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Peptides are promising drug development frameworks that have been hindered by intrinsic undesired properties including hemolytic activity. We aim to get a better insight into the chemical space of hemolytic peptides using a novel approach based on network science and data mining. Metadata networks (METNs) were useful to characterize and find general patterns associated with hemolytic peptides, whereas Half-Space Proximal Networks (HSPNs), represented the hemolytic peptide space. The best candidate HSPNs were used to extract various subsets of hemolytic peptides (scaffolds) considering network centrality and peptide similarity. These scaffolds have been proved to be useful in developing robust similarity-based model classifiers. Finally, using an alignment-free approach, we reported 47 putative hemolytic motifs, which can be used as toxic signatures when developing novel peptide-based drugs. We provided evidence that the number of hemolytic motifs in a sequence might be related to the likelihood of being hemolytic., (© 2024. The Author(s).)

Indazoles have previously been identified as molecules with antiprotozoal activity. In this study, we evaluate the in vitro activity of thirteen 3-alkoxy-1-benzyl-5-nitroindazole derivatives (series D) against L. amazonensis , L. infantum, and L. mexicana . In vitro, cytotoxicity against mouse peritoneal macrophages and growth inhibitory activity in promastigotes were evaluated for all compounds, and those showing adequate activity and selectivity were tested against intracellular amastigotes. Transmission and scanning electron microscopy were employed to study the effects of 3-alkoxy-1-benzyl-5-nitroindazole and 2-benzyl-5-nitroindazolin-3-one derivatives on promastigotes of L. amazonensis . Compounds NV6 and NV8 were active in the two life stages of the three species, with the latter showing the best indicators of activity and selectivity. 3-alkoxy-1-benzyl-5-nitroindazole derivatives (series D) showed in vitro activity comparable to that of amphotericin B against the promastigote stage of Leishmania spp. Two compounds were also found to be active the amastigote stage. Electron microscopy studies confirmed the antileishmanial activity of the indazole derivatives studied and support future research on this family of compounds as antileishmanial agents.

The desirable pharmacological properties and a broad number of therapeutic activities have made peptides promising drugs over small organic molecules and antibody drugs. Nevertheless, toxic effects, such as hemolysis, have hampered the development of such promising drugs. Hence, a reliable computational tool to predict peptide hemolytic toxicity is enormously useful before synthesis and experimental evaluation. Currently, four web servers that predict hemolytic activity using machine learning (ML) algorithms are available; however, they exhibit some limitations, such as the need for a reliable negative set and limited application domain. Hence, we developed a robust model based on a novel theoretical approach that combines network science and a multiquery similarity searching (MQSS) method. A total of 1152 initial models were constructed from 144 scaffolds generated in a previous report. These were evaluated on external data sets, and the best models were fused and improved. Our best MQSS model I1 outperformed all state-of-the-art ML-based models and was used to characterize the prevalence of hemolytic toxicity on therapeutic peptides. Based on our model's estimation, the number of hemolytic peptides might be 3.9-fold higher than the reported.

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 % 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., (© 2024. The Author(s).)

The objective of this study was to evaluate the antifungal activity of free methyl 3,5 dinitrobenzoate (MDNB) and its nanoemulsion (MDNB-NE) against strains of Candida albicans. Additionally, a molecular modeling study was also carried out to propose the mechanism of action and toxicity of MDNB. These results demonstrated the MDNB-NE presented a droplet size of 181.16 ± 3.20 nm and polydispersity index of 0.30 ± 0.03. MDNB and MDNB-NE inhibited the growth of all strains with minimum inhibitory concentrations of 0.27-1.10 mM. The biological results corroborated the molecular model, which pointed to a multi-target antifungal mechanism of action for MDNB in C. albicans. The study could serve as a basis for further research involving compounds with nitro groups with antifungal., (© 2023. The Author(s) under exclusive licence to Sociedade Brasileira de Microbiologia.)

