Determination of prognostic markers for COVID-19 disease severity using routine blood tests and machine learning.

The need for the identification of risk factors associated to COVID-19 disease severity remains urgent. Patients' care and resource allocation can be potentially different and are defined based on the current classification of disease severity. This classification is based on the analysis of clinical parameters and routine blood tests, which are not standardized across the globe. Some laboratory test alterations have been associated to COVID-19 severity, although these data are conflicting partly due to the different methodologies used across different studies. This study aimed to construct and validate a disease severity prediction model using machine learning (ML). Seventy-two patients admitted to a Brazilian hospital and diagnosed with COVID-19 through RT-PCR and/or ELISA, and with varying degrees of disease severity, were included in the study. Their electronic medical records and the results from daily blood tests were used to develop a ML model to predict disease severity. Using the above data set, a combination of five laboratorial biomarkers was identified as accurate predictors of COVID-19 severe disease with a ROC-AUC of 0.80 ​±â€‹ 0.13. Those biomarkers included prothrombin activity, ferritin, serum iron, ATTP and monocytes. The application of the devised ML model may help rationalize clinical decision and care.

Structure-based computational design of antibody mimetics: challenges and perspectives.

The design of antibody mimetics holds great promise for revolutionizing therapeutic interventions by offering alternatives to conventional antibody therapies. Structure-based computational approaches have emerged as indispensable tools in the rational design of those molecules, enabling the precise manipulation of their structural and functional properties. This review covers the main classes of designed antigen-binding motifs, as well as alternative strategies to develop tailored ones. We discuss the intricacies of different computational protein-protein interaction design strategies, showcased by selected successful cases in the literature. Subsequently, we explore the latest advancements in the computational techniques including the integration of machine and deep learning methodologies into the design framework, which has led to an augmented design pipeline. Finally, we verse onto the current challenges that stand in the way between high-throughput computer design of antibody mimetics and experimental realization, offering a forward-looking perspective into the field and the promises it holds to biotechnology.

Identification and characterization of a nonbiological small-molecular mimic of a Zika virus conformational neutralizing epitope.

Antigenic similarities between Zika virus (ZIKV) and other flaviviruses pose challenges to the development of virus-specific diagnostic tools and effective vaccines. Starting with a DNA-encoded one-bead-one-compound combinatorial library of 508,032 synthetic, non-natural oligomers, we selected and characterized small molecules that mimic ZIKV epitopes. High-throughput fluorescence-activated cell sorter-based bead screening was used to select molecules that bound IgG from ZIKV-immune but not from dengue-immune sera. Deep sequencing of the DNA from the "Zika-only" beads identified 40 candidate molecular structures. A lead candidate small molecule "CZV1-1" was selected that correctly identifies serum specimens from Zika-experienced patients with good sensitivity and specificity (85.3% and 98.4%, respectively). Binding competition studies of purified anti-CZV1-1 IgG against known ZIKV-specific monoclonal antibodies (mAbs) showed that CZV1-1 mimics a nonlinear, neutralizing conformational epitope in the domain III of the ZIKV envelope. Purified anti-CZV1-1 IgG neutralized infection of ZIKV in cell cultures with potencies comparable to highly specific ZIKV-neutralizing mAbs. This study demonstrates an innovative approach for identification of synthetic non-natural molecular mimics of conformational virus epitopes. Such molecular mimics may have value in the development of accurate diagnostic assays for Zika, as well as for other viruses.

Evolutionary deletions within the SARS-CoV-2 genome as signature trends for virus fitness and adaptation.

Coronaviruses are large RNA viruses that can infect and spread among humans and animals. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), responsible for coronavirus disease 2019, has evolved since its first detection in December 2019. Deletions are a common occurrence in SARS-CoV-2 evolution, particularly in specific genomic sites, and may be associated with the emergence of highly competent lineages. While deletions typically have a negative impact on viral fitness, some persist and become fixed in viral populations, indicating that they may confer advantageous benefits for the virus's adaptive evolution. This work presents a literature review and data analysis on structural losses in the SARS-CoV-2 genome and the potential relevance of specific signatures for enhanced viral fitness and spread.

Seroprevalence of Dengue, Chikungunya and Zika at the epicenter of the congenital microcephaly epidemic in Northeast Brazil: A population-based survey.

