Biosynthetic enzymes of the SARS-CoV-2 as potential targets for the discovery of new antiviral drugs

La aparición de la pandemia producida por la COVID-19 (enfermedad producida por coronavirus 2019), cuyo agente causal es el SARS-CoV-2, ha provocado una gran preocupación a nivel mundial. Esta emergencia sanitaria ha puesto de manifiesto la necesidad urgente que existe de desarrollar o bien una nueva vacuna o bien agentes terapéuticos antivirales que permitan combatir al SARS-CoV-2. El reposicionamiento de fármacos es una de las estrategias más rápidas y prácticas de identificar rápidamente nuevos fármacos que permitirían prevenir, controlar o incluso erradicar el virus. Encontrar agentes terapéuticos que actúen directamente sobre enzimas específicas que tengan un rol esencial en la replicación viral será un gran logro en la búsqueda de antivirales. En este trabajo se analizan las características generales de varias enzimas que desempeñan un papel fundamental en la biosíntesis o replicación del virus y su potencial como dianas terapéuticas para el desarrollo de nuevos compuestos activos contra el SARS-CoV-2. Este trabajo provee las bases y dirección para el desarrollo de futuras investigaciones de desarrollo de nuevos fármacos o el reposicionamiento de fármacos conocidos para combatir la COVID-19.The outbreak of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has raised a major global health concern. This urgent situation is pressing the world to respond with the development of novel vaccine or small molecule therapeutics for SARS-CoV-2. Drug repurposing screening is regarded as one of the most practical and rapid approaches for the discovery of such therapeutics. Direct-acting agents, targeting specific viral enzymes that play an essential role in viral replication, represent a milestone in antiviral therapy. Several biosynthetic enzymes of the SARS-CoV-2 were analyzed as potential targets to develop new therapeutic drugs. This work provides a basis and directions for future drug development and reuse on the protein level of COVID-19.Ciencias Experimentale

Enzimas de la biosíntesis del virus SARS-CoV-2 como dianas potenciales para el descubrimiento de nuevos antivirales

La aparición de la pandemia producida por la COVID-19 (enfermedad producida por coronavirus 2019), cuyo agente causal es el SARS-CoV-2, ha provocado una gran preocupación a nivel mundial. Esta emergencia sanitaria ha puesto de manifiesto la necesidad urgente que existe de desarrollar o bien una nueva vacuna o bien agentes terapéuticos antivirales que permitan combatir al SARS-CoV-2. El reposicionamiento de fármacos es una de las estrategias más rápidas y prácticas de identificar rápidamente nuevos fármacos que permitirían prevenir, controlar o incluso erradicar el virus. Encontrar agentes terapéuticos que actúen directamente sobre enzimas específicas que tengan un rol esencial en la replicación viral será un gran logro en la búsqueda de antivirales. En este trabajo se analizan las características generales de varias enzimas que desempeñan un papel fundamental en la biosíntesis o replicación del virus y su potencial como dianas terapéuticas para el desarrollo de nuevos compuestos activos contra el SARS-CoV-2. Este trabajo provee las bases y dirección para el desarrollo de futuras investigaciones de desarrollo de nuevos fármacos o el reposicionamiento de fármacos conocidos para combatir la COVID-19

Atom, atom-type, and total linear indices of the "molecular pseudograph's atom adjacency matrix": Application to QSPR/QSAR studies of organic compounds

