COVID-19 is a systemic infection that exerts significant impact on the metabolism. Yet, there is little information on how SARS-CoV-2 affects metabolism. Using NMR spectroscopy, we measured the metabolomic and lipidomic serum profile from 263 (training cohort) + 135 (validation cohort) symptomatic patients hospitalized after positive PCR testing for SARS-CoV-2 infection. We also established the profiles of 280 persons collected before the coronavirus pandemic started. PCA analyses discriminated both cohorts, highlighting the impact that the infection has in overall metabolism. The lipidomic analysis unraveled a pathogenic redistribution of the lipoprotein particle size and composition to increase the atherosclerotic risk. In turn, metabolomic analysis reveals abnormally high levels of ketone bodies (acetoacetic acid, 3-hydroxybutyric acid and acetone) and 2-hydroxybutyric acid, a readout of hepatic glutathione synthesis and marker of oxidative stress. Our results are consistent with a model in which SARS-CoV-2 infection induces liver damage associated with dyslipidemia and oxidative stress.

Essential cell division protein FtsZ forms the bacterial cytokinetic ring and is a target for new antibiotics. FtsZ monomers bind GTP and assemble into filaments. Hydrolysis to GDP at the association interface between monomers leads to filament disassembly. We have developed a homogeneous competition assay, employing the fluorescence anisotropy change of mant-GTP upon binding to nucleotide-free FtsZ, which detects compounds binding to the nucleotide site in FtsZ monomers and measures their affinities within the millimolar to 10 nM range. We have employed this method to determine the apparent contributions of the guanine, ribose, and the α-, β-, and γ-phosphates to the free energy change of nucleotide binding. Similar relative contributions have also been estimated through molecular dynamics and binding free energy calculations, employing the crystal structures of FtsZ-nucleotide complexes. We find an energetically dominant contribution of the β-phosphate, comparable to the whole guanosine moiety. GTP and GDP bind with similar observed affinity to FtsZ monomers. Loss of the regulatory γ-phosphate results in a predicted accommodation of GDP which has not been observed in the crystal structures. The binding affinities of a series of C8-substituted GTP analogues, known to inhibit FtsZ but not eukaryotic tubulin assembly, correlate with their inhibitory capacity on FtsZ polymerization. Our methods permit testing of FtsZ inhibitors targeting its nucleotide site, as well as compounds from virtual screening of large synthetic libraries. Our results give insight into the FtsZ-nucleotide interactions, which could be useful in the rational design of new inhibitors, especially GTP phosphate mimetics.

An ultrafast and accurate scoring function for protein-protein docking is presented. It includes (1) a molecular mechanics (MM) part based on a 12-6 Lennard-Jones potential; (2) an electrostatic component based on an implicit solvent model (ISM) with individual desolvation penalties for each partner in the protein-protein complex plus a hydrogen bonding term; and (3) a surface area (SA) contribution to account for the loss of water contacts upon protein-protein complex formation. The accuracy and performance of the scoring function, termed MM-ISMSA, have been assessed by (1) comparing the total binding energies, the electrostatic term, and its components (charge-charge and individual desolvation energies), as well as the per residue contributions, to results obtained with well-established methods such as APBSA or MM-PB(GB)SA for a set of 1242 decoy protein-protein complexes and (2) testing its ability to recognize the docking solution closest to the experimental structure as that providing the most favorable total binding energy. For this purpose, a test set consisting of 15 protein-protein complexes with known 3D structure mixed with 10 decoys for each complex was used. The correlation between the values afforded by MM-ISMSA and those from the other methods is quite remarkable (r(2) ∼ 0.9), and only 0.2-5.0 s (depending on the number of residues) are spent on a single calculation including an all vs all pairwise energy decomposition. On the other hand, MM-ISMSA correctly identifies the best docking solution as that closest to the experimental structure in 80% of the cases. Finally, MM-ISMSA can process molecular dynamics trajectories and reports the results as averaged values with their standard deviations. MM-ISMSA has been implemented as a plugin to the widely used molecular graphics program PyMOL, although it can also be executed in command-line mode. MM-ISMSA is distributed free of charge to nonprofit organizations.

Quantitative plasma lipoprotein and metabolite profiles were measured on an autonomous community of the Basque Country (Spain) cohort consisting of hospitalized COVID-19 patients (n = 72) and a matched control group (n = 75) and a Western Australian (WA) cohort consisting of (n = 17) SARS-CoV-2 positives and (n = 20) healthy controls using 600 MHz 1H nuclear magnetic resonance (NMR) spectroscopy. Spanish samples were measured in two laboratories using one-dimensional (1D) solvent-suppressed and T2-filtered methods with in vitro diagnostic quantification of lipoproteins and metabolites. SARS-CoV-2 positive patients and healthy controls from both populations were modeled and cross-projected to estimate the biological similarities and validate biomarkers. Using the top 15 most discriminatory variables enabled construction of a cross-predictive model with 100% sensitivity and specificity (within populations) and 100% sensitivity and 82% specificity (between populations). Minor differences were observed between the control metabolic variables in the two cohorts, but the lipoproteins were virtually indistinguishable. We observed highly significant infection-related reductions in high-density lipoprotein (HDL) subfraction 4 phospholipids, apolipoproteins A1 and A2,that have previously been associated with negative regulation of blood coagulation and fibrinolysis. The Spanish and Australian diagnostic SARS-CoV-2 biomarkers were mathematically and biologically equivalent, demonstrating that NMR-based technologies are suitable for the study of the comparative pathology of COVID-19 via plasma phenotyping.