BACKGROUND: The four Dengue viruses (DENV) serotypes were re-introduced in Brazil's Northeast region in a couple of decades, between 1980's and 2010's, where the DENV1 was the first detected serotype and DENV4 the latest. Zika (ZIKV) and Chikungunya (CHIKV) viruses were introduced in Recife around 2014 and led to large outbreaks in 2015 and 2016, respectively. However, the true extent of the ZIKV and CHIKV outbreaks, as well as the risk factors associated with exposure to these viruses remain vague. METHODS: We conducted a stratified multistage household serosurvey among residents aged between 5 and 65 years in the city of Recife, Northeast Brazil, from August 2018 to February 2019. The city neighborhoods were stratified and divided into high, intermediate, and low socioeconomic strata (SES). Previous ZIKV, DENV and CHIKV infections were detected by IgG-based enzyme linked immunosorbent assays (ELISA). Recent ZIKV and CHIKV infections were assessed through IgG3 and IgM ELISA, respectively. Design-adjusted seroprevalence were estimated by age group, sex, and SES. The ZIKV seroprevalence was adjusted to account for the cross-reactivity with dengue. Individual and household-related risk factors were analyzed through regression models to calculate the force of infection. Odds Ratio (OR) were estimated as measure of effect. PRINCIPAL FINDINGS: A total of 2,070 residents' samples were collected and analyzed. The force of viral infection for high SES were lower as compared to low and intermediate SES. DENV seroprevalence was 88.7% (CI95%:87.0-90.4), and ranged from 81.2% (CI95%:76.9-85.6) in the high SES to 90.7% (CI95%:88.3-93.2) in the low SES. The overall adjusted ZIKV seroprevalence was 34.6% (CI95%:20.0-50.9), and ranged from 47.4% (CI95%:31.8-61.5) in the low SES to 23.4% (CI95%:12.2-33.8) in the high SES. The overall CHIKV seroprevalence was 35.7% (CI95%:32.6-38.9), and ranged from 38.6% (CI95%:33.6-43.6) in the low SES to 22.3% (CI95%:15.8-28.8) in the high SES. Surprisingly, ZIKV seroprevalence rapidly increased with age in the low and intermediate SES, while exhibited only a small increase with age in high SES. CHIKV seroprevalence according to age was stable in all SES. The prevalence of serological markers of ZIKV and CHIKV recent infections were 1.5% (CI95%:0.1-3.7) and 3.5% (CI95%:2.7-4.2), respectively. CONCLUSIONS: Our results confirmed continued DENV transmission and intense ZIKV and CHIKV transmission during the 2015/2016 epidemics followed by ongoing low-level transmission. The study also highlights that a significant proportion of the population is still susceptible to be infected by ZIKV and CHIKV. The reasons underlying a ceasing of the ZIKV epidemic in 2017/18 and the impact of antibody decay in susceptibility to future DENV and ZIKV infections may be related to the interplay between disease transmission mechanism and actual exposure in the different SES.

Inhibition of 3-Hydroxykynurenine Transaminase from Aedes aegypti and Anopheles gambiae: A Mosquito-Specific Target to Combat the Transmission of Arboviruses.

Arboviral infections such as Zika, chikungunya, dengue, and yellow fever pose significant health problems globally. The population at risk is expanding with the geographical distribution of the main transmission vector of these viruses, the Aedes aegypti mosquito. The global spreading of this mosquito is driven by human migration, urbanization, climate change, and the ecological plasticity of the species. Currently, there are no specific treatments for Aedes-borne infections. One strategy to combat different mosquito-borne arboviruses is to design molecules that can specifically inhibit a critical host protein. We obtained the crystal structure of 3-hydroxykynurenine transaminase (AeHKT) from A. aegypti, an essential detoxification enzyme of the tryptophan metabolism pathway. Since AeHKT is found exclusively in mosquitoes, it provides the ideal molecular target for the development of inhibitors. Therefore, we determined and compared the free binding energy of the inhibitors 4-(2-aminophenyl)-4-oxobutyric acid (4OB) and sodium 4-(3-phenyl-1,2,4-oxadiazol-5-yl)butanoate (OXA) to AeHKT and AgHKT from Anopheles gambiae, the only crystal structure of this enzyme previously known. The cocrystallized inhibitor 4OB binds to AgHKT with K i of 300 µM. We showed that OXA binds to both AeHKT and AgHKT enzymes with binding energies 2-fold more favorable than the crystallographic inhibitor 4OB and displayed a 2-fold greater residence time τ upon binding to AeHKT than 4OB. These findings indicate that the 1,2,4-oxadiazole derivatives are inhibitors of the HKT enzyme not only from A. aegypti but also from A. gambiae.