Notwithstanding the wide adoption of the OECD principles (or best practices) for QSAR modeling, disparities between in silico predictions and experimental results are frequent, suggesting that model predictions are often too optimistic. Of these OECD principles, the applicability domain (AD) estimation has been recognized in several reports in the literature to be one of the most challenging, implying that the actual reliability measures of model predictions are often unreliable. Applying tree-based error analysis workflows on 5 QSAR models reported in the literature and available in the QsarDB repository, i.e., androgen receptor bioactivity (agonists, antagonists, and binders, respectively) and membrane permeability (highest membrane permeability and the intrinsic permeability), we demonstrate that predictions erroneously tagged as reliable (AD prediction errors) overwhelmingly correspond to instances in subspaces (cohorts) with the highest prediction error rates, highlighting the inhomogeneity of the AD space. In this sense, we call for more stringent AD analysis guidelines which require the incorporation of model error analysis schemes, to provide critical insight on the reliability of underlying AD algorithms. Additionally, any selected AD method should be rigorously validated to demonstrate its suitability for the model space over which it is applied. These steps will ultimately contribute to more accurate estimations of the reliability of model predictions. Finally, error analysis may also be useful in "rational" model refinement in that data expansion efforts and model retraining are focused on cohorts with the highest error rates., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Background: Currently, there is no safe and effective vaccine against leishmaniasis and existing therapies are inadequate due to high toxicity, cost and decreased efficacy caused by the emergence of resistant parasite strains. Some indazole derivatives have shown in vitro and in vivo activity against Trichomonas vaginalis and Trypanosoma cruzi . On that basis, 20 indazole derivatives were tested in vitro against Leishmania amazonensis ., Objective: To evaluate the in vitro activity of twenty 2-benzyl-5-nitroindazolin-3-one derivatives against L. amazonensis ., Design: For the selection of promising compounds, it is necessary to evaluate the indicators for in vitro activity. For this aim, a battery of studies for antileishmanial activity and cytotoxicity were implemented. These results enabled the determination of the substituents in the indazole derivatives responsible for activity and selectivity, through the analysis of the structure-activity relationship (SAR)., Methods: In vitro cytotoxicity against mouse peritoneal macrophages and growth inhibitory activity in promastigotes were evaluated for 20 compounds. Compounds that showed adequate selectivity were tested against intracellular amastigotes. The SAR from the results in promastigotes was represented using the SARANEA software., Results: Eight compounds showed selectivity index >10% and 50% inhibitory concentration <1 µM against the promastigote stage. Against intracellular amastigotes, four were as active as Amphotericin B. The best results were obtained for 2-(benzyl-2,3-dihydro-5-nitro-3-oxoindazol-1-yl) ethyl acetate, with 50% inhibitory concentration of 0.46 ± 0.01 µM against amastigotes and a selectivity index of 875. The SAR study showed the positive effect on the selectivity of the hydrophilic fragments substituted in position 1 of 2-benzyl-5- nitroindazolin-3-one, which played a key role in improving the selectivity profile of this series of compounds., Conclusion: 2-bencyl-5-nitroindazolin-3-one derivatives showed selective and potent in vitro activity, supporting further investigations on this family of compounds as potential antileishmanial hits., Competing Interests: The authors declare that there is no conflict of interest., (© The Author(s), 2023.)

The racemization of biomolecules in the active site can reduce the biological activity of drugs, and the mechanism involved in this process is still not fully comprehended. The present study investigates the impact of aromaticity on racemization using advanced theoretical techniques based on density functional theory. Calculations were performed at the ωb97xd/6-311++g(d,p) level of theory. A compelling explanation for the observed aromatic stabilization via resonance is put forward, involving a carbanion intermediate. The analysis, employing Hammett's parameters, convincingly supports the presence of a negative charge within the transition state of aromatic compounds. Moreover, the combined utilization of natural bond orbital (NBO) analysis and intrinsic reaction coordinate (IRC) calculations confirms the pronounced stabilization of electron distribution within the carbanion intermediate. To enhance our understanding of the racemization process, a thorough examination of the evolution of NBO charges and Wiberg bond indices (WBIs) at all points along the IRC profile is performed. This approach offers valuable insights into the synchronicity parameters governing the racemization reactions.

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 ( Is PETase), 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 Is PETase and using 3D structure information of the enzyme, we rationally designed three mutations in Is PETase 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 Is PETase 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.

