunction of ARID family members has been implicated in various human diseases including cancers and intellectual disability. Among them, ARID3a has gained increasing attention due to its potential involvement in autoimmunity. In this article we provide an overview of the ARID family, focusing on the structure and biological functions of ARID3a. It explores the role of ARID3a in autoreactive B cells and its contribution to autoimmune diseases such as systemic lupus erythematosus and primary biliary cholangitis. Furthermore, we also discuss the potential for drug discovery targeting ARID3a and present a plan for future research in this field.

Ambidexterity and Left-Handedness Induced by Geminally Disubstituted γ Amino Acid Residues in Chiral 310 Helices

Chirality is an omnipresent feature in nature's architecture starting from simple molecules like amino acids to complex higher-order structures viz. proteins, DNA, and RNA. The L configuration of proteinogenic amino acids gives rise to right-handed helices. Ambidexterity is as rare in organisms as in molecules. There are only a few reports of ambidexterity in single-peptide molecules composed of either mixed L and D or achiral residues. Here, we report, for the first time, the ambidextrous and left-handed helical conformations in the chiral nonapeptides P1-P3 (Boc-LUVUγx,xULUV-OMe where U = Aib, x,x = 2,2/3,3/4,4), containing chiral L α amino acid residues, in addition to the usually observed right-handed helical conformation. The centrally located achiral γ residue, capable of adopting both left and right-handed helical conformations, induces its handedness on the neighboring chiral and achiral residues, leading to the observation of both left and right-handed helices in P2 and P3. The presence of a single water molecule proximal to the γ residue induces the reversal of helix handedness by forming distinct and stable water-mediated hydrogen bonds. This gives rise to ambidextrous helices as major conformers in P1 and P2. The absence of the observation of ambidexterity in P3 might be due to the inability of γ4,4 in the recruitment of a water molecule. Experiments (NMR, X-ray, and CD) and density functional theory (DFT) calculations suggest that the position of geminal disubstitution is crucial for determining the population of the amenable helical conformations (ambidextrous, left and right-handed) in these chiral peptides.

SPA-STOCSY: an automated tool for identifying annotated and non-annotated metabolites in high-throughput NMR spectra

MOTIVATION

Nuclear magnetic resonance spectroscopy (NMR) is widely used to analyze metabolites in biological samples, but the analysis requires specific expertise, it is time-consuming, and can be inaccurate. Here, we present a powerful automate tool, SPatial clustering Algorithm-Statistical TOtal Correlation SpectroscopY (SPA-STOCSY), which overcomes challenges faced when analyzing NMR data and identifies metabolites in a sample with high accuracy.

RESULTS

As a data-driven method, SPA-STOCSY estimates all parameters from the input dataset. It first investigates the covariance pattern among datapoints and then calculates the optimal threshold with which to cluster datapoints belonging to the same structural unit, i.e. the metabolite. Generated clusters are then automatically linked to a metabolite library to identify candidates. To assess SPA-STOCSY's efficiency and accuracy, we applied it to synthesized spectra and spectra acquired on Drosophila melanogaster tissue and human embryonic stem cells. In the synthesized spectra, SPA outperformed Statistical Recoupling of Variables (SRV), an existing method for clustering spectral peaks, by capturing a higher percentage of the signal regions and the close-to-zero noise regions. In the biological data, SPA-STOCSY performed comparably to the operator-based Chenomx analysis while avoiding operator bias, and it required <7 min of total computation time. Overall, SPA-STOCSY is a fast, accurate, and unbiased tool for untargeted analysis of metabolites in the NMR spectra. It may thus accelerate the use of NMR for scientific discoveries, medical diagnostics, and patient-specific decision making.

AVAILABILITY AND IMPLEMENTATION

The codes of SPA-STOCSY are available at https://github.com/LiuzLab/SPA-STOCSY.