An artificial neural network model to predict structure-based protein-protein free energy of binding from Rosetta-calculated properties.

The prediction of the free energy (ΔG) of binding for protein-protein complexes is of general scientific interest as it has a variety of applications in the fields of molecular and chemical biology, materials science, and biotechnology. Despite its centrality in understanding protein association phenomena and protein engineering, the ΔG of binding is a daunting quantity to obtain theoretically. In this work, we devise a novel Artificial Neural Network (ANN) model to predict the ΔG of binding for a given three-dimensional structure of a protein-protein complex with Rosetta-calculated properties. Our model was tested using two data sets, and it presented a root-mean-square error ranging from 1.67 kcal mol-1 to 2.45 kcal mol-1, showing a better performance compared to the available state-of-the-art tools. Validation of the model for a variety of protein-protein complexes is showcased.

GM2/GM3 controls the organizational status of CD82/Met microdomains: further studies in GM2/GM3 complexation.

At cell surface gangliosides might associate with signal transducers proteins, grown factor receptors, integrins, small G-proteins and tetraspanins establishing microdomains, which play important role in cell adhesion, cell activation, motility, and growth. Previously, we reported that GM2 and GM3 form a heterodimer that interacts with the tetraspanin CD82, controlling epithelial cell mobility by inhibiting integrin-hepatocyte growth factor-induced cMet tyrosine kinase signaling. By using molecular dynamics simulations to study the molecular basis of GM2/GM3 interaction we demonstrate, here, that intracellular levels of Ca2+ mediate GM2/GM3 complexation via electrostatic interaction with their carboxyl groups, while hydrogen bonds between the ceramide groups likely aid stabilizing the complex. The presence of GM2/GM3 complex alters localization of CD82 on cell surface and therefore downstream signalization. These data contribute for the knowledge of how glycosylation may control signal transduction and phenotypic changes.

Identification of a Zika NS2B epitope as a biomarker for severe clinical phenotypes.

The identification of specific biomarkers for Zika infection and its clinical complications is fundamental to mitigate the infection spread, which has been associated with a broad range of neurological sequelae. We present the characterization of antibody responses in serum samples from individuals infected with Zika, presenting non-severe (classical) and severe (neurological disease) phenotypes, with high-density peptide arrays comprising the Zika NS1 and NS2B proteins. The data pinpoints one strongly IgG-targeted NS2B epitope in non-severe infections, which is absent in Zika patients, where infection progressed to the severe phenotype. This differential IgG profile between the studied groups was confirmed by multivariate data analysis. Molecular dynamics simulations and circular dichroism have shown that the peptide in solution presents itself in a sub-optimal conformation for antibody recognition, which led us to computationally engineer an artificial protein able to stabilize the NS2B epitope structure. The engineered protein was used to interrogate paired samples from mothers and their babies presenting Zika-associated microcephaly and confirmed the absence of NS2B IgG response in those samples. These findings suggest that the assessment of antibody responses to the herein identified NS2B epitope is a strong candidate biomarker for the diagnosis and prognosis of Zika-associated neurological disease.

The ongoing evolution of variants of concern and interest of SARS-CoV-2 in Brazil revealed by convergent indels in the amino (N)-terminal domain of the spike protein.

Mutations at both the receptor-binding domain (RBD) and the amino (N)-terminal domain (NTD) of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Spike (S) glycoprotein can alter its antigenicity and promote immune escape. We identified that SARS-CoV-2 lineages circulating in Brazil with mutations of concern in the RBD independently acquired convergent deletions and insertions in the NTD of the S protein, which altered the NTD antigenic-supersite and other predicted epitopes at this region. Importantly, we detected the community transmission of different P.1 lineages bearing NTD indels ∆69-70 (which can impact several SARS-CoV-2 diagnostic protocols), ∆144 and ins214ANRN, and a new VOI N.10 derived from the B.1.1.33 lineage carrying three NTD deletions (∆141-144, ∆211, and ∆256-258). These findings support that the ongoing widespread transmission of SARS-CoV-2 in Brazil generates new viral lineages that might be more resistant to antibody neutralization than parental variants of concern.