Leishmania infantum is the etiological agent of visceral leishmaniasis (VL) in South America, the Mediterranean basin, and West and Central Asia. The most affected country, Brazil, reported 4297 VL cases in 2017. L. infantum is transmitted by female phlebotomine sand flies during successive blood meals. There are no validated vaccines to prevent the infection and the treatment relies on drugs that often present severe side effects, which justify the efforts to find new antileishmanial drugs. Cinnamic acid derivatives have shown several pharmacological activities, including antiparasitic action. Therefore, in the present study, the biological evaluation of cinnamic acid and thirty-four derivatives against L. infantum is reported. The compounds were prepared by several synthesis methods and characterized by spectroscopic techniques and high-resolution mass spectrometry. The results revealed that compound 32 (N-(4-isopropylbenzyl)cinnamamide) was the most potent antileishmanial agent (IC 50 = 33.71 μM) with the highest selectivity index (SI > 42.46), followed by compound 15 (piperonyl cinnamate) with an IC 50 = 42.80 μM and SI > 32.86. Compound 32 was slightly less potent and nineteen times more selective for the parasite than amphotericin B (MIC = 3.14 uM; SI = 2.24). In the molecular docking study, the most likely target for the compound in L. infantum was aspartyl aminopeptidase, followed by aldehyde dehydrogenase, mitochondrial. The data obtained show the antileishmanial potential of this class of compounds and may be used in the search for new drug candidates against Leishmania species.

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.

Cancer is a principal cause of death in the world, and providing a better quality of life and reducing mortality through effective pharmacological treatment remains a challenge. Among malignant tumor types, squamous cell carcinoma-esophageal cancer (EC) is usually located in the mouth, with approximately 90% located mainly on the tongue and floor of the mouth. Piplartine is an alkamide found in certain species of the genus Piper and presents many pharmacological properties including antitumor activity. In the present study, the cytotoxic potential of a collection of piplartine analogs against human oral SCC9 carcinoma cells was evaluated. The analogs were prepared via Fischer esterification reactions, alkyl and aryl halide esterification, and a coupling reaction with PyBOP using the natural compound 3,4,5-trimethoxybenzoic acid as a starting material. The products were structurally characterized using 1 H and 13 C nuclear magnetic resonance, infrared spectroscopy, and high-resolution mass spectrometry for the unpublished compounds. The compound 4-methoxy-benzyl 3,4,5-trimethoxybenzoate ( 9 ) presented an IC 50 of 46.21 µM, high selectively (SI > 16), and caused apoptosis in SCC9 cancer cells. The molecular modeling study suggested a multi-target mechanism of action for the antitumor activity of compound 9 with CRM1 as the main target receptor.

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., Competing Interests: The authors declare no competing financial interest., (© 2022 The Authors. Published by American Chemical Society.)

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 leishmanicidal 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.

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: 1 H-NMR, 13 C-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.

Ocimum basilicum and Ocimum tenuiflorum are two basil species widely used medicinally as an anti-inflammatory, antimicrobial and cardioprotective agent. This study focuses on the chemical characterization of the majoritarian compounds of both species and their anti-inflammatory potential. Up to 22 compounds such as various types of salvianolic acids, derivatives of rosmaniric acid and flavones were identified in both plants. The identified compounds were very similar between both plants and are consistent with previous finding in other studies in Portugal and Italy. Based on the identified molecules a consensus target prediction was carried out. Among the main predicted target proteins, we found a high representation of the carbonic anhydrase family (CA2, CA7 and CA12) and several key proteins from the arachidonic pathway (LOX5, PLA2, COX1 and COX2). Both pathways are well related to inflammation. The interaction between the compounds and these targets were explored through molecular docking and molecular dynamics simulation. Our results suggest that some molecules present in both plants can induce an anti-inflammatory response through a non-steroidal mechanism of action connected to the carbon dioxide metabolism., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Fungal infections remain a high-incidence worldwide health problem that is aggravated by limited therapeutic options and the emergence of drug-resistant strains. Cinnamic and benzoic acid amides have previously shown bioactivity against different species belonging to the Candida genus. Here, 20 cinnamic and benzoic acid amides were synthesized and tested for inhibition of C. krusei ATCC 14243 and C. parapsilosis ATCC 22019. Five compounds inhibited the Candida strains tested, with compound 16 (MIC = 7.8 µg/mL) producing stronger antifungal activity than fluconazole (MIC = 16 µg/mL) against C. krusei ATCC 14243. It was also tested against eight Candida strains, including five clinical strains resistant to fluconazole, and showed an inhibitory effect against all strains tested (MIC = 85.3-341.3 µg/mL). The MIC value against C. krusei ATCC 6258 was 85.3 mcg/mL, while against C. krusei ATCC 14243, it was 10.9 times smaller. This strain had greater sensitivity to the antifungal action of compound 16 . The inhibition of C. krusei ATCC 14243 and C. parapsilosis ATCC 22019 was also achieved by compounds 2 , 9 , 12 , 14 and 15 . Computational experiments combining target fishing, molecular docking and molecular dynamics simulations were performed to study the potential mechanism of action of compound 16 against C. krusei . From these, a multi-target mechanism of action is proposed for this compound that involves proteins related to critical cellular processes such as the redox balance, kinases-mediated signaling, protein folding and cell wall synthesis. The modeling results might guide future experiments focusing on the wet-lab investigation of the mechanism of action of this series of compounds, as well as on the optimization of their inhibitory potency.

