BioMagResBank

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.

Differences in Exudates Between Strains of Chlorella sorokiniana Affect the Interaction with the Microalga Growth-Promoting Bacteria Azospirillum brasilense : Differences in Exudates Between Strains of Chlorella sorokiniana Affect the Interaction with the Microalga Growth-Promoting Bacteria Azospirillum brasilense

The microalga Chlorella sorokiniana and the microalgae growth-promoting bacteria (MGPB) Azospirillum brasilense have a mutualistic interaction that can begin within the first hours of co-incubation; however, the metabolites participating in this initial interaction are not yet identified. Nuclear magnetic resonance (NMR) was used in the present study to characterize the metabolites exuded by two strains of C. sorokiniana (UTEX 2714 and UTEX 2805) and A. brasilense Cd when grown together in an oligotrophic medium. Lactate and myo-inositol were identified as carbon metabolites exuded by the two strains of C. sorokiniana; however, only the UTEX 2714 strain exuded glycerol as the main carbon compound. In turn, A. brasilense exuded uracil when grown on the exudates of either microalga, and both microalga strains were able to utilize uracil as a nitrogen source. Interestingly, although the total carbohydrate content was higher in exudates from C. sorokiniana UTEX 2805 than from C. sorokiniana UTEX 2714, the growth of A. brasilense was greater in the exudates from the UTEX 2714 strain. These results highlight the fact that in the exuded carbon compounds differ between strains of the same species of microalgae and suggest that the type, rather than the quantity, of carbon source is more important for sustaining the growth of the partner bacteria.

Metabolomic Study of Aging in fa/fa Rats: Multiplatform Urine and Serum Analysis

Zucker fatty (fa/fa) rats represent a well-established and widely used model of genetic obesity. Because previous metabolomic studies have only been published for young fa/fa rats up to 20 weeks of age, which can be considered early maturity in male fa/fa rats, the aim of our work was to extend the metabolomic characterization to significantly older animals. Therefore, the urinary profiles of obese fa/fa rats and their lean controls were monitored using untargeted NMR metabolomics between 12 and 40 weeks of age. At the end of the experiment, the rats were also characterized by NMR and LC-MS serum analysis, which was supplemented by a targeted LC-MS analysis of serum bile acids and neurotransmitters. The urine analysis showed that most of the characteristic differences detected in young obese fa/fa rats persisted throughout the experiment, primarily through a decrease in microbial co-metabolite levels, the upregulation of the citrate cycle, and changes in nicotinamide metabolism compared with the age-related controls. The serum of 40-week-old obese rats showed a reduction in several bile acid conjugates and an increase in serotonin. Our study demonstrated that the fa/fa model of genetic obesity is stable up to 40 weeks of age and is therefore suitable for long-term experiments.

1H NMR-Based Metabolomics to Assess the Impact of Soil Type on the Chemical Composition of Nero d'Avola Red Wines

In this study, the soil effect on the micro-component composition of Nero d'Avola wines obtained from different locations was investigated through ¹H NMR-based metabolomics. Two different approaches were applied: the targeted (TA) and the non-targeted one (NTA). The former differentiated the wines by profiling (i.e., by identifying and quantifying) a number of different metabolites. The latter provided wine fingerprinting by processing the entire spectra with multivariate statistical analysis. NTA also allowed investigation of the hydrogen bond network inside wines via the analysis of ¹H NMR chemical shift dispersions. Results showed that the differences among wines were due not only to the concentrations of various analytes but also to the characteristics of the H-bond network where different solutes were involved. The H-bond network affects both gustatory and olfactory perceptions by modulating the way how solutes interact with the human sensorial receptors. Moreover, the aforementioned H-bond network is also related to the soil properties from which the grapes were taken. Therefore, the present study can be considered a good attempt to investigate terroir, i.e., the relationship between wine quality and soil characteristics.

Demographic, Health and Lifestyle Factors Associated with the Metabolome in Older Women

Demographic and clinical factors influence the metabolome. The discovery and validation of disease biomarkers are often challenged by potential confounding effects from such factors. To address this challenge, we investigated the magnitude of the correlation between serum and urine metabolites and demographic and clinical parameters in a well-characterized observational cohort of 444 post-menopausal women participating in the Women’s Health Initiative (WHI). Using LC-MS and lipidomics, we measured 157 aqueous metabolites and 756 lipid species across 13 lipid classes in serum, along with 195 metabolites detected by GC-MS and NMR in urine and evaluated their correlations with 29 potential disease risk factors, including demographic, dietary and lifestyle factors, and medication use. After controlling for multiple testing (FDR < 0.01), we found that log-transformed metabolites were mainly associated with age, BMI, alcohol intake, race, sample storage time (urine only), and dietary supplement use. Statistically significant correlations were in the absolute range of 0.2–0.6, with the majority falling below 0.4. Incorporation of important potential confounding factors in metabolite and disease association analyses may lead to improved statistical power as well as reduced false discovery rates in a variety of data analysis settings.

