Studying SARS-CoV-2 interactions using phage-displayed receptor binding domain as a model protein.

SARS-CoV-2 receptor binding domain (RBD) mediates viral entry into human cells through its interaction with angiotensin converting enzyme 2 (ACE2). Most neutralizing antibodies elicited by infection or vaccination target this domain. Such a functional relevance, together with large RBD sequence variability arising during viral spreading, point to the need of exploring the complex landscape of interactions between RBD-derived variants, ACE2 and antibodies. The current work was aimed at developing a simple platform to do so. Biologically active and antigenic Wuhan-Hu-1 RBD, as well as mutated RBD variants found in nature, were successfully displayed on filamentous phages. Mutational scanning confirmed the global plasticity of the receptor binding motif within RBD, highlighted residues playing a critical role in receptor binding, and identified mutations strengthening the interaction. The ability of vaccine-induced antibodies to inhibit ACE2 binding of many mutated RBD variants, albeit at different extents, was shown. Amino acid replacements which could compromise such inhibitory potential were underscored. The expansion of our approach could be the starting point for a large-scale phage-based exploration of diversity within RBD of SARS-CoV-2 and related coronaviruses, useful to understand structure-function relationships, to engineer RBD proteins, and to anticipate changes to watch during viral evolution.

Inhibition of Pertussis Toxin by Human α-Defensins-1 and -5: Differential Mechanisms of Action.

Whooping cough is a severe childhood disease, caused by the bacterium Bordetella pertussis, which releases pertussis toxin (PT) as a major virulence factor. Previously, we identified the human antimicrobial peptides α-defensin-1 and -5 as inhibitors of PT and demonstrated their capacity to inhibit the activity of the PT enzyme subunit PTS1. Here, the underlying mechanism of toxin inhibition was investigated in more detail, which is essential for developing the therapeutic potential of these peptides. Flow cytometry and immunocytochemistry revealed that α-defensin-5 strongly reduced PT binding to, and uptake into cells, whereas α-defensin-1 caused only a mild reduction. Conversely, α-defensin-1, but not α-defensin-5 was taken up into different cell lines and interacted with PTS1 inside cells, based on proximity ligation assay. In-silico modeling revealed specific interaction interfaces for α-defensin-1 with PTS1 and vice versa, unlike α-defensin-5. Dot blot experiments showed that α-defensin-1 binds to PTS1 and even stronger to its substrate protein Gαi in vitro. NADase activity of PTS1 in vitro was not inhibited by α-defensin-1 in the absence of Gαi. Taken together, these results suggest that α-defensin-1 inhibits PT mainly by inhibiting enzyme activity of PTS1, whereas α-defensin-5 mainly inhibits cellular uptake of PT. These findings will pave the way for optimization of α-defensins as novel therapeutics against whooping cough.

Molecular Characterization of Coxsackievirus A24v from Feces and Conjunctiva Reveals Epidemiological Links.

Coxsackievirus A24 variant (CVA24v), the main causative agent of acute hemorrhagic conjunctivitis (AHC), can be isolated from both the eyes and lower alimentary tract. However, the molecular features of CVA24v in feces is not well-documented. In this study, we compared the VP1 and 3C sequences of CVA24v strains isolated from feces during AHC epidemics in Cuba in 1997, 2003, and 2008-2009 with those obtained from conjunctival swabs during the same epidemic period. The sequence analyses of the 3C and VP1 region of stool isolates from the three epidemics showed a high degree of nucleotide identity (ranging from 97.3-100%) to the corresponding conjunctival isolates. The phylogenetic analysis showed that fecal CVA24v isolates from the 1997 and 2003 Cuban outbreaks formed a clade with CVA24v strains isolated from conjunctival swabs in Cuba and other countries during the same period. There were three amino acid changes (3C region) and one amino acid change (VP1 region) in seven CVA24v strains isolated sequentially over 20 days from fecal samples of one patient, suggesting viral replication in the intestine. Despite these substitutions, the virus from the conjunctival swab and fecal samples were genetically very similar. Therefore, fecal samples should be considered as a reliable alternative sample type for the routine molecular diagnosis and molecular epidemiology of CVA24v, also during outbreaks of AHC.

Molecular evolution of coxsackievirus A24v in Cuba over 23-years, 1986-2009.