In this paper we describe the application in QSPR/QSAR studies of a new group of molecular descriptors: atom, atom-type and total linear indices of the molecular pseudograph's atom adjacency matrix. These novel molecular descriptors were used for the prediction of boiling point and partition coefficient (log P), specific rate constant (log k), and antibacterial activity of 28 alkyl-alcohols and 34 derivatives of 2-furylethylenes, respectively. For this purpose two quantitative models were obtained to describe the alkyl-alcohols' boiling points. The first one includes only two total linear indices and showed a good behavior from a statistical point of view (R2 = 0.984, s = 3.78, F = 748.57, q2 = 0.981, and scv = 3.91). The second one includes four variables [3 global and 1 local (heteroatom) linear indices] and it showed an improvement in the description of physical property (R 2 = 0.9934, s = 2.48, F = 871.96, q2 = 0.990, and s cv = 2.79). Later, linear multiple regression analysis was also used to describe log P and log k of the 2-furyl-ethylenes derivatives. These models were statistically significant [(R2 = 0.984, s = 0.143, and F = 113.38) and (R2 = 0.973, s = 0.26 and F = 161.22), respectively] and showed very good stability to data variation in leave-one-out (LOO) cross-validation experiment [(q2 = 0.93.8 and scv = 0.178) and (q2 = 0.948 and scv = 0.33), respectively]. Finally, a linear discriminant model for classifying antibacterial activity of these compounds was also achieved with the use of the atom and atom-type linear indices. The global percent of good classification in training and external test set obtained was of 94.12% and 100.0%, respectively. The comparison with other approaches (connectivity indices, total and local spectral moments, quantum chemical descriptors, topographic indices and Estate/biomolecular encounter parameters) reveals a good behavior of our method. The approach described in this paper appears to be a very promising structural invariant, useful for QSPR/QSAR studies and computer-aided "rational" drug design.Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas (INIFTA

Big-Datasets Manager: una herramienta libre para la manipulación de ficheros de datos con número elevado de instancias y atributos

A program called Big-Datasets Manager designed to manage large text documents with information structured in instances and attributes is introduced. The program has a friendly graphical interface, is free and multiplatform. Big-Datasets Manager allows editing text datasets for data mining analyses, common tasks are related to selecting and ordering attributes, filtering and replace data, concatenate files, etc. The program is aimed to specialists in medicinal chemistry, economic, social science specialists or other specialists who employ large files of data structured in rows (instances) and columns (attributes).Se introduce un programa denominado Big-Datasets Manager destinado a la gestión de documentos con grandes volúmenes de información estructurada en instancias y atributos. El programa posee una interfaz gráfica amigable, es libre y multiplataforma. El software permite dar solución a problemáticas usuales en la edición de documentos que se han de emplear en análisis de minería de datos cuya base implica seleccionar y ordenar atributos, filtrar y remplazar datos y concatenar documentos, entre otros. El programa está dirigido a especialistas de la química medicinal, económicos, especialistas de las ciencias sociales y otros especialistas que empleen grandes ficheros de datos estructurados en filas y columnas

Larvicidal activity prediction against Aedes aegypti mosquito using computational tools

Background & objectives: Aedes aegypti is an important vector for transmission of dengue, yellow fever, chikun- gunya, arthritis, and Zika fever. According to the World Health Organization, it is estimated that Ae. aegypti causes 50 million infections and 25,000 deaths per year. Use of larvicidal agents is one of the recommendations of health organizations to control mosquito populations and limit their distribution. The aim of present study was to deduce a mathematical model to predict the larvicidal action of chemical compounds, based on their structure. Methods: A series of different compounds with experimental evidence of larvicidal activity were selected to develop a predictive model, using multiple linear regression and a genetic algorithm for the selection of variables, implemented in the QSARINS software. The model was assessed and validated using the OECDs principles. Results: The best model showed good value for the determination coefficient (R2 = 0.752), and others parameters were appropriate for fitting (s = 0.278 and RMSEtr = 0.261). The validation results confirmed that the model hasgood robustness (Q2LOO = 0.682) and stability (R2-Q2LOO = 0.070) with low correlation between the descriptors (KXX = 0.241), an excellent predictive power (R2 ext = 0.834) and was product of a non-random correlation R2 Y-scr = 0.100). Interpretation & conclusion: The present model shows better parameters than the models reported earlier in the literature, using the same dataset, indicating that the proposed computational tools are more efficient in identifying novel larvicidal compounds against Ae. aegypti