NMR structure of emfourin, a novel protein metalloprotease inhibitor: Insights into the mechanism of action

Emfourin (M4in) is a protein metalloprotease inhibitor recently discovered in the bacterium Serratia proteamaculans and the prototype of a new family of protein protease inhibitors with an unknown mechanism of action. Protealysin-like proteases (PLPs) of the thermolysin family are natural targets of emfourin-like inhibitors widespread in bacteria and known in archaea. The available data indicate the involvement of PLPs in interbacterial interaction as well as bacterial interaction with other organisms and likely in pathogenesis. Arguably, emfourin-like inhibitors participate in the regulation of bacterial pathogenesis by controlling PLP activity. Here, we determined the 3D structure of M4in using solution NMR spectroscopy. The obtained structure demonstrated no significant similarity to known protein structures. This structure was used to model the M4in-enzyme complex and the complex model was verified by small-angle X-ray scattering. Based on the model analysis, we propose a molecular mechanism for the inhibitor, which was confirmed by site-directed mutagenesis. We show that two spatially close flexible loop regions are critical for the inhibitor-protease interaction. One region includes aspartic acid forming a coordination bond with catalytic Zn2+ of the enzyme and the second region carries hydrophobic amino acids interacting with protease substrate binding sites. Such an active site structure corresponds to the noncanonical inhibition mechanism. This is the first demonstration of such a mechanism for protein inhibitors of thermolysin family metalloproteases, which puts forward M4in as a new basis for the development of antibacterial agents relying on selective inhibition of prominent factors of bacterial pathogenesis belonging to this family.

β-Catenin interacts with the TAZ1 and TAZ2 domains of CBP/p300 to activate gene transcription

The transcriptional co-regulator β-catenin is a critical member of the canonical Wnt signaling pathway, which plays an important role in regulating cell fate. Deregulation of the Wnt/β-catenin pathway is characteristic in the development of major types of cancer, where accumulation of β-catenin promotes cancer cell proliferation and renewal. β-catenin gene expression is facilitated through recruitment of co-activators such as histone acetyltransferases CBP/p300; however, the mechanism of their interaction is not fully understood. Here we investigate the interaction between the C-terminal transactivation domain of β-catenin and CBP/p300. Using a combination of pulldown assays, isothermal titration calorimetry, and nuclear resonance spectroscopy we determine the disordered C-terminal region of β-catenin binds promiscuously to the TAZ1 and TAZ2 domains of CBP/p300. We then map the interaction site of the C-terminal β-catenin transactivation domain onto TAZ1 and TAZ2 using chemical-shift perturbation studies. Luciferase-based gene reporter assays indicate Asp750-Leu781 is critical to β-catenin gene activation, and mutagenesis revealed that acidic and hydrophobic residues within this region are necessary to maintain TAZ1 binding. These results outline a mechanism of Wnt/β-catenin gene regulation that underlies cell development and provides a framework to develop methods to block β-catenin dependent signaling.

Robust automated backbone triple resonance NMR assignments of proteins using Bayesian-based simulated annealing

Assignment of resonances of nuclear magnetic resonance (NMR) spectra to specific atoms within a protein remains a labor-intensive and challenging task. Automation of the assignment process often remains a bottleneck in the exploitation of solution NMR spectroscopy for the study of protein structure-dynamics-function relationships. We present an approach to the assignment of backbone triple resonance spectra of proteins. A Bayesian statistical analysis of predicted and observed chemical shifts is used in conjunction with inter-spin connectivities provided by triple resonance spectroscopy to calculate a pseudo-energy potential that drives a simulated annealing search for the most optimal set of resonance assignments. Termed Bayesian Assisted Assignments by Simulated Annealing (BARASA), a C++ program implementation is tested against systems ranging in size to over 450 amino acids including examples of intrinsically disordered proteins. BARASA is fast, robust, accommodates incomplete and incorrect information, and outperforms current algorithms - especially in cases of sparse data and is sufficiently fast to allow for real-time evaluation during data acquisition.

Phosphorylation Modification Force Field FB18CMAP Improving Conformation Sampling of Phosphoproteins

Phosphorylation of proteins plays an important regulatory role at almost all levels of cellular organization. Molecular dynamics (MD) simulation is a promising tool to reveal the mechanism of how phosphorylation regulates many key biological processes at the atomistic level. MD simulation accuracy depends on force field precision, while the current force fields for phospho-amino acids have resulted in notable inconsistency with experimental data. Here, a new force field parameter (named FB18CMAP) is generated by fitting against quantum mechanics (QM) energy in aqueous solution with φ/ψ dihedral potential-energy surfaces optimized using CMAP parameters. MD simulations of phosphorylated dipeptides, intrinsically disordered proteins (IDPs), and ordered (folded) proteins show that FB18CMAP can mimic NMR observables and structural characteristics of phosphorylated dipeptides and proteins more accurately than the FB18 force field. These findings suggest that FB18CMAP performs well in both the simulation of ordered and disordered states of phosphorylated proteins.

The Assembly of Bacteria Living in Natural Environments Shapes Neuronal Integrity and Behavioral Outputs in Caenorhabditis elegans

Bacterivore nematodes are the most abundant animals in the biosphere, largely contributing to global biogeochemistry. Thus, the effects of environmental microbes on the nematodes' life-history traits are likely to contribute to the general health of the biosphere. Caenorhabditis elegans is an excellent model to study the behavioral and physiological outputs of microbial diets. However, the effects of complex natural bacterial assemblies have only recently been reported, as most studies have been carried out with monoxenic cultures of laboratory-reared bacteria. Here, we quantified the physiological, phenotypic, and behavioral traits of C. elegans feeding on two bacteria that were coisolated with wild nematodes from a soil sample. These bacteria were identified as a putative novel species of Stenotrophomonas named Stenotrophomonas sp. strain Iso1 and a strain of Bacillus pumilus designated Iso2. The distinctive behaviors and developmental patterns observed in animals fed with individual isolates changed when bacteria were mixed. We studied in more depth the degeneration rate of the touch circuit of C. elegans and show that B. pumilus alone is protective, while the mix with Stenotrophomonas sp. is degenerative. The analysis of the metabolite contents of each isolate and their combination identified NAD⁺ as being potentially neuroprotective. In vivo supplementation shows that NAD⁺ restores neuroprotection to the mixes and also to individual nonprotective bacteria. Our results highlight the distinctive physiological effects of bacteria resembling native diets in a multicomponent scenario rather than using single isolates on nematodes. IMPORTANCE Do behavioral choices depend on animals' microbiota? To answer this question, we studied how different bacterial assemblies impact the life-history traits of the bacterivore nematode C. elegans using isolated bacteria found in association with wild nematodes in Chilean soil. We identified the first isolate, Iso1, as a novel species of Stenotrophomonas and isolate Iso2 as Bacillus pumilus. We find that worm traits such as food choice, pharyngeal pumping, and neuroprotection, among others, are dependent on the biota composition. For example, the neurodegeneration of the touch circuit needed to sense and escape from predators in the wild decreases when nematodes are fed on B. pumilus, while its coculture with Stenotrophomonas sp. eliminates neuroprotection. Using metabolomics analysis, we identify metabolites such as NAD⁺, present in B. pumilus yet lost in the mix, as being neuroprotective and validated their protective effects using in vivo experiments.

