In silico Analyses of Immune System Protein Interactome Network, Single-Cell RNA Sequencing of Human Tissues, and Artificial Neural Networks Reveal Potential Therapeutic Targets for Drug Repurposing Against COVID-19

Background: There is pressing urgency to identify therapeutic targets and drugs that allow treating COVID-19 patients effectively.Methods: We performed in silico analyses of immune system protein interactome network, single-cell RNA sequencing of human tissues, and artificial neural networks to reveal potential therapeutic targets for drug repurposing against COVID-19.Results: We screened 1,584 high-confidence immune system proteins in ACE2 and TMPRSS2 co-expressing cells, finding 25 potential therapeutic targets significantly overexpressed in nasal goblet secretory cells, lung type II pneumocytes, and ileal absorptive enterocytes of patients with several immunopathologies. Then, we performed fully connected deep neural networks to find the best multitask classification model to predict the activity of 10,672 drugs, obtaining several approved drugs, compounds under investigation, and experimental compounds with the highest area under the receiver operating characteristics.Conclusion: After being effectively analyzed in clinical trials, these drugs can be considered for treatment of severe COVID-19 patients. Scripts can be downloaded at

The dilemma of bacterial expansins evolution. The unusual case of Streptomyces acidiscabies and Kutzneria sp. 744

Expansins are a superfamily of proteins mainly present in plants that are also found in bacteria, fungi and amoebozoa. Expansin proteins bind the plant cells wall and relax the cellulose microfibrils without any enzymatic action. The evolution of this kind of proteins exposes a complex pattern of horizontal gene transferences that makes difficult to determine the precise origin of non-plant expansins. We performed a genome-wide search of inter-domain horizontal gene transfer events using Streptomyces species and found a plant-like expansin in the Streptomyces acidiscabies proteome. This finding leads us to study in deep the origin and the characteristics of this peculiar protein, also present in the species Kutzneria sp.744. Using phylogenetic analyses, we determine that indeed S. acidiscabies and Kutzneria sp.744 expansins are located inside the plants expansins A clade. Using secondary and tertiary structural information, we observed that the electrostatic potentials and the folding of expansins are similar, independently of the proteins’ origin. Using all this information, we conclude that S. acidiscabies and Kutzneria sp.744 expansins have a plant origin but differ from plant and bacterial canonical expansins. This finding suggests that the experimental research around this kind of expansins can be promissory in the future

Prediction of compound-target interaction using several artificial intelligence algorithms and comparison with a consensus-based strategy

Abstract For understanding a chemical compound’s mechanism of action and its side effects, as well as for drug discovery, it is crucial to predict its possible protein targets. This study examines 15 developed target-centric models (TCM) employing different molecular descriptions and machine learning algorithms. They were contrasted with 17 third-party models implemented as web tools (WTCM). In both sets of models, consensus strategies were implemented as potential improvement over individual predictions. The findings indicate that TCM reach f1-score values greater than 0.8. Comparing both approaches, the best TCM achieves values of 0.75, 0.61, 0.25 and 0.38 for true positive/negative rates (TPR, TNR) and false negative/positive rates (FNR, FPR); outperforming the best WTCM. Moreover, the consensus strategy proves to have the most relevant results in the top $$20\%$$ 20 % of target profiles. TCM consensus reach TPR and FNR values of 0.98 and 0; while on WTCM reach values of 0.75 and 0.24. The implemented computational tool with the TCM and their consensus strategy at: https://bioquimio.udla.edu.ec/tidentification01/ . Scientific Contribution: We compare and discuss the performances of 17 public compound-target interaction prediction models and 15 new constructions. We also explore a compound-target interaction prioritization strategy using a consensus approach, and we analyzed the challenging involved in interactions modeling. Graphical Abstrac

GA(M)E-QSAR: A Novel, Fully Automatic Genetic-Algorithm-(Meta)-Ensembles Approach for Binary Classification in Ligand-Based Drug Design