Amides derived from ferulic acid have a wide spectrum of pharmacological activities, including antitumor and antifungal activity. In the present study, a series of ten amides were obtained by coupling reactions using the reagents (benzotriazol-1-yloxy) tripyrrolidinophosphonium hexafluorophosphate (PyBOP) and N,N ' - dicyclohexylcarbodiimide (DCC). All the compounds were identified on the basis of their IR, 1 H- and 13 C-NMR, HRMS data, and with yields ranging from 43.17% to 91.37%. The compounds were subjected to cytotoxic tests by the alamar blue technique and antifungal screening by the broth microdilution method to determine the minimum inhibitory concentration (MIC). The amides 10 and 11 displayed the best result in both biological evaluations, and compound 10 was the most potent and selective in HL-60 cancer cells, with no cytotoxicity on healthy cells. This amide had antifungal activity in all strains and had the lowest MIC against Candida albicans and Candida tropicalis . The possible mechanism of antifungal action occurs via the fungal cell wall. Molecular modeling suggested that compounds 10 and 11 interact with the enzymes GWT1 and GSC1, which are essential for the development of C. albicans . The findings of the present study demonstrated that compounds 10 and 11 may be used as a platform in drug development in the future., Competing Interests: The authors declare no conflicts of interest., (Copyright © 2021 Mayara Castro de Morais et al.)

Background: There is currently no effective dengue virus (DENV) therapeutic. We aim to develop a genetic algorithm-based framework for the design of peptides with possible DENV inhibitory activity. Methods & results: A Python-based tool (denominated AutoPepGEN) based on a DENV support vector machine classifier as the objective function was implemented. AutoPepGEN was applied to the design of three- to seven-amino acid sequences and ten peptides were selected. Peptide-protease (DENV) docking and Molecular Mechanics-Generalized Born Surface Area calculations were performed for the selected sequences and favorable binding energies were observed. Conclusion: It is hoped that AutoPepGEN will serve as an in silico alternative to the experimental design of positional scanning combinatorial libraries, known to be prone to a combinatorial explosion. AutoPepGEN is available at: https://github.com/sjbarigye/AutoPepGEN.

The coronavirus disease 2019 (COVID-19) pandemic is caused by a novel coronavirus; the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). Millions of cases and deaths to date have resulted in a global challenge for healthcare systems. COVID-19 has a high mortality rate, especially in elderly individuals with pre-existing chronic comorbidities. There are currently no effective therapeutic approaches for the prevention and treatment of COVID-19. Therefore, the identification of effective therapeutics is a necessity. Terpenes are the largest class of natural products that could serve as a source of new drugs or as prototypes for the development of effective pharmacotherapeutic agents. In the present study, we discuss the antiviral activity of these natural products and we perform simulations against the M pro and PL pro enzymes of SARS-CoV-2. Our results strongly suggest the potential of these compounds against human coronaviruses, including SARS-CoV-2.