Accurate Classification and Prediction of Acute Myocardial Infarction through an ARMD Procedure

The risk stratification of acute myocardial infarction (AMI) patients is of prime importance for clinical management and prognosis assessment. Thus, we propose an ensemble machine learning analysis procedure named ADASYN-RFECV-MDA-DNN (ARMD) to address sample-unbalanced problems and enable stratification and prediction of AMI outcomes. The ARMD analysis procedure was applied to the NMR data of sera from 534 AMI-related subjects in four categories with an extremely imbalanced sample proportion. Firstly, the adaptive synthetic sampling (ADASYN) algorithm was used to address the issue of the original sample imbalance. Secondly, the recursive feature elimination with cross-validation (RFECV) processing and random forest mean decrease accuracy (RF-MDA) algorithm was performed to identify the differential metabolites corresponding to each AMI outcome. Finally, the deep neural network (DNN) was employed to classify and predict AMI events, and its performance was evaluated by comparing the four traditional machine learning methods. Compared with the other four machine learning models, DNN presented consistent superiority in almost all of the model parameters including precision, f1-score, sensitivity, specificity, area under the receiver operating characteristic curve (AUC), and classification accuracy, highlighting the potential of deep learning in classification and stratification of clinical diseases. The ARMD analysis procedure was a practical analysis tool for supervised classification and regression modeling of clinical diseases.

Direct deduction of chemical class from NMR spectra

This paper presents a proof-of-concept method for classifying chemical compounds directly from NMR data without performing structure elucidation. This can help to reduce the time in finding good structure candidates, as in most cases matching must be done by a human engineer, or at the very least a process for matching must be meaningfully interpreted by one. The method identified as suitable for classification is a convolutional neural network (CNN). Other methods, including clustering and image registration, have not been found to be suitable for the task in a comparative analysis. The result shows that deep learning can offer solutions to spectral interpretation problems.

PDBms-An online tool for PDB file splitting and interactive molecular visualization

The rapid growth of biological databases has resulted in the vast development of many state-of-the-art molecular analysis tools for accurate disease diagnosis and drug discovery. Protein Data Bank (PDB) is a leading molecular database consisting of three-dimensional (3D) experimental structures of macromolecules and small molecules. The most significant role of PDB in Bioinformatics includes molecular modelling and computer-aided drug design (CADD). PDBms is a web tool for splitting PDB file and interactive visualization of molecules. It parses coordinate section records in the PDB file and categorizes them into a group of molecules. Moreover, it supports 3D graphic visualization in the various model for polymers and target-ligand interactions of complex structures. The web interface of PDBms is designed using NGL Viewer/WebGL JavaScript package and programming languages such as PHP, HTML, CSS, JavaScript, AJAX, and jQuery. PDBms is freely accessible at https://www.biogem.org/tool/pdbms/ .

Redox Regulation and Metabolic Dependency of Zika Virus Replication: Inhibition by Nrf2-Antioxidant Response and NAD(H) Antimetabolites

Viral infections alter host cell metabolism and homeostasis; however, the mechanisms that regulate these processes have only begun to be elucidated. We report here that Zika virus (ZIKV) infection activates the antioxidant nuclear factor erythroid 2-related factor 2 (Nrf2), which precedes oxidative stress. Downregulation of Nrf2 or inhibition of glutathione (GSH) synthesis resulted in significantly increased viral replication. Interestingly, 6-amino-nicotinamide (6-AN), a nicotinamide analog commonly used as an inhibitor of the pentose phosphate pathway (PPP), decreased viral replication by over 1,000-fold. This inhibition was neither recapitulated by the knockdown of PPP enzymes, glucose 6-phosphate dehydrogenase (G6PD), or 6-phosphogluconate dehydrogenase (6PGD), nor prevented by supplementation with ribose 5-phosphate. Instead, our metabolomics and metabolic phenotype studies support a mechanism in which 6-AN depletes cells of NAD(H) and impairs NAD(H)-dependent glycolytic steps resulting in inhibition of viral replication. The inhibitory effect of 6-AN was rescued with precursors of the salvage pathway but not with those of other NAD+ biosynthesis pathways. Inhibition of glycolysis reduced viral protein levels, which were recovered transiently. This transient recovery in viral protein synthesis was prevented when oxidative metabolism was inhibited by blockage of the mitochondrial pyruvate carrier, fatty acid oxidation, or glutaminolysis, demonstrating a compensatory role of mitochondrial metabolism in ZIKV replication. These results establish an antagonistic role for the host cell Nrf2/GSH/NADPH-dependent antioxidant response against ZIKV and demonstrate the dependency of ZIKV replication on NAD(H). Importantly, our work suggests the potential use of NAD(H) antimetabolite therapy against the viral infection. IMPORTANCE Zika virus (ZIKV) is a major public health concern of international proportions. While the incidence of ZIKV infections has declined substantially in recent years, the potential for the reemergence or reintroduction remains high. Although viral infection alters host cell metabolism and homeostasis to promote its replication, deciphering the mechanism(s) involved in these processes is important for identifying therapeutic targets. The present work reveals the complexities of host cell redox regulation and metabolic dependency of ZIKV replication. An antagonistic effect of the Nrf2/GSH/NADP(H)-dependent antioxidant response against ZIKV infection and an essential role of NAD(H) metabolism and glycolysis for viral replication are established for the first time. These findings highlight the potential use of NAD(H) antimetabolites to counter ZIKV infection and pathogenesis.