Atom, Atom-Type, and Total Linear Indices of the “Molecular Pseudograph’s Atom Adjacency Matrix”: Application to QSPR/QSAR Studies of Organic Compounds

group of molecular descriptors: atom, atom-type and total linear indices of the molecular pseudograph’s atom adjacency matrix. These novel molecular descriptors were used for the prediction of boiling point and partition coefficient (log P), specific rate constant (log k), and antibacterial activity of 28 alkyl-alcohols and 34 derivatives of 2-furylethylenes, respectively. For this purpose two quantitative models were obtained to describe the alkyl-alcohols ’ boiling points. The first one includes only two total linear indices and showed a good behavior from a statistical point of view (R 2 = 0.984, s = 3.78, F = 748.57, q 2 = 0.981, and scv = 3.91). The second one includes four variables [3 global and 1 local (heteroatom) linear indices] and it showed an improvement in the description of physical property (R = 0.9934, s = 2.48, F = 871.96, q 2 = 0.990, and scv = 2.79). Later, linear multiple regression analysis was also used to describe log P and log k of the 2-furyl-Molecules 2004, 9 1101 ethylenes derivatives. These models were statistically significant [(R = 0.984, s = 0.143

Isolation and characterization of extracellular vesicles in Candida albicans

Background: The occurrence of systemic infections due to C. albicans has increased especially in critically ill patients. In fungal infections, secretory mechanisms are key events for disease establishment. Recent findings demonstrate that fungal organisms release many molecular com-ponents to the extracellular space in extracellular vesicles. Aims: We develop a method to obtain exosomes from yeast cultures of the Candida albicans. Methods: Yeast strains used in this work were C. albicans SC5314, C. parapsilosis (ATCC 22019) and C. krusei (ATCC 6258). Yeasts were grown at 37.º in liquid YPD medium. The cell cultures were centrifuged and the supernatant filtered through sterile nitrocellulose. Filtrates were concentrated and centrifuged using an ultracentrifuge. The sediment was analyzed by electron microscopy of transmission.Results: The transmission of electron microscopy and nanoparticle tracking analysis confirmed the presence of extracellular vesicles (exosomes) of sizes between 100 and 200 nm and the absence of cellular contaminants. This was ratified by the characterization of proteins performed through the western blot technique, where the absence of cell contamination in the preparations was assessed. Conclusions: The method proves to be highly effective due to the homogeneity and purity of the obtained microvesicles. The protocol developed in this paper proves to be effective for obtaining exosomes of other Candida species, which will allow future studies to determine its protein composition and the role that these vesicles can play.Contexto: La aparición de infecciones sistémicas por C. albicans ha aumentado sobre todo en pacientes graves. En las infecciones fúngicas, los mecanismos de secreción son eventos clave para que el establecimiento de la enfermedad. Hallazgos recientes demuestran que los organismos fúngicos liberan muchos componentes moleculares al espacio extracelular en vesículas extracelulares. Objetivos: Desarrollamos un método para obtener exosomas de cultivos de levadura de Candida albicans. Métodos: Las cepas de levadura que se usaron en este trabajo son C. albicans SC5314, C. parapsilosis (ATCC 22019) y C. krusei (ATCC 6258). Las levaduras se cultivaron a 37.º C en un medio YPD líquido. Los cultivos de células fueron centrifugados y el sobrenadante, filtrado por medio de nitrocelulosa estéril. Los filtrados se concentraron y centrifugaron usando una ultracentrifugadora. El sedimento fue analizado por un microscopio electrónico de transmisión. Resultados: La microscopía electrónica de transmisión y el análisis de nanopartículas confirman la presencia de vesículas extracelulares (exosomas) de un tamaño entre 100 y 200 nm, así como la ausencia de contaminantes celulares. Esto se ratificó mediante la caracterización de proteínas obtenidas por medio de la técnica de Western blot, donde se evaluó la ausencia de contaminación celular en las preparaciones. Conclusiones: El método es altamente eficaz dada la homogeneidad y la pureza de las microvesículas obtenidas. El protocolo desarrollado en este artículo demuestra ser efectivo para obtener exosomas de otras especies Candida, lo que permitirá que en futuros estudios se determine su composición proteica y el papel que estas vesículas pueden desempeñar

Prediction of the Binding Affinity between Fenoterol Derivatives and the b2 Adrenergic Receptor Using Atom-Based 3D-Chiral LinearIndices