In the present report we evaluate the possible utility of the Generative Adversarial Networks (GANs) in mapping the chemical structural space for molecular property profiles, with the goal of subsequently yielding synthetic (artificial) samples for ligand-based molecular modeling. Two case studies are considered: BACE-1 (β-Secretase 1) and DENV (Dengue Virus) inhibitory activities, with the former focused on data populating and the latter on data balancing tasks. We train GANs using subsamples extracted from datasets for each bioactivity endpoint, and apply the trained networks in generating synthetic examples from the respective bioactivity chemical spaces. Original and synthetic samples are pooled together and employed to build BACE-1 and DENV inhibitory activity classifiers and their performance evaluated over tenfold external validation sets. In both case studies, the obtained classifiers demonstrate satisfactory predictivity with the former yielding accuracy (ACC) and Mathew's correlation coefficient (MCC) values of 0.80 and 0.59, while the latter produces balanced accuracy(BACC) and MCC values of 0.81 and 0.70, respectively. Moreover, the statistics of these classifiers are compared with those of other models in the literature demonstrating comparable to better performance. These results suggest that GANs may be useful in mapping the chemical space for molecular property profiles of interest, and thus allow for the extraction of synthetic examples for computational modeling., (© 2020 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Horizontal gene transfer (HGT) plays an important role for evolutionary innovations within prokaryotic communities and is a crucial event for their survival. Several computational approaches have arisen to identify HGT events in recipient genomes. However, this has been proven to be a complex task due to the generation of a great number of false positives and the prediction disagreement among the existing methods. Phylogenetic reconstruction methods turned out to be the most reliable ones, but they are not extensible to all genes/species and are computationally demanding when dealing with large datasets. In contrast, the so-called surrogate methods that use heuristic solutions either based on nucleotide composition patterns or phyletic distribution of BLAST hits can be applied easily to the genomic scale, but they fail in identifying common HGT events. Here, we present ShadowCaster, a hybrid approach that sequentially combines nucleotide composition-based predictions by support vector machines (SVMs) under the shadow of phylogenetic models independent of tree reconstruction, to improve the detection of HGT events in prokaryotes. ShadowCaster successfully predicted close and distant HGT events in both artificial and bacterial genomes. ShadowCaster detected HGT related to heavy metal resistance in the genome of Rhodanobacter denitrificans with higher accuracy than the most popular state-of-the-art computational approaches, encompassing most of the predicted cases made by other methods. ShadowCaster is released at the GitHub platform as an open-source software under the GPLv3 license.

Over the last decade, there has been a dramatic increase in the prevalence and gravity of systemic fungal diseases. This study aimed therefore at evaluating the antifungal potential of ester derivatives of benzoic and cinnamic acids from three Candida species. The compounds were prepared via Fischer esterification, and the antifungal assay was performed by the microdilution method in 96-well microplates for determining the minimal inhibitory concentrations (MICs). The findings of the antifungal tests revealed that the analogue compound methyl ferulate, methyl o -coumarate, and methyl biphenyl-3-carboxylate displayed an interesting antifungal activity against all Candida strains tested, with MIC values of 31.25-62.5, 62.5-125, and 62.5  μ g/ml, respectively. A preliminary Structure-Activity Relationship study of benzoic and cinnamic acid derivatives has led to the recognition of some important structural requirements for antifungal activity. The results of molecular docking indicate that the presence of the enoate moiety along with hydroxyl and one methoxy substitution in the phenyl ring has a positive effect on the bioactivity of compound 7 against Candida albicans . These observations further support the hypothesis that the antifungal activity of compound 7 could be due to its binding to multiple targets, specifically to QR, TS, and ST-PK. Additional experiments are required in the future to test this hypothesis and to propose novel compounds with improved antifungal activity., Competing Interests: No potential conflict of interest was reported by the authors., (Copyright © 2020 Yunierkis Perez-Castillo et al.)

The emergence of SARS-CoV-2 has resulted in nearly 1,280,000 infections and 73,000 deaths globally so far. This novel virus acquired the ability to infect human cells using the SARS-CoV cell receptor hACE2. Because of this, it is essential to improve our understanding of the evolutionary dynamics surrounding the SARS-CoV-2 hACE2 interaction. One way theory predicts selection pressures should shape viral evolution is to enhance binding with host cells. We first assessed evolutionary dynamics in select betacoronavirus spike protein genes to predict whether these genomic regions are under directional or purifying selection between divergent viral lineages, at various scales of relatedness. With this analysis, we determine a region inside the receptor-binding domain with putative sites under positive selection interspersed among highly conserved sites, which are implicated in structural stability of the viral spike protein and its union with human receptor ACE2. Next, to gain further insights into factors associated with recognition of the human host receptor, we performed modeling studies of five different betacoronaviruses and their potential binding to hACE2. Modeling results indicate that interfering with the salt bridges at hot spot 353 could be an effective strategy for inhibiting binding, and hence for the prevention of SARS-CoV-2 infections. We also propose that a glycine residue at the receptor-binding domain of the spike glycoprotein can have a critical role in permitting bat SARS-related coronaviruses to infect human cells., Competing Interests: None declared., (© 2020 The Authors. Evolutionary Applications published by John Wiley & Sons Ltd.)