Machine Learning in Automated Monitoring of Metabolic Changes Accompanying the Differentiation of Adipose-Tissue-Derived Human Mesenchymal Stem Cells Employing 1H-1H TOCSY NMR

The ability to monitor the dynamics of stem cell differentiation is a major goal for understanding biochemical evolution pathways. Automating the process of metabolic profiling using 2D NMR helps us to understand the various differentiation behaviors of stem cells, and therefore sheds light on the cellular pathways of development, and enhances our understanding of best practices for in vitro differentiation to guide cellular therapies. In this work, the dynamic evolution of adipose-tissue-derived human Mesenchymal stem cells (AT-derived hMSCs) after fourteen days of cultivation, adipocyte and osteocyte differentiation, was inspected based on 1H-1H TOCSY using machine learning. Multi-class classification in addition to the novelty detection of metabolites was established based on a control hMSC sample after four days' cultivation and we successively detected the changes of metabolites in differentiated MSCs following a set of 1H-1H TOCSY experiments. The classifiers Kernel Null Foley-Sammon Transform and Kernel Density Estimation achieved a total classification error between 0% and 3.6% and false positive and false negative rates of 0%. This approach was successfully able to automatically reveal metabolic changes that accompanied MSC cellular evolution starting from their undifferentiated status to their prolonged cultivation and differentiation into adipocytes and osteocytes using machine learning supporting the research in the field of metabolic pathways of stem cell differentiation.

SPA-STOCSY: An Automated Tool for Identification of Annotated and Non-Annotated Metabolites in High-Throughput NMR Spectra

Nuclear Magnetic Resonance (NMR) spectroscopy is widely used to analyze metabolites in biological samples, but the analysis can be cumbersome and inaccurate. Here, we present a powerful automated tool, SPA-STOCSY (Spatial Clustering Algorithm - Statistical Total Correlation Spectroscopy), which overcomes the challenges by identifying metabolites in each sample with high accuracy. As a data-driven method, SPA-STOCSY estimates all parameters from the input dataset, first investigating the covariance pattern and then calculating the optimal threshold with which to cluster data points belonging to the same structural unit, i.e. metabolite. The generated clusters are then automatically linked to a compound library to identify candidates. To assess SPA-STOCSY’s efficiency and accuracy, we applied it to synthesized and real NMR data obtained from Drosophila melanogaster brains and human embryonic stem cells. In the synthesized spectra, SPA outperforms Statistical Recoupling of Variables, 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 real spectra, SPA-STOCSY performs comparably to operator-based Chenomx analysis but avoids operator bias and performs the analyses in less than seven minutes of total computation time. Overall, SPA-STOCSY is a fast, accurate, and unbiased tool for untargeted analysis of metabolites in the NMR spectra. As such, it might accelerate the utilization of NMR for scientific discoveries, medical diagnostics, and patient-specific decision making.

Analysis of Hazelnuts (Corylus avellana L.) Stored for Extended Periods by 1H NMR Spectroscopy Monitoring Storage-Induced Changes in the Polar and Nonpolar Metabolome

Storage is a critical step in the post-harvest processing of hazelnuts, as it can lead to mold, rancidity, and off-flavor. However, there is a lack of analytical methods to detect improper or extended storage. To comprehensively investigate the effects of hazelnut storage, samples were stored under five different conditions for up to 18 months. Subsequently, the polar and nonpolar metabolome were analyzed by ¹H NMR spectroscopy and chemometric approaches for classification as well as variable selection. Increases in hexanoic, octanoic, and nonanoic acid, all products of lipid oxidation and responsible for quality defects, were found across all conditions. Furthermore, the concentration of free long-chain fatty acids increased in samples stored at high temperatures. Harsh short-term storage resulted in an increase in fumaric and lactic acid, glucose, fructose, and choline and a decrease in acetic acid.

13C NMR metabolomics: J-resolved STOCSY meets INADEQUATE

Robust annotation of metabolites is a challenging task in metabolomics. Among available applications, ¹³C NMR experiment INADEQUATE determines direct ¹³C-¹³C connectivity unambiguously, offering indispensable information on molecular structure. Despite its great utility, it is not always practical to collect INADEQUATE data on every sample in a large metabolomics study because of its relatively long experiment time. Here, we propose an alternative approach that maintains the quality of information but saves experiment time. In this approach, individual samples in a study are first screened by ¹³C homonuclear J-resolved experiment (JRES). Next, JRES data are processed by statistical total correlation spectroscopy (STOCSY) to extract peaks that behave similarly among samples. Finally, INADEQUATE is collected on one internal pooled sample to select STOCSY peaks that originate from the same compound. We tested this concept using the ¹³C-labeled endometabolome of a model marine diatom strain incubated under various settings, intending to cover a range of metabolites produced under different external conditions. This scheme was able to extract known diatom metabolites proline, 2,3-dihydroxypropane-1-sulfonate (DHPS), β-1,3-glucan, choline, and glutamate. This pipeline also detected unknown compounds with structural information, which is valuable in metabolomics where a priori knowledge of metabolites is not always available. The ability of this scheme was seen even in sugar regions, which are usually challenging in ¹H NMR due to severe peak overlap. JRES and INADEQUATE were highly complementary; INADEQUATE provided directly-bonded ¹³C networks, whereas JRES linked INADEQUATE networks within the same compound but broken by nitrogen or sulfur atoms, highlighting the advantage of this integrated approach.