Computer-aided drug design has become an important component of the drug discovery process. Despite the advances in this field, there is not a unique modeling approach that can be successfully applied to solve the whole range of problems faced during QSAR modeling. Feature selection and ensemble modeling are active areas of research in ligand-based drug design. Here we introduce the GA(M)E-QSAR algorithm that combines the search and optimization capabilities of Genetic Algorithms with the simplicity of the Adaboost ensemble-based classification algorithm to solve binary classification problems. We also explore the usefulness of Meta-Ensembles trained with Adaboost and Voting schemes to further improve the accuracy, generalization, and robustness of the optimal Adaboost Single Ensemble derived from the Genetic Algorithm optimization. We evaluated the performance of our algorithm using five data sets from the literature and found that it is capable of yielding similar or better classification results to what has been reported for these data sets with a higher enrichment of active compounds relative to the whole actives subset when only the most active chemicals are considered. More important, we compared our methodology with state of the art feature selection and classification approaches and found that it can provide highly accurate, robust, and generalizable models. In the case of the Adaboost Ensembles derived from the Genetic Algorithm search, the final models are quite simple since they consist of a weighted sum of the output of single feature classifiers. Furthermore, the Adaboost scores can be used as ranking criterion to prioritize chemicals for synthesis and biological evaluation after virtual screening experiments.status: publishe

Gallic acid alkyl esters: Trypanocidal and Leishmanicidal activity, and target identification via modeling studies

Eight gallic acid alkyl esters (1-8) were synthesized via Fischer esterification and evaluated for their trypanocidal and leishmanicidal activity using bloodstream forms of Trypanosoma brucei and promastigotes of Leishmania major. The general cytotoxicity of the esters was evaluated with human HL-60 cells. The compounds displayed moderate to good trypanocidal but zero to low leish-manicidal activity. Gallic acid esters with alkyl chains of three or four carbon atoms in linear arrangement (propyl (4), butyl (5), and isopentyl (6)) were found to be the most trypanocidal compounds with 50% growth inhibition values of ~3 μM. On the other hand, HL-60 cells were less susceptible to the compounds, thus resulting in moderate selectivity indices (ratio of cytotoxic to trypanocidal activity) of >20 for the esters 4-6. Modeling studies combining molecular docking and molecular dynamics simulations suggest that the trypanocidal mechanism of action of gallic acid alkyl esters could be related to the inhibition of the T. brucei alternative oxidase. This suggestion is supported by the observation that trypanosomes became immobile within minutes when incubated with the esters in the presence of glycerol as the sole substrate. These results indicate that gallic acid alkyl esters are interesting compounds to be considered for further antitrypanosomal drug development

Synthetic Cinnamides and Cinnamates: Antimicrobial Activity, Mechanism of Action, and In Silico Study

The severity of infectious diseases associated with the resistance of microorganisms to drugs highlights the importance of investigating bioactive compounds with antimicrobial potential. Therefore, nineteen synthetic cinnamides and cinnamates having a cinnamoyl nucleus were prepared and submitted for the evaluation of antimicrobial activity against pathogenic fungi and bacteria in this study. To determine the minimum inhibitory concentration (MIC) of the compounds, possible mechanisms of antifungal action, and synergistic effects, microdilution testing in broth was used. The structures of the synthesized products were characterized with FTIR spectroscopy, 1 H-NMR, 13 C-NMR, and HRMS. Derivative 6 presented the best antifungal profile, suggesting that the presence of the butyl substituent potentiates its biological response (MIC = 626.62 μM), followed by compound 4 (672.83 μM) and compound 3 (726.36 μM). All three compounds were fungicidal, with MFC/MIC ≤ 4. For mechanism of action, compounds 4 and 6 directly interacted with the ergosterol present in the fungal plasmatic membrane and with the cell wall. Compound 18 presented the best antibacterial profile (MIC = 458.15 μM), followed by compound 9 (550.96 μM) and compound 6 (626.62 μM), which suggested that the presence of an isopropyl group is important for antibacterial activity. The compounds were bactericidal, with MBC/MIC ≤ 4. Association tests were performed using the Checkerboard method to evaluate potential synergistic effects with nystatin (fungi) and amoxicillin (bacteria). Derivatives 6 and 18 presented additive effects. Molecular docking simulations suggested that the most likely targets of compound 6 in C. albicans were caHOS2 and caRPD3, while the most likely target of compound 18 in S. aureus was saFABH. Our results suggest that these compounds could be used as prototypes to obtain new antimicrobial drugs