Novel 3D protein descriptors based on bilinear, quadratic and linear algebraic maps in R n are proposed. The latter employs the k th 2-tuple (dis) similarity matrix to codify information related to covalent and non-covalent interactions in these biopolymers. The calculation of the inter-amino acid distances is generalized by using several dis-similarity coefficients, where normalization procedures based on the simple stochastic and mutual probability schemes are applied. A new local-fragment approach based on amino acid-types and amino acid-groups is proposed to characterize regions of interest in proteins. Topological and geometric macromolecular cutoffs are defined using local and total indices to highlight non-covalent interactions existing between the side-chains of each amino acid. Moreover, local and total indices calculations are generalized considering a LEGO approach, by using several aggregation operators. Collinearity and variability analyses are performed to evaluate every generalizing component applied to the definition of these novel indices. These experiments are oriented to reduce the number of MDs obtained for performing prediction models. The predictive power of the proposed indices was evaluated using two benchmark datasets, folding rate and secondary structural classification of proteins. The proposed MDs are modeled using the following strategies: Multiple Linear Regression (MLR) and Support Vector Machine (SVM), respectively. The best regression model developed for the folding rate of proteins yields a cross-validation coefficient of 0.875 (Test Set) and the best model developed for secondary structural classification obtained 98% of instances correctly classified (Test Set). These statistical parameters are superior to the ones obtained with existing MDs reported in the literature. Overall, the new theoretical generalization enhanced the information extraction into the MDs, allowing a better correlation between these two evaluated benchmark datasets and the proposed indices. The optimal theoretical configurations defined for the calculation of these MDs consider low collinearity and less information redundancy among them. These theoretical configurations and the software are available at http://tomocomd.com/mulims-mcompas., (Copyright © 2019. Published by Elsevier Ltd.)

Consensus scoring has become a commonly used strategy within structure-based virtual screening (VS) workflows with improved performance compared to those based in a single scoring function. However, no research has been devoted to analyze the worth of docking scoring functions components in consensus scoring. We implemented and tested a method that incorporates docking scoring functions components into the setting of high performance VS workflows. This method uses genetic algorithms for finding the combination of scoring components that maximizes the VS enrichment for any target. Our methodology was validated using a data set including ligands and decoys for 102 targets that have been widely used in VS validation studies. Results show that our approach outperforms other methods for all targets. It also boosts the initial enrichment performance of the traditional use of whole scoring functions in consensus scoring by an average of 45%. Our methodology showed to be outstandingly predictive when challenged to rescore external (previously unseen) data. Remarkably, CompScore was able not only to retain its performance after redocking with a different software, but also proved that the enrichment obtained was not artificial. CompScore is freely available at: http://bioquimio.udla.edu.ec/compscore/ .

Piplartine (PPL), also known as piperlongumine, is a biologically active alkaloid extracted from the Piper genus which has been found to have highly effective anticancer activity against several tumor cell lines. This study investigates in detail the antitumoral potential of a PPL analogue; ( E )-N-(4-fluorobenzyl)-3-(3,4,5-trimethoxyphenyl) acrylamide (NFBTA). The anticancer potential of NFBTA on the glioblastoma multiforme (GBM) cell line (U87MG) was determined by 3-(4,5-dimethyl-2-thia-zolyl)-2, 5-diphenyl-2H-tetrazolium bromide (MTT), and lactate dehydrogenase (LDH) release analysis, and the selectivity index (SI) was calculated. To detect cell apoptosis, fluorescent staining via flow cytometry and Hoechst 33258 staining were performed. Oxidative alterations were assessed via colorimetric measurement methods. Alterations in expressions of key genes related to carcinogenesis were determined. Additionally, in terms of NFBTA cytotoxic, oxidative, and genotoxic damage potential, the biosafety of this novel agent was evaluated in cultured human whole blood cells. Cell viability analyses revealed that NFBTA exhibited strong cytotoxic activity in cultured U87MG cells, with high selectivity and inhibitory activity in apoptotic processes, as well as potential for altering the principal molecular genetic responses in U87MG cell growth. Molecular docking studies strongly suggested a plausible anti-proliferative mechanism for NBFTA. The results of the experimental in vitro human glioblastoma model and computational approach revealed promising cytotoxic activity for NFBTA, helping to orient further studies evaluating its antitumor profile for safe and effective therapeutic applications.