Byproducts of Globe Artichoke and Cauliflower Production as a New Source of Bioactive Compounds in the Green Economy Perspective: An NMR Study

The recovery of bioactive compounds from crop byproducts leads to a new perspective way of waste reutilization as a part of the circular economy. The present study aimed at an exhaustive metabolite profile characterization of globe artichoke and cauliflower byproducts (leaves, stalks, and florets for cauliflower only) as a prerequisite for their valorization and future implementations. The metabolite profile of aqueous and organic extracts of byproducts was analyzed using the NMR-based metabolomics approach. Free amino acids, organic acids, sugars, polyols, polyphenols, amines, glucosinolates, fatty acids, phospho- and galactolipids, sterols, and sesquiterpene lactones were identified and quantified. In particular, globe artichoke byproducts are a source of health-beneficial compounds including chiro-inositol (up to 10.1 mg/g), scyllo-inositol (up to 1.8 mg/g), sesquiterpene lactones (cynaropicrin, grosheimin, dehydrocynaropicrin, up to 45.5 mg/g in total), inulins, and chlorogenic acid (up to 7.5 mg/g), whereas cauliflower byproducts enclose bioactive sulfur-containing compounds S-methyl-L-cysteine S-oxide (methiin, up to 20.7 mg/g) and glucosinolates. A variable content of all metabolites was observed depending on the crop type (globe artichoke vs. cauliflower) and the plant part (leaves vs. stalks). The results here reported can be potentially used in different ways, including the formulation of new plant biostimulants and food supplements.

NMR Studies of Aromatic Ring Flips to Probe Conformational Fluctuations in Proteins

Aromatic residues form a significant part of the protein core, where they make tight interactions with multiple surrounding side chains. Despite the dense packing of internal side chains, the aromatic rings of phenylalanine and tyrosine residues undergo 180° rotations, or flips, which are mediated by transient and large-scale "breathing" motions that generate sufficient void volume around the aromatic ring. Forty years after the seminal work by Wagner and Wüthrich, NMR studies of aromatic ring flips are now undergoing a renaissance as a powerful means of probing fundamental dynamic properties of proteins. Recent developments of improved NMR methods and isotope labeling schemes have enabled a number of advances in addressing the mechanisms and energetics of aromatic ring flips. The nature of the transition states associated with ring flips can be described by thermodynamic activation parameters, including the activation enthalpy, activation entropy, activation volume, and also the isothermal volume compressibility of activation. Consequently, it is of great interest to study how ring flip rate constants and activation parameters might vary with protein structure and external conditions like temperature and pressure. The field is beginning to gather such data for aromatic residues in a variety of environments, ranging from surface exposed to buried. In the future, the combination of solution and solid-state NMR spectroscopy together with molecular dynamics simulations and other computational approaches is likely to provide detailed information about the coupled dynamics of aromatic rings and neighboring residues. In this Perspective, we highlight recent developments and provide an outlook toward the future.

Characterisation of Elevenin-Vc1 from the Venom of Conus victoriae: A Structural Analogue of α-Conotoxins

Elevenins are peptides found in a range of organisms, including arthropods, annelids, nematodes, and molluscs. They consist of 17 to 19 amino acid residues with a single conserved disulfide bond. The subject of this study, elevenin-Vc1, was first identified in the venom of the cone snail Conus victoriae (Gen. Comp. Endocrinol. 2017, 244, 11-18). Although numerous elevenin sequences have been reported, their physiological function is unclear, and no structural information is available. Upon intracranial injection in mice, elevenin-Vc1 induced hyperactivity at doses of 5 or 10 nmol. The structure of elevenin-Vc1, determined using nuclear magnetic resonance spectroscopy, consists of a short helix and a bend region stabilised by the single disulfide bond. The elevenin-Vc1 structural fold is similar to that of α-conotoxins such as α-RgIA and α-ImI, which are also found in the venoms of cone snails and are antagonists at specific subtypes of nicotinic acetylcholine receptors (nAChRs). In an attempt to mimic the functional motif, Asp-Pro-Arg, of α-RgIA and α-ImI, we synthesised an analogue, designated elevenin-Vc1-DPR. However, neither elevenin-Vc1 nor the analogue was active at six different human nAChR subtypes (α1β1εδ, α3β2, α3β4, α4β2, α7, and α9α10) at 1 µM concentrations.

Metabolic rewiring in MYC-driven medulloblastoma by BET-bromodomain inhibition

Medulloblastoma (MB) is the most common malignant brain tumour in children. High-risk MB patients harbouring MYC amplification or overexpression exhibit a very poor prognosis. Aberrant activation of MYC markedly reprograms cell metabolism to sustain tumorigenesis, yet how metabolism is dysregulated in MYC-driven MB is not well understood. Growing evidence unveiled the potential of BET-bromodomain inhibitors (BETis) as next generation agents for treating MYC-driven MB, but whether and how BETis may affect tumour cell metabolism to exert their anticancer activities remains unknown. In this study, we explore the metabolic features characterising MYC-driven MB and examine how these are altered by BET-bromodomain inhibition. To this end, we employed an NMR-based metabolomics approach applied to the MYC-driven MB D283 and D458 cell lines before and after the treatment with the BETi OTX-015. We found that OTX-015 triggers a metabolic shift in both cell lines resulting in increased levels of myo-inositol, glycerophosphocholine, UDP-N-acetylglucosamine, glycine, serine, pantothenate and phosphocholine. Moreover, we show that OTX-015 alters ascorbate and aldarate metabolism, inositol phosphate metabolism, phosphatidylinositol signalling system, glycerophospholipid metabolism, ether lipid metabolism, aminoacyl-tRNA biosynthesis, and glycine, serine and threonine metabolism pathways in both cell lines. These insights provide a metabolic characterisation of MYC-driven childhood MB cell lines, which could pave the way for the discovery of novel druggable pathways. Importantly, these findings will also contribute to understand the downstream effects of BETis on MYC-driven MB, potentially aiding the development of new therapeutic strategies to combat medulloblastoma.