Are Zika virus cross-reactive antibodies against aquaporin-4 associated to Neuromyelitis Optica Spectrum Disorder?

Zika virus (ZIKV) infection has been associated with the development of Neuromyelitis Optica Spectrum Disorder (NMOSD). ZIKV-induced antibodies that putatively cross-react to aquaporin-4 (AQP4) protein are suggested to cause inflammation of the optic nerve. A region of similarity between AQP4 and the ZIKV NS2B protein was identified. Our data showed that ZIKV-associated NMOSD patients develop anti-AQP4 antibodies, but not anti-ZIKV NS2B antibodies, revealing that cross-reacting antibodies are not the underlying cause of this phenotype. ZIKV infection in mice showed persistent viral replication in the eye tissue, suggesting that NMOSD symptoms are consequence of viral infection of the optic nerve cells.

Repositioning Lopinavir, an HIV Protease Inhibitor, as a Promising Antifungal Drug: Lessons Learned from Candida albicans-In Silico, In Vitro and In Vivo Approaches.

The repurposing strategy was applied herein to evaluate the effects of lopinavir, an aspartic protease inhibitor currently used in the treatment of HIV-infected individuals, on the globally widespread opportunistic human fungal pathogen Candida albicans by using in silico, in vitro and in vivo approaches in order to decipher its targets on fungal cells and its antifungal mechanisms of action. Secreted aspartic proteases (Saps) are the obviously main target of lopinavir. To confirm this hypothesis, molecular docking assays revealed that lopinavir bound to the Sap2 catalytic site of C. albicans as well as inhibited the Sap hydrolytic activity in a typically dose-dependent manner. The inhibition of Saps culminated in the inability of C. albicans yeasts to assimilate the unique nitrogen source (albumin) available in the culture medium, culminating with fungal growth inhibition (IC50 = 39.8 µM). The antifungal action of lopinavir was corroborated by distinct microscopy analyses, which evidenced drastic and irreversible changes in the morphology that justified the fungal death. Furthermore, our results revealed that lopinavir was able to (i) arrest the yeasts-into-hyphae transformation, (ii) disturb the synthesis of neutral lipids, including ergosterol, (iii) modulate the surface-located molecules, such as Saps and mannose-, sialic acid- and N-acetylglucosamine-containing glycoconjugates, (iv) diminish the secretion of hydrolytic enzymes, such as Saps and esterase, (v) negatively influence the biofilm formation on polystyrene surface, (vi) block the in vitro adhesion to epithelial cells, (vii) contain the in vivo infection in both immunocompetent and immunosuppressed mice and (viii) reduce the Sap production by yeasts recovered from kidneys of infected animals. Conclusively, the exposed results highlight that lopinavir may be used as a promising repurposing drug against C. albicans infection as well as may be used as a lead compound for the development of novel antifungal drugs.

Immune evasion of SARS-CoV-2 variants of concern is driven by low affinity to neutralizing antibodies.

SARS-CoV-2 VOC immune evasion is mainly due to lower cross-reactivity from previously elicited class I/II neutralizing antibodies, while increased affinity to hACE2 plays a minor role. The affinity between antibodies and VOCs is impacted by remodeling of the electrostatic surface potential of the Spike RBDs. The P.3 variant is a putative VOC.

Differential Frequencies of HLA-DRB1, DQA1, and DQB1 Alleles and Haplotypes Are Observed in the Arbovirus-Related Neurological Syndromes.