Application of NMR and Chemometric Analyses to Better Understand the Quality Attributes in pH and Thermally Degraded Monoclonal Antibodies

PURPOSE

Nuclear magnetic resonance (NMR) spectroscopy provides the sensitivity and specificity to probe the higher order structure (HOS) of monoclonal antibodies (mAbs) for potential changes. This study demonstrates an application of chemometric tools to measure differences in the NMR spectra of mAbs after forced degradation relative to the respective unstressed starting materials.

METHODS

Samples of adalimumab (Humira, ADL-REF) and trastuzumab (Herceptin, TRA-REF) were incubated in three buffer-pH conditions at 40°C for 4 weeks to compare to a control sample that was left unstressed. Replicate 1D 1H and 2D 1H-13C HMQC NMR spectra were collected on all samples. Chemometric analyses such as Easy Comparability of HOS (ECHOS), PROtein FIngerprinting by Lineshape Enhancement (PROFILE), and Principal Component Analysis (PCA) were applied to capture and quantitate differences between the spectra.

RESULTS

Visual and statistical inspection of the 2D 1H-13C HMQC spectra of adalimumab and trastuzumab after forced degradation conditions shows no changes in the spectra relative to the unstressed material. Chemometric analysis of the 1D 1H NMR spectra shows only minor changes in the spectra of adalimumab after forced degradation, but significant differences in trastuzumab.

CONCLUSION

The chemometric analyses support the lack of statistical differences in the structure of pH-thermal stressed adalimumab, however, it reveals conformational changes or chemical modifications in trastuzumab after forced degradation. Application of chemometrics in comparative NMR studies enables HOS characterization and showcases the sensitivity and specificity in detecting differences in the spectra of mAbs after pH-thermal forced degradation with respect to local and global protein structure.

High-physiological and supra-physiological 1,2-13C2 glucose focal supplementation to the traumatised human brain

How to optimise glucose metabolism in the traumatised human brain remains unclear, including whether injured brain can metabolise additional glucose when supplied. We studied the effect of microdialysis-delivered 1,2-13C2 glucose at 4 and 8 mmol/L on brain extracellular chemistry using bedside ISCUSflex, and the fate of the 13C label in the 8 mmol/L group using high-resolution NMR of recovered microdialysates, in 20 patients. Compared with unsupplemented perfusion, 4 mmol/L glucose increased extracellular concentrations of pyruvate (17%, p = 0.04) and lactate (19%, p = 0.01), with a small increase in lactate/pyruvate ratio (5%, p = 0.007). Perfusion with 8 mmol/L glucose did not significantly influence extracellular chemistry measured with ISCUSflex, compared to unsupplemented perfusion. These extracellular chemistry changes appeared influenced by the underlying metabolic states of patients' traumatised brains, and the presence of relative neuroglycopaenia. Despite abundant 13C glucose supplementation, NMR revealed only 16.7% 13C enrichment of recovered extracellular lactate; the majority being glycolytic in origin. Furthermore, no 13C enrichment of TCA cycle-derived extracellular glutamine was detected. These findings indicate that a large proportion of extracellular lactate does not originate from local glucose metabolism, and taken together with our earlier studies, suggest that extracellular lactate is an important transitional step in the brain's production of glutamine.

Minimum information guidelines for experiments structurally characterizing intrinsically disordered protein regions

An unambiguous description of an experiment, and the subsequent biological observation, is vital for accurate data interpretation. Minimum information guidelines define the fundamental complement of data that can support an unambiguous conclusion based on experimental observations. We present the Minimum Information About Disorder Experiments (MIADE) guidelines to define the parameters required for the wider scientific community to understand the findings of an experiment studying the structural properties of intrinsically disordered regions (IDRs). MIADE guidelines provide recommendations for data producers to describe the results of their experiments at source, for curators to annotate experimental data to community resources and for database developers maintaining community resources to disseminate the data. The MIADE guidelines will improve the interpretability of experimental results for data consumers, facilitate direct data submission, simplify data curation, improve data exchange among repositories and standardize the dissemination of the key metadata on an IDR experiment by IDR data sources.