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) 15 N- 1 H 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.

Highlights

AF2 models assessed against NMR data for 9 monomeric proteins not used in training.AF2 models fit NMR data almost as well as the experimentally-determined structures. RPF-DP, PSVS , and PDBStat software provide structure quality and RDC assessment. RPF-DP analysis using AF2 models suggests multiple conformational states.

The Footprint of Type 1 Diabetes on Red Blood Cells: A Metabolomic and Lipidomic Study

The prevalence of diabetes type 1 (T1D) in the world populations is continuously growing. Although treatment methods are improving, the diagnostic is still symptom-based and sometimes far after onset of the disease. In this context, the aim of the study was the search of new biomarkers of the disease in red blood cells (RBCs), until now unexplored. The metabolomic and the lipidomic profile of RBCs from T1D patients and matched healthy controls was determined by NMR spectroscopy, and different multivariate discrimination models were built to select the metabolites and lipids that change most significantly. Relevant metabolites were further confirmed by univariate statistical analysis. Robust separation in the metabolomic and lipidomic profiles of RBCs from patients and controls was confirmed by orthogonal projection on latent structure discriminant analysis (OPLS-DA), random forest analysis, and significance analysis of metabolites (SAM). The main changes were detected in the levels of amino acids, organic acids, creatine and phosphocreatine, lipid change length, and choline derivatives, demonstrating changes in glycolysis, BCAA metabolism, and phospholipid metabolism. Our study proves that robust differences exist in the metabolic and lipidomic profile of RBCs from T1D patients, in comparison with matched healthy individuals. Some changes were similar to alterations found already in RBCs of T2D patients, but others seemed to be specific for type 1 diabetes. Thus, many of the metabolic differences found could be biomarker candidates for an earlier diagnosis or monitoring of patients with T1D.

RCSB Protein Data Bank (RCSB.org): delivery of experimentally-determined PDB structures alongside one million computed structure models of proteins from artificial intelligence/machine learning

The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB), founding member of the Worldwide Protein Data Bank (wwPDB), is the US data center for the open-access PDB archive. As wwPDB-designated Archive Keeper, RCSB PDB is also responsible for PDB data security. Annually, RCSB PDB serves >10 000 depositors of three-dimensional (3D) biostructures working on all permanently inhabited continents. RCSB PDB delivers data from its research-focused RCSB.org web portal to many millions of PDB data consumers based in virtually every United Nations-recognized country, territory, etc. This Database Issue contribution describes upgrades to the research-focused RCSB.org web portal that created a one-stop-shop for open access to ∼200 000 experimentally-determined PDB structures of biological macromolecules alongside >1 000 000 incorporated Computed Structure Models (CSMs) predicted using artificial intelligence/machine learning methods. RCSB.org is a 'living data resource.' Every PDB structure and CSM is integrated weekly with related functional annotations from external biodata resources, providing up-to-date information for the entire corpus of 3D biostructure data freely available from RCSB.org with no usage limitations. Within RCSB.org, PDB structures and the CSMs are clearly identified as to their provenance and reliability. Both are fully searchable, and can be analyzed and visualized using the full complement of RCSB.org web portal capabilities.

A direct nuclear magnetic resonance method to investigate lysine acetylation of intrinsically disordered proteins

Intrinsically disordered proteins are frequent targets for functional regulation through post-translational modification due to their high accessibility to modifying enzymes and the strong influence of changes in primary structure on their chemical properties. While lysine Nε-acetylation was first observed as a common modification of histone tails, proteomic data suggest that lysine acetylation is ubiquitous among both nuclear and cytosolic proteins. However, compared with our biophysical understanding of the other common post-translational modifications, mechanistic studies to document how lysine Nε-acetyl marks are placed, utilized to transduce signals, and eliminated when signals need to be turned off, have not kept pace with proteomic discoveries. Herein we report a nuclear magnetic resonance method to monitor Nε-lysine acetylation through enzymatic installation of a13C-acetyl probe on a protein substrate, followed by detection through 13C direct-detect spectroscopy. We demonstrate the ease and utility of this method using histone H3 tail acetylation as a model. The clearest advantage to this method is that it requires no exogenous tags that would otherwise add steric bulk, change the chemical properties of the modified lysine, or generally interfere with downstream biochemical processes. The non-perturbing nature of this tagging method is beneficial for application in any system where changes to local structure and chemical properties beyond those imparted by lysine modification are unacceptable, including intrinsically disordered proteins, bromodomain containing protein complexes, and lysine deacetylase enzyme assays.

Structure of the Sec14 domain of Kalirin reveals a distinct class of lipid-binding module in RhoGEFs

Gated entry of lipophilic ligands into the enclosed hydrophobic pocket in stand-alone Sec14 domain proteins often links lipid metabolism to membrane trafficking. Similar domains occur in multidomain mammalian proteins that activate small GTPases and regulate actin dynamics. The neuronal RhoGEF Kalirin, a central regulator of cytoskeletal dynamics, contains a Sec14 domain (Kal^bSec14) followed by multiple spectrin-like repeats and catalytic domains. Previous studies demonstrated that Kalirin lacking its Sec14 domain fails to maintain cell morphology or dendritic spine length, yet whether and how Kal^bSec14 interacts with lipids remain unknown. Here, we report the structural and biochemical characterization of Kal^bSec14. Kal^bSec14 adopts a closed conformation, sealing off the canonical ligand entry site, and instead employs a surface groove to bind a limited set of lysophospholipids. The low-affinity interactions of Kal^bSec14 with lysolipids are expected to serve as a general model for the regulation of Rho signaling by other Sec14-containing Rho activators.