COVID‐19 is a systemic infectious disease that may affect many organs, accompanied by a measurable metabolic dysregulation. The disease is also associated with significant mortality, particularly among the elderly, patients with comorbidities, and solid organ transplant recipients. Yet, the largest segment of the patient population is asymptomatic, and most other patients develop mild to moderate symptoms after SARS‐CoV‐2 infection. Here, we have used NMR metabolomics to characterize plasma samples from a cohort of the abovementioned group of COVID‐19 patients ( n = 69), between 3 and 10 months after diagnosis, and compared them with a set of reference samples from individuals never infected by the virus ( n = 71). Our results indicate that half of the patient population show abnormal metabolism including porphyrin levels and altered lipoprotein profiles six months after the infection, while the other half show little molecular record of the disease. Remarkably, most of these patients are asymptomatic or mild COVID‐19 patients, and we hypothesize that this is due to a metabolic reflection of the immune response stress.

Inborn errors of metabolism (IEMs) are rare diseases produced by the accumulation of abnormal amounts of metabolites, toxic to the newborn. When not detected on time, they can lead to irreversible physiological and psychological sequels or even demise. Metabolomics has emerged as an efficient and powerful tool for IEM detection in newborns, children, and adults with late onset. In here, we screened urine samples from a large set of neonates (470 individuals) from a homogeneous population (Basque Country), for the identification of congenital metabolic diseases using NMR spectroscopy. Absolute quantification allowed to derive a probability function for up to 66 metabolites that adequately describes their normal concentration ranges in newborns from the Basque Country. The absence of another 84 metabolites, considered abnormal, was routinely verified in the healthy newborn population and confirmed for all but 2 samples, of which one showed toxic concentrations of metabolites associated to ketosis and the other one a high trimethylamine concentration that strongly suggested an episode of trimethylaminuria. Thus, a non-invasive and readily accessible urine sample contains enough information to assess the potential existence of a substantial number (>70) of IEMs in newborns, using a single, automated and standardized 1H- NMR-based analysis.

A new implicit solvent model for computing the electrostatics binding free energy in protein-ligand docking is proposed. The new method is based on an adaptation of the screening coulombic potentials proposed originally by Hassan et al. (J Phys Chem B 2000;104:6490-6498). In essence, it relies on two basic assumptions; (i) solvent screening can be accounted for by means of radially dependent sigmoidal dielectric functions and; (ii) the effective atom Born radii can be expressed only as a function of the exposed atom surface. Parameters of the model other than radii and charges are generic. These were optimized for a dataset of 826 protein-ligand complexes, comprising both X-ray complexes for 23 receptors as well as decoys generated by docking computations. We show that the new model provides satisfactory results when benchmarked against reference values based on the numerical solution of the Poisson equation, with a root mean square error of 4.2 kcal/mol over a range of approximately 40 kcal/mol in electrostatics binding free energies, a cross-validated r2 of 0.81, a slope of 0.97, and an intercept of 1.06 kcal/mol. We show that the model is appropriate for ligands of different sizes, polarities, overall charge, and chemical composition. Furthermore, not only the total value of the electrostatic contribution to the binding free energy, but also its components (coulombic term, receptor desolvation, and ligand desolvation) are reasonably well reproduced. Computation times of approximately 0.030 s per pose are obtained on a single processor desktop workstation.

We present gCOMBINE, a Java-written graphical user interface (GUI) for performing comparative binding energy (COMBINE) analysis (Ortiz et al. J Med Chem 1995; 38:2681-2691) on a set of ligand-receptor complexeswith the aim of deriving highly informative quantitative structure-activity relationships. The essence of the method is to decompose the ligand-receptor interaction energies into a series of terms, explore the origins of the variance within the set using Principal Component Analysis, and then assign weights to selected ligandresidue interactions using partial least squares analysis to correlate with the experimental activities or binding affinities. The GUI allows plenty of interactivity and provides multiple plots representing the energy descriptors entering the analysis, scores, loadings, experimental versus predicted regression lines, and the evolution of parameterssuch as r(2) (correlation coefficient), q(2) (cross-validated r(2)), and prediction errors as the number of extracted latent variables increases. Other representative features include the implementation of a sigmoidal dielectric function for electrostatic energy calculations, alternative cross-validation procedures (leave-N-out and random groups), drawing of confidence ellipses, and the possibility to carry out several additional tasks such as optional truncation of positive interaction energy values and generation of ready-to-use PDB files containing information related to the importance for activity of individual protein residues. This information can be displayed and color-coded using a standard molecular graphics program such as PyMOL. It is expected that this user-friendly tool will expand the applicability of the COMBINE analysis method and encourage more groups to use it in their drug design research programs.