The non-stochastic and stochastic atom-based 3D-chiral quadratic indices were applied to the study of the β2 - adrenoceptor (β2 -AR) agonist effect (binding affinities) between a set of 26 stereoisomers of fenoterol, reported with this activity. Linear multiple regression analysis was carried out to predict the β2 -AR binding affinities of the stereoisomers. Two statistically significant QSAR models, able to describe more than the 92% of the variance of the experimental binding affinities, were obtained using non-stochastic (R2 = 0.924 and s = 0.21) and stochastic (R2 = 0.92 and s = 0.22) 3D-chiral linear indices, respectively. The predictability and stability (robustness) of the obtained models (assessed by the leave-one-out cross-validation experiment) yielded values of q2 = 0.893 (scv = 0.237) and q2 = 0.886 (scv = 0.245), respectively. The results obtained with our approach were slightly better than the results of a 3DQSAR model, obtained with the CoMFA method (R2 = 0.920, q2 = 0.847 and scv = 0.309). The results of our work demonstrate the usefulness of our topological approach for drug discovery of new lead compounds, even in those studies in which the three-dimensional configuration of the chemicals play an important role in the biological activity.Los índices lineales 3D-quirales no-estocásticos y estocásticos basados en relaciones de átomos son aplicados al estudio del efecto agonista (afinidad de unión) sobre el receptor adrenérgico β2 (β2 -AR) entre una serie de 26 estereoisómeros del fenoterol, a los cuales se les ha reportado esta actividad. Una regresión lineal múltiple es llevada a cabo para predecir la afinidad de unión β2 -AR de los estereoisómeros. Se obtienen dos modelos QSAR estadísticamente significativos, capaces de describir más del 92 % de la varianza experimental de las afinidades de unión, empleando los índices lineales 3D-quirales no-estocásticos (R2 = 0.924 y s = 0.21) y estocásticos (R2 = 0.92 y s = 0.22) respectivamente. El poder predictivo y la robustez de los modelos obtenidos (comprobados mediante una validación cruzada dejando-uno-fuera) alcanzan valores de q2 = 0.893 (scv = 0.237) y q2 = 0.886 (scv = 0.245), correspondientemente. Los resultados obtenidos con nuestro enfoque fueron ligeramente superiores a aquellos resultados obtenidos previamente con un modelo 3D-QSAR, empleando el método CoMFA (R2 = 0.920, q2 = 0.847 y scv = 0.309). Los resultados de nuestro trabajo demuestran la utilidad de nuestro enfoque topológico para el descubrimiento de nuevos compuestos líderes candidatos a fármacos, incluso para estudios en los cuales las conformaciones tridimensionales de los compuestos juegan un rol fundamental en la actividad biológica

Machine learning approach to discovery of small molecules with potential inhibitory action against vasoactive metalloproteases

With the advancement of combinatorial chemistry and big data, drug repositioning has boomed. In this sense, machine learning and artificial intelligence techniques offer a priori information to identify the most promising candidates. In this study, we combine QSAR and docking methodologies to identify compounds with potential inhibitory activity of vasoactive metalloproteases for the treatment of cardiovascular diseases. To develop this study, we used a database of 191 thermolysin inhibitor compounds, which is the largest as far as we know. First, we use Dragon's molecular descriptors (0-3D) to develop classification models using Bayesian networks (Naive Bayes) and artificial neural networks (Multilayer Perceptron). The obtained models are used for virtual screening of small molecules in the international DrugBank database. Second, docking experiments are carried out for all three enzymes using the Autodock Vina program, to identify possible interactions with the active site of human metalloproteases. As a result, high-performance artificial intelligence QSAR models are obtained for training and prediction sets. These allowed the identification of 18 compounds with potential inhibitory activity and an adequate oral bioavailability profile, which were evaluated using docking. Four of them showed high binding energies for the three enzymes, and we propose them as potential dual ACE/NEP inhibitors for the control of blood pressure. In summary, the in silico strategies used here constitute an important tool for the early identification of new antihypertensive drug candidates, with substantial savings in time and money. Graphic abstrac

Prediction of aquatic toxicity of benzene derivatives using molecular descriptor from atomic weighted vectors

Several descriptors from atom weighted vectors are used in the prediction of aquatic toxicity o