Deazaflavin cofactor boosts earthworms Henlea bioluminescence

The bioluminescence of Siberian earthworms Henlea sp. was found to be enhanced by two low molecular weight activators, termed ActH and ActS, found in the hot extracts. The fluorescence emission maximum of the activators matches the bioluminescence spectrum that peaks at 464 nm. We purified 4.3 and 8.8 micrograms of ActH and ActS from 200 worms and explored them using orbitrap HRMS with deep fragmentation and 1D/2D NMR equipped with cryoprobes. Their chemical structures were ascertained using chemical shift prediction services, structure elucidation software and database searches. ActH was identified as the riboflavin analoge archaeal cofactor F0, namely 7,8-didemethyl-8-hydroxy-5-deazariboflavin. ActS is a novel compound, namely ActH sulfated at the 3' ribityl hydroxyl. We designed and implemented a new four step synthesis strategy forActH that outperformed previous synthetic approaches. The synthetic ActH was identical to the natural one and activated Henlea sp. bioluminescence. The bioluminescence enhancement factor X was measured at different ActH concentrations and the Michaelis constant K_m = 0.22 ± 0.01 μM was obtained by nonlinear regression. At an excess of synthetic ActH, the factor X was saturated at X_max = 33.3 ± 0.5, thus opening an avenue to further characterisation of the Henlea sp. bioluminescence system. ActH did not produce bioluminescence without the luciferin with an as yet unknown chemical structure. We propose that ActH and the novel sulfated deazariboflavin ActS either emit the light of the Henlea sp. bioluminescence and/or accept hydride(s) donor upon luciferin oxidation.

Histone H3 core domain in chromatin with different DNA linker lengths studied by 1H-Detected solid-state NMR spectroscopy

Chromatin, a dynamic protein-DNA complex that regulates eukaryotic genome accessibility and essential functions, is composed of nucleosomes connected by linker DNA with each nucleosome consisting of DNA wrapped around an octamer of histones H2A, H2B, H3 and H4. Magic angle spinning solid-state nuclear magnetic resonance (NMR) spectroscopy can yield unique insights into histone structure and dynamics in condensed nucleosomes and nucleosome arrays representative of chromatin at physiological concentrations. Recently we used J-coupling-based solid-state NMR methods to investigate with residue-specific resolution the conformational dynamics of histone H3 N-terminal tails in 16-mer nucleosome arrays containing 15, 30 or 60 bp DNA linkers. Here, we probe the H3 core domain in the 16-mer arrays as a function of DNA linker length via dipolar coupling-based 1H-detected solid-state NMR techniques. Specifically, we established nearly complete assignments of backbone chemical shifts for H3 core residues in arrays with 15-60 bp DNA linkers reconstituted with 2H,13C,15N-labeled H3. Overall, these chemical shifts were similar irrespective of the DNA linker length indicating no major changes in H3 core conformation. Notably, however, multiple residues at the H3-nucleosomal DNA interface in arrays with 15 bp DNA linkers exhibited relatively pronounced differences in chemical shifts and line broadening compared to arrays with 30 and 60 bp linkers. These findings are consistent with increased heterogeneity in nucleosome packing and structural strain within arrays containing short DNA linkers that likely leads to side-chains of these interfacial residues experiencing alternate conformations or shifts in their rotamer populations relative to arrays with the longer DNA linkers.

Unveiling Chemical Interactions Between Plants and Fungi Using Metabolomics Approaches

Metabolomics has been extensively used in clinical studies in the search for new biomarkers of human diseases. However, this approach has also been highlighted in agriculture and biological sciences, once metabolomics studies have been assisting researchers to deduce new chemical mechanisms involved in biological interactions that occur between microorganisms and plants. In this sense, the knowledge of the biological role of each metabolite (virulence factors, signaling compounds, antimicrobial metabolites, among others) and the affected biochemical pathways during the interaction contribute to a better understand of different ecological relationships established in nature. The current chapter addresses five different applications of the metabolomics approach in fungal-plant interactions research: (1) Discovery of biomarkers in pathogen-host interactions, (2) plant diseases diagnosis, (3) chemotaxonomy, (4) plant defense, and (5) plant resistance; using mass spectrometry and/or nuclear magnetic resonance spectroscopy, which are the techniques most used in metabolomics.

Using NMR Titration Experiments to Study E. coli FAS-II- and AcpP-Mediated Protein-Protein Interactions

Acyl carrier proteins (ACPs) are central to many primary and secondary metabolic pathways. In E. coli fatty acid biosynthesis (FAB), the central ACP, AcpP, transports intermediates to a suite of partner proteins (PP) for iterative modification and elongation. The regulatory protein-protein interactions that occur between AcpP and the PP in FAB are poorly understood due to the dynamic and transient nature of these interactions. Solution-state NMR spectroscopy can reveal information at the atomic level through experiments such as the 2D heteronuclear single quantum coherence (HSQC). The following protocol describes NMR HSQC titration experiments that can elucidate biomolecular recognition events.