Analysis of the Dynamics of the Human Growth Hormone Secretagogue Receptor Reveals Insights into the Energy Landscape of the Molecule

G protein-coupled receptors initiate signal transduction in response to ligand binding. Growth hormone secretagogue receptor (GHSR), the focus of this study, binds the 28 residue peptide ghrelin. While structures of GHSR in different states of activation are available, dynamics within each state have not been investigated in depth. We analyze long molecular dynamics simulation trajectories using "detectors" to compare dynamics of the apo and ghrelin-bound states yielding timescale-specific amplitudes of motion. We identify differences in dynamics between apo and ghrelin-bound GHSR in the extracellular loop 2 and transmembrane helices 5-7. NMR of the GHSR histidine residues reveals chemical shift differences in these regions. We evaluate timescale specific correlation of motions between residues of ghrelin and GHSR, where binding yields a high degree of correlation for the first 8 ghrelin residues, but less correlation for the helical end. Finally, we investigate the traverse of GHSR over a rugged energy landscape via principal component analysis.

Applications of Solution NMR Spectroscopy in Quality Assessment and Authentication of Bovine Milk

Nuclear magnetic resonance (NMR) spectroscopy is emerging as a promising technique for the analysis of bovine milk, primarily due to its non-destructive nature, minimal sample preparation requirements, and comprehensive approach to untargeted milk analysis. These inherent strengths of NMR make it a formidable complementary tool to mass spectrometry-based techniques in milk metabolomic studies. This review aims to provide a comprehensive overview of the applications of NMR techniques in the quality assessment and authentication of bovine milk. It will focus on the experimental setup and data processing techniques that contribute to achieving accurate and highly reproducible results. The review will also highlight key studies that have utilized commonly used NMR methodologies in milk analysis, covering a wide range of application fields. These applications include determining milk animal species and feeding regimes, as well as assessing milk nutritional quality and authenticity. By providing an overview of the diverse applications of NMR in milk analysis, this review aims to demonstrate the versatility and significance of NMR spectroscopy as an invaluable tool for milk and dairy metabolomics research and hence, for assessing the quality and authenticity of bovine milk.

Mouse sperm energy restriction and recovery (SER) revealed novel metabolic pathways

Mammalian sperm must undergo capacitation to become fertilization-competent. While working on mice, we recently developed a new methodology for treating sperm in vitro, which results in higher rates of fertilization and embryo development after in vitro fertilization. Sperm incubated in media devoid of nutrients lose motility, although they remain viable. Upon re-adding energy substrates, sperm resume motility and become capacitated with improved functionality. Here, we explore how sperm energy restriction and recovery (SER) treatment affects sperm metabolism and capacitation-associated signaling. Using extracellular flux analysis and metabolite profiling and tracing via nuclear magnetic resonance (NMR) and mass spectrometry (MS), we found that the levels of many metabolites were altered during the starvation phase of SER. Of particular interest, two metabolites, AMP and L-carnitine, were significantly increased in energy-restricted sperm. Upon re-addition of glucose and initiation of capacitation, most metabolite levels recovered and closely mimic the levels observed in capacitating sperm that have not undergone starvation. In both control and SER-treated sperm, incubation under capacitating conditions upregulated glycolysis and oxidative phosphorylation. However, ATP levels were diminished, presumably reflecting the increased energy consumption during capacitation. Flux data following the fate of 13C glucose indicate that, similar to other cells with high glucose consumption rates, pyruvate is converted into 13C-lactate and, with lower efficiency, into 13C-acetate, which are then released into the incubation media. Furthermore, our metabolic flux data show that exogenously supplied glucose is converted into citrate, providing evidence that in sperm cells, as in somatic cells, glycolytic products can be converted into Krebs cycle metabolites.