Coxsackievirus A24 variant (CVA24v) is a major causative agent of acute hemorrhagic conjunctivitis outbreaks worldwide, yet the evolutionary and transmission dynamics of the virus remain unclear. To address this, we analyzed and compared the 3C and partial VP1 gene regions of CVA24v isolates obtained from five outbreaks in Cuba between 1986 and 2009 and strains isolated worldwide. Here we show that Cuban strains were homologous to those isolated in Africa, the Americas and Asia during the same time period. Two genotypes of CVA24v (GIII and GIV) were repeatedly introduced into Cuba and they arose about two years before the epidemic was detected. The two genotypes co-evolved with a population size that is stable over time. However, nucleotide substitution rates peaked during pandemics with 4.39 × 10-3 and 5.80 × 10-3 substitutions per site per year for the 3C and VP1 region, respectively. The phylogeographic analysis identified 25 and 19 viral transmission routes based on 3C and VP1 regions, respectively. Pandemic viruses usually originated in Asia, and both China and Brazil were the major hub for the global dispersal of the virus. Together, these data provide novel insight into the epidemiological dynamics of this virus and possibly other pandemic viruses.

Enhancing Acute Oral Toxicity Predictions by using Consensus Modeling and Algebraic Form-Based 0D-to-2D Molecular Encodes.

Quantitative structure-activity relationships (QSAR) are introduced to predict acute oral toxicity (AOT), by using the QuBiLS-MAS (acronym for quadratic, bilinear and N-Linear maps based on graph-theoretic electronic-density matrices and atomic weightings) framework for the molecular encoding. Three training sets were employed to build the models: EPA training set (5931 compounds), EPA-full training set (7413 compounds), and Zhu training set (10 152 compounds). Additionally, the EPA test set (1482 compounds) was used for the validation of the QSAR models built on the EPA training set, while the ProTox (425 compounds) and T3DB (284 compounds) external sets were employed for the assessment of all the models. The k-nearest neighbor, multilayer perceptron, random forest, and support vector machine procedures were employed to build several base (individual) models. The base models with REPA-training ≥ 0.75 ( R = correlation coefficient) and MAEEPA-training ≤ 0.5 (MAE = mean absolute error) were retained to build consensus models. As a result, two consensus models based on the minimum operator and denoted as M19 and M22, as well as a consensus model based on the weighted average operator and denoted as M24, were selected as the best ones for each training set considered. According to the applicability domain (AD) analysis performed, model M19 (built on the EPA training set) has MAEtest-AD = 0.4044, MAEProTox-AD = 0.4067 and MAET3DB-AD = 0.2586 on the EPA test set, ProTox external set, and T3DB external set, respectively; whereas model M22 (built on the EPA-full set) and model M24 (built on the Zhu set) present MAEProTox-AD = 0.3992 and MAET3DB-AD = 0.2286, and MAEProTox-AD = 0.3773 and MAET3DB-AD = 0.2471 on the two external sets accounted for, respectively. These outcomes were compared and statistically validated with respect to 14 QSAR methods (e.g., admetSAR, ProTox-II) from the literature. As a result, model M22 presents the best overall performance. In addition, a retrospective study on 261 withdrawn drugs due to their toxic/side effects was performed, to assess the usefulness of prospectively using the QSAR models proposed in the labeling of chemicals. A comparison with regard to the methods from the literature was also made. As a result, model M22 has the best ability of labeling a compound as toxic according to the globally harmonized system of classification and labeling of chemicals. Therefore, it can be concluded that the models proposed, especially model M22, constitute prominent tools for studying AOT, at providing the best results among all the methods examined. A freely available software was also developed to be used in virtual screening tasks ( http://tomocomd.com/apps/ptoxra ).

Choquet integral-based fuzzy molecular characterizations: when global definitions are computed from the dependency among atom/bond contributions (LOVIs/LOEIs).