Critical Review of Plant-Derived Compounds as Possible Inhibitors of SARS-CoV-2 Proteases: A Comparison with Experimentally Validated Molecules

Ever since coronavirus disease 2019 (COVID-19), caused by SARS-CoV-2, was declared a pandemic on March 11, 2020, by the WHO, a concerted effort has been made to find compounds capable of acting on the virus and preventing its replication. In this context, researchers have refocused part of their attention on certain natural compounds that have shown promising effects on the virus. Considering the importance of this topic in the current context, this study aimed to present a critical review and analysis of the main reports of plant-derived compounds as possible inhibitors of the two SARS-CoV-2 proteases: main protease (Mpro) and Papain-like protease (PLpro). From the search in the PubMed database, a total of 165 published articles were found that met the search patterns. A total of 590 unique molecules were identified from a total of 122 articles as potential protease inhibitors. At the same time, 114 molecules reported as natural products and with annotation of theoretical support and antiviral effects were extracted from the COVID-19 Help database. After combining the molecules extracted from articles and those obtained from the database, we identified 648 unique molecules predicted as potential inhibitors of Mpro and/or PLpro. According to our results, several of the predicted compounds with higher theoretical confidence are present in many plants used in traditional medicine and even food, such as flavonoids, carboxylic acids, phenolic acids, triterpenes, terpenes phytosterols, and triterpenoids. These are potential inhibitors of Mpro and PLpro. Although the predictions of several molecules against SARS-CoV-2 are promising, little experimental information was found regarding certain families of compounds. Only 45 out of the 648 unique molecules have experimental data validating them as inhibitors of Mpro or PLpro, with the most frequent scaffold present in these 45 compounds being the flavone. The novelty of this work lies in the analysis of the structural diversity of the chemical space among the molecules predicted as inhibitors of SARS-CoV-2 Mpro and PLpro proteases and the comparison to those molecules experimentally validated. This work emphasizes the need for experimental validation of certain families of compounds, preferentially combining classical enzymatic assays with interaction-based methods. Furthermore, we recommend checking the presence of Pan-Assay Interference Compounds (PAINS) and the presence of molecules previously reported as inhibitors of Mpro or PLpro to optimize resources and time in the discovery of new SARS-CoV-2 antivirals from plant-derived molecules

Synthesis of Coumarin and Homoisoflavonoid Derivatives and Analogs: The Search for New Antifungal Agents

A set of twenty-four synthetic derivatives, with coumarin and homoisoflavonoid cores and structural analogs, were submitted for evaluation of antifungal activity against various species of Candida. The broth microdilution test was used to determine the Minimum Inhibitory Concentration (MIC) of the compounds and to verify the possible antifungal action mechanisms. The synthetic derivatives were obtained using various reaction methods, and six new compounds were obtained. The structures of the synthesized products were characterized by FTIR spectroscopy: 1H-NMR, 13C-NMR, and HRMS. The coumarin derivative 8 presented the best antifungal profile, suggesting that the pentyloxy substituent at the C-7 position of coumarin ring could potentiate the bioactivity. Compound 8 was then evaluated against the biofilm of C. tropicalis ATCC 13803, which showed a statistically significant reduction in biofilm at concentrations of 0.268 µmol/mL and 0.067 µmol/mL, when compared to the growth control group. For a better understanding of their antifungal activity, compounds 8 and 21 were submitted to a study of the mode of action on the fungal cell wall and plasma membrane. It was observed that neither compound interacted directly with ergosterol present in the fungal plasma membrane or with the fungal cell wall. This suggests that their bioactivity was due to interaction involving other pharmacological targets. Compound 8 was also subjected to a molecular modeling study, which showed that its antifungal action mechanism occurred mainly through interference in the redox balance of the fungal cell, and by compromising the plasma membrane; not by direct interaction, but by interference in ergosterol synthesis. Another important finding was the antifungal capacity of homoisoflavonoids 23 and 24. Derivative 23 presented slightly higher antifungal activity, possibly due to the presence of the methoxyl substituent in the meta position in ring B