DeepSAT: Learning Molecular Structures from Nuclear Magnetic Resonance Data

The identification of molecular structure is essential for understanding chemical diversity and for developing drug leads from small molecules. Nevertheless, the structure elucidation of small molecules by Nuclear Magnetic Resonance (NMR) experiments is often a long and non-trivial process that relies on years of training. To achieve this process efficiently, several spectral databases have been established to retrieve reference NMR spectra. However, the number of reference NMR spectra available is limited and has mostly facilitated annotation of commercially available derivatives. Here, we introduce DeepSAT, a neural network-based structure annotation and scaffold prediction system that directly extracts the chemical features associated with molecular structures from their NMR spectra. Using only the 1H-13C HSQC spectrum, DeepSAT identifies related known compounds and thus efficiently assists in the identification of molecular structures. DeepSAT is expected to accelerate chemical and biomedical research by accelerating the identification of molecular structures.

Improved methodology for protein NMR structure calculation using hydrogen bond restraints and ANSURR validation: The SH2 domain of SH2B1

Protein structures calculated using NMR data are less accurate and less well-defined than they could be. Here we use the program ANSURR to show that this deficiency is at least in part due to a lack of hydrogen bond restraints. We describe a protocol to introduce hydrogen bond restraints into the structure calculation of the SH2 domain from SH2B1 in a systematic and transparent way and show that the structures generated are more accurate and better defined as a result. We also show that ANSURR can be used as a guide to know when the structure calculation is good enough to stop.

Structural features, intrinsic disorder, and modularity of a pyriform spidroin 1 core repetitive domain

Orb-weaving spiders produce up to seven silk types, each with distinct biological roles, protein compositions, and mechanics. Pyriform (or piriform) silk is composed of pyriform spidroin 1 (PySp1) and is the fibrillar component of attachment discs that attach webs to substrates and to each other. Here, we characterize the 234-residue repeat unit (the "Py unit") from the core repetitive domain of Argiope argentata PySp1. Solution-state nuclear magnetic resonance (NMR) spectroscopy-based backbone chemical shift and dynamics analysis demonstrate a structured core flanked by disordered tails, structuring that is maintained in a tandem protein of two connected Py units, indicative of structural modularity of the Py unit in the context of the repetitive domain. Notably, AlphaFold2 predicts the Py unit structure with low confidence, echoing low confidence and poor agreement to the NMR-derived structure for the Argiope trifasciata aciniform spidroin (AcSp1) repeat unit. Rational truncation, validated through NMR spectroscopy, provided a 144-residue construct retaining the Py unit core fold, enabling near-complete backbone and side chain 1H, 13C, and 15N resonance assignment. A six α-helix globular core is inferred, flanked by regions of intrinsic disorder that would link helical bundles in tandem repeat proteins in a beads-on-a-string architecture.

Integrated NMR and MS Analysis of the Plasma Metabolome Reveals Major Changes in One-Carbon, Lipid, and Amino Acid Metabolism in Severe and Fatal Cases of COVID-19

Brazil has the second-highest COVID-19 death rate worldwide, and Rio de Janeiro is among the states with the highest rate in the country. Although vaccine coverage has been achieved, it is anticipated that COVID-19 will transition into an endemic disease. It is concerning that the molecular mechanisms underlying clinical evolution from mild to severe disease, as well as the mechanisms leading to long COVID-19, are not yet fully understood. NMR and MS-based metabolomics were used to identify metabolites associated with COVID-19 pathophysiology and disease outcome. Severe COVID-19 cases (n = 35) were enrolled in two reference centers in Rio de Janeiro within 72 h of ICU admission, alongside 12 non-infected control subjects. COVID-19 patients were grouped into survivors (n = 18) and non-survivors (n = 17). Choline-related metabolites, serine, glycine, and betaine, were reduced in severe COVID-19, indicating dysregulation in methyl donors. Non-survivors had higher levels of creatine/creatinine, 4-hydroxyproline, gluconic acid, and N-acetylserine, indicating liver and kidney dysfunction. Several changes were greater in women; thus, patients' sex should be considered in pandemic surveillance to achieve better disease stratification and improve outcomes. These metabolic alterations may be useful to monitor organ (dys) function and to understand the pathophysiology of acute and possibly post-acute COVID-19 syndromes.