BACKGROUND: Several topological (2D) and geometric (3D) molecular descriptors (MDs) are calculated from local vertex/edge invariants (LOVIs/LOEIs) by performing an aggregation process. To this end, norm-, mean- and statistic-based (non-fuzzy) operators are used, under the assumption that LOVIs/LOEIs are independent (orthogonal) values of one another. These operators are based on additive and/or linear measures and, consequently, they cannot be used to encode information from interrelated criteria. Thus, as LOVIs/LOEIs are not orthogonal values, then non-additive (fuzzy) measures can be used to encode the interrelation among them. RESULTS: General approaches to compute fuzzy 2D/3D-MDs from the contribution of each atom (LOVIs) or covalent bond (LOEIs) within a molecule are proposed, by using the Choquet integral as fuzzy aggregation operator. The Choquet integral-based operator is rather different from the other operators often used for the 2D/3D-MDs calculation. It performs a reordering step to fuse the LOVIs/LOEIs according to their magnitudes and, in addition, it considers the interrelation among them through a fuzzy measure. With this operator, fuzzy definitions can be derived from traditional or recent MDs; for instance, fuzzy Randic-like connectivity indices, fuzzy Balaban-like indices, fuzzy Kier-Hall connectivity indices, among others. To demonstrate the feasibility of using this operator, the QuBiLS-MIDAS 3D-MDs were used as study case and, as a result, a module was built into the corresponding software to compute them ( http://tomocomd.com/qubils-midas ). Thus, it is the only software reported in the literature that can be employed to determine Choquet integral-based fuzzy MDs. Moreover, regression models were created on eight chemical datasets. In this way, a comparison between the results achieved by the models based on the non-fuzzy QuBiLS-MIDAS 3D-MDs with regard to the ones achieved by the models based on the fuzzy QuBiLS-MIDAS 3D-MDs was made. As a result, the models built with the fuzzy QuBiLS-MIDAS 3D-MDs achieved the best performance, which was statistically corroborated through the Wilcoxon signed-rank test. CONCLUSIONS: All in all, it can be concluded that the Choquet integral constitutes a prominent alternative to compute fuzzy 2D/3D-MDs from LOVIs/LOEIs. In this way, better characterizations of the compounds can be obtained, which will be ultimately useful in enhancing the modelling ability of existing traditional 2D/3D-MDs.

Examining the predictive accuracy of the novel 3D N-linear algebraic molecular codifications on benchmark datasets.

BACKGROUND: Recently, novel 3D alignment-free molecular descriptors (also known as QuBiLS-MIDAS) based on two-linear, three-linear and four-linear algebraic forms have been introduced. These descriptors codify chemical information for relations between two, three and four atoms by using several (dis-)similarity metrics and multi-metrics. Several studies aimed at assessing the quality of these novel descriptors have been performed. However, a deeper analysis of their performance is necessary. Therefore, in the present manuscript an assessment and statistical validation of the performance of these novel descriptors in QSAR studies is performed. RESULTS: To this end, eight molecular datasets (angiotensin converting enzyme, acetylcholinesterase inhibitors, benzodiazepine receptor, cyclooxygenase-2 inhibitors, dihydrofolate reductase inhibitors, glycogen phosphorylase b, thermolysin inhibitors, thrombin inhibitors) widely used as benchmarks in the evaluation of several procedures are utilized. Three to nine variable QSAR models based on Multiple Linear Regression are built for each chemical dataset according to the original division into training/test sets. Comparisons with respect to leave-one-out cross-validation correlation coefficients[Formula: see text] reveal that the models based on QuBiLS-MIDAS indices possess superior predictive ability in 7 of the 8 datasets analyzed, outperforming methodologies based on similar or more complex techniques such as: Partial Least Square, Neural Networks, Support Vector Machine and others. On the other hand, superior external correlation coefficients[Formula: see text] are attained in 6 of the 8 test sets considered, confirming the good predictive power of the obtained models. For the [Formula: see text] values non-parametric statistic tests were performed, which demonstrated that the models based on QuBiLS-MIDAS indices have the best global performance and yield significantly better predictions in 11 of the 12 QSAR procedures used in the comparison. Lastly, a study concerning to the performance of the indices according to several conformer generation methods was performed. This demonstrated that the quality of predictions of the QSAR models based on QuBiLS-MIDAS indices depend on 3D structure generation method considered, although in this preliminary study the results achieved do not present significant statistical differences among them. CONCLUSIONS: As conclusions it can be stated that the QuBiLS-MIDAS indices are suitable for extracting structural information of the molecules and thus, constitute a promissory alternative to build models that contribute to the prediction of pharmacokinetic, pharmacodynamics and toxicological properties on novel compounds.Graphical abstractComparative graphical representation of the performance of the novel QuBiLS-MIDAS 3D-MDs with respect to other methodologies in QSAR modeling of eight chemical datasets.