BACKGROUND: We took advantage of the 2015-2016 Brazilian arbovirus outbreak (Zika [ZIKV]/dengue/chikungunya viruses) associated with neurological complications to type HLA-DRB1/DQA1/DQB1 variants in patients exhibiting neurological complications and in bone marrow donors from the same endemic geographical region. METHODS: DRB1/DQA1/DQB1 loci were typed using sequence-specific oligonucleotides. In silico studies were performed using X-ray resolved dimer constructions. RESULTS: The DQA1*01, DQA1*05, DQB1*02, or DQB1*06 genotypes/haplotypes and DQA1/DQB1 haplotypes that encode the putative DQA1/DQB1 dimers were overrepresented in the whole group of patients and in patients exhibiting peripheral neurological spectrum disorders (PSD) or encephalitis spectrum disorders (ESD). The DRB1*04, DRB1*13, and DQA1*03 allele groups protected against arbovirus neurological manifestation, being underrepresented in whole group of patients and ESD and PSD groups. Genetic and in silico studies revealed that DQA1/DQB1 dimers (1) were primarily associated with susceptibility to arbovirus infections; (2) can bind to a broad range of ZIKV peptides (235 of 1878 peptides, primarily prM and NS2A); and (3) exhibited hydrophilic and highly positively charged grooves when compared to the DRA1/DRB1 cleft. The protective dimer (DRA1/DRB1*04) bound a limited number of ZIKV peptides (40 of 1878 peptides, primarily prM). CONCLUSION: Protective haplotypes may recognize arbovirus peptides more specifically than susceptible haplotypes.

Rotational Profiler: A Fast, Automated, and Interactive Server to Derive Torsional Dihedral Potentials for Classical Molecular Simulations.

Rotational Profiler provides an analytical algorithm to compute sets of classical torsional dihedral parameters by fitting an empirical energy profile to a reference one that can be obtained experimentally or by quantum-mechanical methods. The resulting profiles are compatible with the functional forms in the most widely used biomolecular force fields (e.g., GROMOS, AMBER, OPLS, and CHARMM). The linear least-squares regression method is used to generate sets of parameters that best satisfy the fitting. Rotational Profiler is free to use, analytical, and force field/package independent. The formalism is herein described, and its usage, in an interactive and automated manner, is made available as a Web server at http://rotprof.lncc.br.

SuAVE: A Tool for Analyzing Curvature-Dependent Properties in Chemical Interfaces.

Curvature is an intrinsic feature of biological membranes underlying vital cellular processes such as endocytosis, membrane fusion-fission, trafficking, and remodeling. The continuous expansion of the spatiotemporal scales accessible to computational simulations nowadays makes possible quasi-atomistic molecular dynamics simulations of these processes. In despite of that, computation of the shapes and curvatures associated with the dynamics of biological membranes remains challenging. For this reason, the effect of curvature is often neglected in the analysis of quantities essential for the accurate description of membrane properties (e.g., area and volume per lipid, density profiles, membrane thickness). We propose an algorithm for surface assessment via grid evaluation (SuAVE) that relies on the application of a radial base function to interpolate points scattered across an interface of any shape. This enables the representation of the chemical interface as fully differentiable so that related geometrical properties can be calculated through the straightforward employment of well-established differential geometry techniques. Hence, the effect of different types or degrees of curvature can be accurately taken into account in the calculations of structural properties of any interfaces regardless of chemical composition, asymmetry, and level of atom coarseness. The main functionalities implemented in SuAVE are featured for a number of tetraacylated and hexaacylated Lipid-A membranes of distinct curvatures and a surfactant micelle. We show that the properties calculated for moderately to highly curved membranes differ significantly between curvature-dependent and -independent algorithms. The SuAVE software is freely available from www.biomatsite.net/suave-software .

The influence of biotinylation on the ability of a computer designed protein to detect B-cells producing anti-HIV-1 2F5 antibodies.

Antibodies against the HIV-1 2F5 epitope are known as one of the most powerful and broadly protective anti-HIV antibodies. Therefore, vaccine strategies that include the 2F5 epitope in their formulation require a robust method to detect specific anti-2F5 antibody production by B cells. Towards this goal, we have biotinylated a previously reported computer-designed protein carrying the HIV-1 2F5 epitope aiming the further development of a platform to detect human B-cells expressing anti-2F5 antibodies through flow cytometry. Biophysical and immunological properties of our devised protein were characterized by computer simulation and experimental methods. Biotinylation did not affect folding and improved protein stability and solubility. The biotinylated protein exhibited similar binding affinity trends compared to its unbiotinylated counterpart and was recognized by anti-HIV-1 2F5 antibodies expressed on the surface of patient-derived peripheral blood mononuclear cells. Moreover, we present a high affinity marker for the identification of epitope-specific B cells that can be used to measure the efficacy of vaccine strategies based on the HIV-1 envelope protein.