Multi-omics quantitative data of tomato fruit unveils regulation modes of least variable metabolites

BACKGROUND

The composition of ripe fruits depends on various metabolites which content evolves greatly throughout fruit development and may be influenced by the environment. The corresponding metabolism regulations have been widely described in tomato during fruit growth and ripening. However, the regulation of other metabolites that do not show large changes in content have scarcely been studied.

RESULTS

We analysed the metabolites of tomato fruits collected on different trusses during fruit development, using complementary analytical strategies. We identified the 22 least variable metabolites, based on their coefficients of variation. We first verified that they had a limited functional link with the least variable proteins and transcripts. We then posited that metabolite contents could be stabilized through complex regulations and combined their data with the quantitative proteome or transcriptome data, using sparse partial-least-square analyses. This showed shared regulations between several metabolites, which interestingly remained linked to early fruit development. We also examined regulations in specific metabolites using correlations with individual proteins and transcripts, which revealed that a stable metabolite does not always correlate with proteins and transcripts of its known related pathways.

CONCLUSIONS

The regulation of the least variable metabolites was then interpreted regarding their roles as hubs in metabolic pathways or as signalling molecules.

Characterization of trehalolipid biosurfactant produced by the novel marine strain Rhodococcus sp. SP1d and its potential for environmental applications

BACKGROUND

Biosurfactants are surface-active compounds with environmental and industrial applications. These molecules show higher biocompatibility, stability and efficiency compared to synthetic surfactants. On the other hand, biosurfactants are not cost-competitive to their chemical counterparts. Cost effective technology such as the use of low-cost substrates is a promising approach aimed at reducing the production cost. This study aimed to evaluate the biosurfactant production and activity by the novel strain Rhodococcus sp. SP1d by using different growth substrates. Therefore, to exploit the biosurfactant synthesized by SP1d for environmental applications, the effect of this compound on the bacteria biofilm formation was evaluated. Eventually, for a possible bioremediation application, the biosurfactant properties and its chemical characteristics were investigated using diesel as source of carbon.

RESULTS

Rhodococcus sp. SP1d evidence the highest similarity to Rhodococcus globerulus DSM 43954^T and the ability to biosynthesize surfactants using a wide range of substrates such as exhausted vegetable oil, mineral oil, butter, n-hexadecane, and diesel. The maximum production of crude biosurfactant after 10 days of incubation was reached on n-hexadecane and diesel with a final yield of 2.38 ± 0.51 and 1.86 ± 0.31 g L^- 1 respectively. Biosurfactants produced by SP1d enhanced the biofilm production of P. protegens MP12. Moreover, the results showed the ability of SP1d to produce biosurfactants on diesel even when grown at 10 and 18 °C. The biosurfactant activity was maintained over a wide range of NaCl concentration, pH, and temperature. A concentration of 1000 mg L^- 1 of the crude biosurfactant showed an emulsification activity of 55% towards both xylene and olive oil and a reduction of 25.0 mN m^- 1 of surface tension of water. Eventually, nuclear magnetic resonance spectroscopy indicated that the biosurfactant is formed by trehalolipids.

CONCLUSIONS

The use of low-cost substrates such as exhausted oils and waste butter reduce both the costs of biosurfactant synthesis and the environmental pollution due to the inappropriate disposal of these residues. High production yields, stability and emulsification properties using diesel and n-hexadecane as substrates, make the biosurfactant produced by SP1d a sustainable biocompound for bioremediation purpose. Eventually, the purified biosurfactant improved the biofilm formation of the fungal antagonistic strain P. protegens MP12, and thus seem to be exploitable to increase the adherence and colonization of plant surfaces by this antagonistic strain and possibly enhance antifungal activity.