Quinolinecarboxamides Inhibit the Replication of the Bovine Viral Diarrhea Virus by Targeting a Hot Spot for the Inhibition of Pestivirus Replication in the RNA-Dependent RNA Polymerase

The bovine viral diarrhea virus (BVDV), a pestivirus from the family of Flaviviridae is ubiquitous and causes a range of clinical manifestations in livestock, mainly cattle. Two quinolinecarboxamide analogues were identified in a CPE-based screening effort, as selective inhibitors of the in vitro bovine viral diarrhea virus (BVDV) replication, i.e., TO505-6180/CSFCI (average EC50 = 0.07 µM, SD = 0.02 µM, CC50 > 100 µM) and TO502-2403/CSFCII (average EC50 = 0.2 µM, SD = 0.06 µM, CC50 > 100 µM). The initial antiviral activity observed for both hits against BVDV was corroborated by measuring the inhibitory effect on viral RNA synthesis and the production of infectious virus. Modification of the substituents on the quinolinecarboxamide scaffold resulted in analogues that proved about 7-fold more potent (average EC50 = 0.03 with a SD = 0.01 µM) and that were devoid of cellular toxicity, for the concentration range tested (SI = 3333). CSFCII resistant BVDV variants were selected and were found to carry the F224P mutation in the viral RNA-dependent RNA polymerase (RdRp), whereas CSFCI resistant BVDV carried two mutations in the same region of the RdRp, i.e., N264D and F224Y. Likewise, molecular modeling revealed that F224P/Y and N264D are located in a small cavity near the fingertip domain of the pestivirus polymerase. CSFC-resistant BVDV proved to be cross-resistant to earlier reported pestivirus inhibitors (BPIP, AG110, LZ37, and BBP) that are known to target the same region of the RdRp. CSFC analogues did not inhibit the in vitro activity of recombinant BVDV RdRp but inhibited the activity of BVDV replication complexes (RCs). CSFC analogues likely interact with the fingertip of the pestivirus RdRp at the same position as BPIP, AG110, LZ37, and BBP. This indicates that this region is a "hot spot" for the inhibition of pestivirus replication.status: publishe

Degradation of PET Bottles by an Engineered <i>Ideonella sakaiensis</i> PETase

Extensive plastic production has become a serious environmental and health problem due to the lack of efficient treatment of plastic waste. Polyethylene terephthalate (PET) is one of the most used polymers and is accumulating in landfills or elsewhere in nature at alarming rates. In recent years, enzymatic degradation of PET by Ideonella sakaiensis PETase (IsPETase), a cutinase-like enzyme, has emerged as a promising strategy to completely depolymerize this polymer into its building blocks. Here, inspired by the architecture of cutinases and lipases homologous to IsPETase and using 3D structure information of the enzyme, we rationally designed three mutations in IsPETase active site for enhancing its PET-degrading activity. In particular, the S238Y mutant, located nearby the catalytic triad, showed a degradation activity increased by 3.3-fold in comparison to the wild-type enzyme. Importantly, this structural modification favoured the function of the enzyme in breaking down highly crystallized (~31%) PET, which is found in commercial soft drink bottles. In addition, microscopical analysis of enzyme-treated PET samples showed that IsPETase acts better when the smooth surface of highly crystalline PET is altered by mechanical stress. These results represent important progress in the accomplishment of a sustainable and complete degradation of PET pollution