Influence of directional positive Darwinian selection-driven evolution on arboviruses Dengue and Zika virulence and pathogenesis.

Dengue (DENV) and Zika (ZIKV) viruses are antigenically and evolutionarily related; immunological cross-reactions between them have been associated to both cross-protection and infection-enhanced mechanisms. Here, DENV-1-4 and ZIKV were investigated through Bayesian coalescent-based approaches and selection-driven Darwinian evolution methods using robust datasets. Our findings show that both DENV and ZIKV, driven essentially by directional positive selection, have undergone evolution and diversification and that their entire polyproteins are subject to an intense directional evolution. Interestingly, positively selected codons mapped here are directly associated to DENV-1-2 virulence as well as the ZIKV burgeoning 2015-16 outbreak in the Americas, therefore, having impact on the pathogenesis of these viruses. Biochemical prediction analysis focusing on markers involved in virulence and viral transmission dynamics identified alterations in N-Glycosylation-, Phosphorylation- and Palmitoylation-sites in ZIKV sampled from different countries, hosts and isolation sources. Taking into account both DENV-ZIKV co-circulation either into and/or out of flavivirus-endemic regions, as well as recombination and quasispecies scenarios, these results indicate the action of a selection-driven evolution affecting the biology, virulence and pathogenesis of these pathogens in a non-randomized environment.

Duffy binding-like 1α adhesin from Plasmodium falciparum recognizes ABH histo-blood group saccharide in a type specific manner.

The ability of erythrocytes, infected by Plasmodium falciparum, to adhere to endothelial cells (cytoadherence) and to capture uninfected erythrocyte (rosetting) is the leading cause of death by severe malaria. Evidences link the binding of the adhesin Duffy Binding Like1-α (DBL1α) domain to the ABH histo-blood antigens with formation of rosettes. Inspired by this very close relationship between the disease susceptibility and individual blood type, here we investigate the structural requirements involved in the interaction of DBL1α with A, B and H histo-blood determinants and their subtypes. Our results evidence the high preference of DBL1α to A epitopes, in comparison to B and H epitopes. DBL1α interacts with ABH epitopes in subtype specific manner, presenting a remarkable affinity for type 2 structures, Fucα1-2Galß1-4GlcNAcß1, particularly the A2 epitope. The contacts made by DBL1α binding pocket and the ABH histo-blood groups were mapped by theoretical methods and supported by NMR experiments.

Aromatic Rings Commonly Used in Medicinal Chemistry: Force Fields Comparison and Interactions With Water Toward the Design of New Chemical Entities.

The identification of lead compounds usually includes a step of chemical diversity generation. Its rationale may be supported by both qualitative (SAR) and quantitative (QSAR) approaches, offering models of the putative ligand-receptor interactions. In both scenarios, our understanding of which interactions functional groups can perform is mostly based on their chemical nature (such as electronegativity, volume, melting point, lipophilicity etc.) instead of their dynamics in aqueous, biological solutions (solvent accessibility, lifetime of hydrogen bonds, solvent structure etc.). As a consequence, it is challenging to predict from 2D structures which functional groups will be able to perform interactions with the target receptor, at which intensity and relative abundance in the biological environment, all of which will contribute to ligand potency and intrinsic activity. With this in mind, the aim of this work is to assess properties of aromatic rings, commonly used for drug design, in aqueous solution through molecular dynamics simulations in order to characterize their chemical features and infer their impact in complexation dynamics. For this, common aromatic and heteroaromatic rings were selected and received new atomic charge set based on the direction and module of the dipole moment from MP2/6-31G* calculations, while other topological terms were taken from GROMOS53A6 force field. Afterwards, liquid physicochemical properties were simulated for a calibration set composed by nearly 40 molecules and compared to their respective experimental data, in order to validate each topology. Based on the reliance of the employed strategy, we expanded the dataset to more than 100 aromatic rings. Properties in aqueous solution such as solvent accessible surface area, H-bonds availability, H-bonds residence time, and water structure around heteroatoms were calculated for each ring, creating a database of potential interactions, shedding light on features of drugs in biological solutions, on the structural basis for bioisosterism and on the enthalpic/entropic costs for ligand-receptor complexation dynamics.