Blind assessment of monomeric AlphaFold2 protein structure models with experimental NMR data

Recent advances in molecular modeling of protein structures are changing the field of structural biology. AlphaFold-2 (AF2), an AI system developed by DeepMind, Inc., utilizes attention-based deep learning to predict models of protein structures with high accuracy relative to structures determined by X-ray crystallography and cryo-electron microscopy (cryoEM). Comparing AF2 models to structures determined using solution NMR data, both high similarities and distinct differences have been observed. Since AF2 was trained on X-ray crystal and cryoEM structures, we assessed how accurately AF2 can model small, monomeric, solution protein NMR structures which (i) were not used in the AF2 training data set, and (ii) did not have homologous structures in the Protein Data Bank at the time of AF2 training. We identified nine open-source protein NMR data sets for such "blind" targets, including chemical shift, raw NMR FID data, NOESY peak lists, and (for 1 case) 15N-1H residual dipolar coupling data. For these nine small (70-108 residues) monomeric proteins, we generated AF2 prediction models and assessed how well these models fit to these experimental NMR data, using several well-established NMR structure validation tools. In most of these cases, the AF2 models fit the NMR data nearly as well, or sometimes better than, the corresponding NMR structure models previously deposited in the Protein Data Bank. These results provide benchmark NMR data for assessing new NMR data analysis and protein structure prediction methods. They also document the potential for using AF2 as a guiding tool in protein NMR data analysis, and more generally for hypothesis generation in structural biology research.

Plasma metabolomics by nuclear magnetic resonance reveals biomarkers and metabolic pathways associated with the control of HIV-1 infection/progression

How the human body reacts to the exposure of HIV-1 is an important research goal. Frequently, HIV exposure leads to infection, but some individuals show natural resistance to this infection; they are known as HIV-1-exposed but seronegative (HESN). Others, although infected but without antiretroviral therapy, control HIV-1 replication and progression to AIDS; they are named controllers, maintaining low viral levels and an adequate count of CD4⁺ T lymphocytes. Biological mechanisms explaining these phenomena are not precise. In this context, metabolomics emerges as a method to find metabolites in response to pathophysiological stimuli, which can help to establish mechanisms of natural resistance to HIV-1 infection and its progression. We conducted a cross-sectional study including 30 HESN, 14 HIV-1 progressors, 14 controllers and 30 healthy controls. Plasma samples (directly and deproteinized) were analyzed through Nuclear Magnetic Resonance (NMR) metabolomics to find biomarkers and altered metabolic pathways. The metabolic profile analysis of progressors, controllers and HESN demonstrated significant differences with healthy controls when a discriminant analysis (PLS-DA) was applied. In the discriminant models, 13 metabolites associated with HESN, 14 with progressors and 12 with controllers were identified, which presented statistically significant mean differences with healthy controls. In progressors, the metabolites were related to high energy expenditure (creatinine), mood disorders (tyrosine) and immune activation (lipoproteins), phenomena typical of the natural course of the infection. In controllers, they were related to an inflammation-modulating profile (glutamate and pyruvate) and a better adaptive immune system response (acetate) associated with resistance to progression. In the HESN group, with anti-inflammatory (lactate and phosphocholine) and virucidal (lactate) effects which constitute a protective profile in the sexual transmission of HIV. Concerning the significant metabolites of each group, we identified 24 genes involved in HIV-1 replication or virus proteins that were all altered in progressors but only partially in controllers and HESN. In summary, our results indicate that exposure to HIV-1 in HESN, as well as infection in progressors and controllers, affects the metabolism of individuals and that this affectation can be determined using NMR metabolomics.