Discovering New Natural Products Using Metabolomics-Based Approaches

The incessant search for new natural molecules with biological activities has forced researchers in the field of chemistry of natural products to seek different approaches for their prospection studies. In particular, researchers around the world are turning to approaches in metabolomics to avoid high rates of re-isolation of certain compounds, something recurrent in this branch of science. Thanks to the development of new technologies in the analytical instrumentation of spectroscopic and spectrometric techniques, as well as the advance in the computational processing modes of the results, metabolomics has been gaining more and more space in studies that involve the prospection of natural products. Thus, this chapter summarizes the precepts and good practices in the metabolomics of microbial natural products using mass spectrometry and nuclear magnetic resonance spectroscopy, and also summarizes several examples where this approach has been applied in the discovery of bioactive molecules.

Modeling structural interconversion in Alzheimers' amyloid beta peptide with classical and intrinsically disordered protein force fields

A comprehensive understanding of the aggregation mechanism in amyloid beta 42 (Aβ42) peptide is imperative for developing therapeutic drugs to prevent or treat Alzheimer's disease. Because of the high flexibility and lack of native tertiary structures of Aβ42, molecular dynamics (MD) simulations may help elucidate the peptide's dynamics with atomic details and collectively improve ensembles not seen in experiments. We applied microsecond-timescale MD simulations to investigate the dynamics and conformational changes of Aβ42 by using a newly developed Amber force field (ff14IDPSFF). We compared the ff14IDPSFF and the regular ff14SB force field by examining the conformational changes of two distinct Aβ42 monomers in explicit solvent. Conformational ensembles obtained by simulations depend on the force field and initial structure, Aβ42^α-helix or Aβ42^β-strand. The ff14IDPSFF sampled a high ratio of disordered structures and diverse β-strand secondary structures; in contrast, ff14SB favored helicity during the Aβ42^α-helix simulations. The conformations obtained from Aβ42^β-strand simulations maintained a balanced content in the disordered and helical structures when simulated by ff14SB, but the conformers clearly favored disordered and β-sheet structures simulated by ff14IDPSFF. The results obtained with ff14IDPSFF qualitatively reproduced the NMR chemical shifts well. In-depth peptide and cluster analysis revealed some characteristic features that may be linked to early onset of the fibril-like structure. The C-terminal region (mainly M35-V40) featured in-registered anti-parallel β-strand (β-hairpin) conformations with tested systems. Our work should expand the knowledge of force field and structure dependency in MD simulations and reveals the underlying structural mechanism-function relationship in Aβ42 peptides. Communicated by Ramaswamy H. Sarma.

Grape Berry Responses to Sequential Flooding and Heatwave Events: A Physiological, Transcriptional, and Metabolic Overview

Grapevine cultivation, such as the whole horticulture, is currently challenged by several factors, among which the extreme weather events occurring under the climate change scenario are the most relevant. Within this context, the present study aims at characterizing at the berry level the physiological response of Vitis vinifera cv. Sauvignon Blanc to sequential stresses simulated under a semi-controlled environment: flooding at bud-break followed by multiple summer stress (drought plus heatwave) occurring at pre-vèraison. Transcriptomic and metabolomic assessments were performed through RNASeq and NMR, respectively. A comprehensive hormone profiling was also carried out. Results pointed out a different response to the heatwave in the two situations. Flooding caused a developmental advance, determining a different physiological background in the berry, thus affecting its response to the summer stress at both transcriptional levels, with the upregulation of genes involved in oxidative stress responses, and metabolic level, with the increase in osmoprotectants, such as proline and other amino acids. In conclusion, sequential stress, including a flooding event at bud-break followed by a summer heatwave, may impact phenological development and berry ripening, with possible consequences on berry and wine quality. A berry physiological model is presented that may support the development of sustainable vineyard management solutions to improve the water use efficiency and adaptation capacity of actual viticultural systems to future scenarios.

Specific Lipid Studies in Complex Membranes by Solid-State NMR Spectroscopy

Specific interactions with phospholipids are often critical for the function of proteins or drugs, but studying these interactions at high resolution remains difficult, especially in complex membranes that mimic biological conditions. In principle, molecular interactions with phospholipids could be directly probed by solid-state NMR (ssNMR). However, due to the challenge to detect specific lipids in mixed liposomes and limited spectral sensitivity, ssNMR studies of specific lipids in complex membranes are scarce. Here, by using purified biological 13 C,15 N-labeled phospholipids, we show that we can selectively detect traces of specific lipids in complex membranes. In combination with 1 H-detected ssNMR, we show that our approach provides unprecedented high-resolution insights into the mechanisms of drugs that target specific lipids. This broadly applicable approach opens new opportunities for the molecular characterization of specific lipid interactions with proteins or drugs in complex fluid membranes.

Intrinsically disordered regions that drive phase separation form a robustly distinct protein class

Protein phase separation is thought to be a primary driving force for the formation of membrane-less organelles, which control a wide range of biological functions from stress response to ribosome biogenesis. Among phase-separating (PS) proteins, many have intrinsically disordered regions (IDRs) that are needed for phase separation to occur. Accurate identification of IDRs that drive phase separation is important for testing the underlying mechanisms of phase separation, identifying biological processes that rely on phase separation, and designing sequences that modulate phase separation. To identify IDRs that drive phase separation, we first curated datasets of folded, ID, and PS ID sequences. We then used these sequence sets to examine how broadly existing amino acid property scales can be used to distinguish between the three classes of protein regions. We found that there are robust property differences between the classes and, consequently, that numerous combinations of amino acid property scales can be used to make robust predictions of protein phase separation. This result indicates that multiple, redundant mechanisms contribute to the formation of phase-separated droplets from IDRs. The top-performing scales were used to further optimize our previously developed predictor of PS IDRs, ParSe. We then modified ParSe to account for interactions between amino acids and obtained reasonable predictive power for mutations that have been designed to test the role of amino acid interactions in driving protein phase separation. Collectively, our findings provide further insight into the classification of IDRs and the elements involved in protein phase separation.