Molecular docking in organic, inorganic, and hybrid systems: a tutorial review

Molecular docking simulation is a very popular and well-established computational approach and has been extensively used to understand molecular interactions between a natural organic molecule (ideally taken as a receptor) such as an enzyme, protein, DNA, RNA and a natural or synthetic organic/inorganic molecule (considered as a ligand). But the implementation of docking ideas to synthetic organic, inorganic, or hybrid systems is very limited with respect to their use as a receptor despite their huge popularity in different experimental systems. In this context, molecular docking can be an efficient computational tool for understanding the role of intermolecular interactions in hybrid systems that can help in designing materials on mesoscale for different applications. The current review focuses on the implementation of the docking method in organic, inorganic, and hybrid systems along with examples from different case studies. We describe different resources, including databases and tools required in the docking study and applications. The concept of docking techniques, types of docking models, and the role of different intermolecular interactions involved in the docking process to understand the binding mechanisms are explained. Finally, the challenges and limitations of dockings are also discussed in this review.

Graphical abstract

ADOPT: intrinsic protein disorder prediction through deep bidirectional transformers

Intrinsically disordered proteins (IDPs) are important for a broad range of biological functions and are involved in many diseases. An understanding of intrinsic disorder is key to develop compounds that target IDPs. Experimental characterization of IDPs is hindered by the very fact that they are highly dynamic. Computational methods that predict disorder from the amino acid sequence have been proposed. Here, we present ADOPT (Attention DisOrder PredicTor), a new predictor of protein disorder. ADOPT is composed of a self-supervised encoder and a supervised disorder predictor. The former is based on a deep bidirectional transformer, which extracts dense residue-level representations from Facebook's Evolutionary Scale Modeling library. The latter uses a database of nuclear magnetic resonance chemical shifts, constructed to ensure balanced amounts of disordered and ordered residues, as a training and a test dataset for protein disorder. ADOPT predicts whether a protein or a specific region is disordered with better performance than the best existing predictors and faster than most other proposed methods (a few seconds per sequence). We identify the features that are relevant for the prediction performance and show that good performance can already be gained with <100 features. ADOPT is available as a stand-alone package at https://github.com/PeptoneLtd/ADOPT and as a web server at https://adopt.peptone.io/.

Development of a root exudate collection protocol for metabolomics analysis using Nuclear Magnetic Resonance

Large amounts of root exudates are released by plant roots into the soil. Due to their importance in regulating the rhizosphere properties, it is necessary to unravel the precise composition and function of exudates at the root-soil interface. However, obtaining root exudates without inducing artefacts is a difficult task. To analyse the low molecular weight molecules secreted by pea roots, a protocol of root exudate collection was developed to perform a metabolomics analysis using Nuclear Magnetic Resonance (NMR). To date a few NMR studies are dedicated to root exudates. Plant culture, exudates collection and sample preparation methods had thus to be adapted to the NMR approach. Here, pea seedlings were hydroponically grown. The obtained NMR fingerprints show that osmotic stress increases the quantity of the exudates but not their diversity. We therefore selected a protocol reducing the harvest time and using an ionic solvent and applied it to the analysis of faba bean exudates. NMR analysis of the metabolic profiles allowed to discriminate between pea and faba bean according to their exudate composition. This protocol is therefore very promising for studying the composition of root exudates from different plant species as well as their evolution in response to different environmental conditions or pathophysiological events.

Protein Backbone Alteration in Non-Hairpin β-Turns: Impacts on Tertiary Folded Structure and Folded Stability

The importance of β-turns to protein folding has motivated extensive efforts to stabilize the motif with non-canonical backbone connectivity. Prior work has focused almost exclusively on turns between strands in a β-sheet (i. e., hairpins). Turns in other structural contexts are also common in nature and have distinct conformational preferences; however, design principles for their mimicry remain poorly understood. Here, we report strategies that stabilize non-hairpin β-turns through systematic evaluation of the impacts of backbone alteration on the high-resolution folded structure and folded stability of a helix-loop-helix prototype protein. Several well-established hairpin turn mimetics are shown detrimental to folded stability and/or hydrophobic core packing, while less-explored modification schemes that reinforce alternate turn types lead to improved stability and more faithful structural mimicry. Collectively, these results have implications in control over protein folding through chemical modification as well as the design of protein mimetics.

Application of NMR spectroscopy for the detection and quantification of phthalic acid in fish muscles: The case of Atlantic Cod from Norwegian Sea

Plastic litter might contain phthalates that can be transferred to marine environment or can be introduced into the marine food chain. Phthalic acid is the final product of phthalate decomposition in marine organisms. Here we used NMR spectroscopy to determine and quantify phthalic acid and dimethyl phthalate in fish muscles. Spike-and-recovery experiments were carried out to confirm assignment of phthalates resonance signals in NMR spectra and to evaluate the method specificity, accuracy, and linearity. The LOQ and LOD of the rapid ¹H NMR experiment with a standard setting were respectively 23.0 and 8.0 mg of phthalic acid in kg of fish muscles. Phthalic acid was detected in 13 out of 113 Atlantic cod and none in farmed Atlantic salmon from Norwegian sea.

Quantitative analysis of protein dynamics using a deep learning technique combined with experimental cryo-EM density data and MD simulations

Protein functions associated with biological activity are precisely regulated by both tertiary structure and dynamic behavior. Thus, elucidating the high-resolution structures and quantitative information on in-solution dynamics is essential for understanding the molecular mechanisms. The main experimental approaches for determining tertiary structures include nuclear magnetic resonance (NMR), X-ray crystallography, and cryogenic electron microscopy (cryo-EM). Among these procedures, recent remarkable advances in the hardware and analytical techniques of cryo-EM have increasingly determined novel atomic structures of macromolecules, especially those with large molecular weights and complex assemblies. In addition to these experimental approaches, deep learning techniques, such as AlphaFold 2, accurately predict structures from amino acid sequences, accelerating structural biology research. Meanwhile, the quantitative analyses of the protein dynamics are conducted using experimental approaches, such as NMR and hydrogen-deuterium mass spectrometry, and computational approaches, such as molecular dynamics (MD) simulations. Although these procedures can quantitatively explore dynamic behavior at high resolution, the fundamental difficulties, such as signal crowding and high computational cost, greatly hinder their application to large and complex biological macromolecules. In recent years, machine learning techniques, especially deep learning techniques, have been actively applied to structural data to identify features that are difficult for humans to recognize from big data. Here, we review our approach to accurately estimate dynamic properties associated with local fluctuations from three-dimensional cryo-EM density data using a deep learning technique combined with MD simulations.

Metabolic Adaptations Correlated with Antibody Response after Immunization with Inactivated SARS-CoV-2 in Brazilian Subjects

The adsorbed vaccine SARS-CoV-2 (inactivated) produced by Sinovac (SV) was the first vaccine against COVID-19 to be used in Brazil. To understand the metabolic effects of SV in Brazilian subjects, NMR-based metabolomics was used, and the immune response was studied in Brazilian subjects. Forty adults without (group^-, n = 23) and with previous COVID-19 infection (group⁺, n = 17) were followed-up for 90 days postcompletion of the vaccine regimen. After 90 days, our results showed that subjects had increased levels of lipoproteins, lipids, and N-acetylation of glycoproteins (NAG) as well as decreased levels of amino acids, lactate, citrate, and 3-hydroxypropionate. NAG and threonine were the highest correlated metabolites with N and S proteins, and neutralizing Ab levels. This study sheds light on the immunometabolism associated with the use of SV in Brazilian subjects from Rio de Janeiro and identifies potential metabolic markers associated with the immune status.