Streptomyces clavuligerus is a Gram-positive bacterium that is a high producer of secondary metabolites with industrial applications. The production of antibiotics such as clavulanic acid or cephamycin has been extensively studied in this species; nevertheless, other aspects, such as evolution or ecology, have received less attention. Furthermore, genes that arise from ancient events of lateral transfer have been demonstrated to be implicated in important functions of host species. This approximation discovered relevant genes that genomic analyses overlooked. Thus, we studied the impact of horizontal gene transfer in the S. clavuligerus genome. To perform this task, we applied whole-genome analysis to identify a laterally transferred sequence from different domains. The most relevant result was a putative antimicrobial peptide (AMP) with a clear origin in the Hymenoptera order of insects. Next, we determined that two copies of these genes were present in the megaplasmid pSCL4 but absent in the S. clavuligerus ATCC 27064 chromosome. Additionally, we found that these sequences were exclusive to the ATCC 27064 strain (and so were not present in any other bacteria) and we also verified the expression of the genes using RNAseq data. Next, we used several AMP predictors to validate the original annotation extracted from Hymenoptera sequences and explored the possibility that these proteins had post-translational modifications using peptidase cleavage prediction. We suggest that Hymenoptera AMP-like proteins of S. clavuligerus ATCC 27064 may be useful for both species adaptation and as an antimicrobial molecule with industrial applications., Competing Interests: None declared.

Background: Malaria or Paludism is a tropical disease caused by parasites of the Plasmodium genre and transmitted to humans through the bite of infected mosquitos of the Anopheles genre. This pathology is considered one of the first causes of death in tropical countries and, despite several existing therapies, they have a high toxicity. Computational methods based on Quantitative Structure- Activity Relationship studies have been widely used in drug design work flows., Objective: The main goal of the current research is to develop computational models for the identification of antimalarial hit compounds., Materials and Methods: For this, a data set suitable for the modeling of the antimalarial activity of chemical compounds was compiled from the literature and subjected to a thorough curation process. In addition, the performance of a diverse set of ensemble-based classification methodologies was evaluated and one of these ensembles was selected as the most suitable for the identification of antimalarial hits based on its virtual screening performance. Data curation was conducted to minimize noise. Among the explored ensemble-based methods, the one combining Genetic Algorithms for the selection of the base classifiers and Majority Vote for their aggregation showed the best performance., Results: Our results also show that ensemble modeling is an effective strategy for the QSAR modeling of highly heterogeneous datasets in the discovery of potential antimalarial compounds., Conclusion: It was determined that the best performing ensembles were those that use Genetic Algorithms as a method of selection of base models and Majority Vote as the aggregation method., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

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.

Gastric cancer is the third leading cause of cancer-related mortality worldwide and despite advances in prevention, diagnosis and therapy, it is still regarded as a global health concern. The efficacy of the therapies for gastric cancer is limited by a poor response to currently available therapeutic regimens. One of the reasons that may explain these poor clinical outcomes is the highly heterogeneous nature of this disease. In this sense, it is essential to discover new molecular agents capable of targeting various gastric cancer subtypes simultaneously. Here, we present a multi-objective approach for the ligand-based virtual screening discovery of chemical compounds simultaneously active against the gastric cancer cell lines AGS, NCI-N87 and SNU-1. The proposed approach relays in a novel methodology based on the development of ensemble models for the bioactivity prediction against each individual gastric cancer cell line. The methodology includes the aggregation of one ensemble per cell line using a desirability-based algorithm into virtual screening protocols. Our research leads to the proposal of a multi-targeted virtual screening protocol able to achieve high enrichment of known chemicals with anti-gastric cancer activity. Specifically, our results indicate that, using the proposed protocol, it is possible to retrieve almost 20 more times multi-targeted compounds in the first 1% of the ranked list than what is expected from a uniform distribution of the active ones in the virtual screening database. More importantly, the proposed protocol attains an outstanding initial enrichment of known multi-targeted anti-gastric cancer agents.

Background: Virtual methodologies have become essential components of the drug discovery pipeline. Specifically, structure-based drug design methodologies exploit the 3D structure of molecular targets to discover new drug candidates through molecular docking. Recently, dual target ligands of the Adenosine A2A Receptor and Monoamine Oxidase B enzyme have been proposed as effective therapies for the treatment of Parkinson's disease., Methods: In this paper we propose a structure-based methodology, which is extensively validated, for the discovery of dual Adenosine A2A Receptor/Monoamine Oxidase B ligands. This methodology involves molecular docking studies against both receptors and the evaluation of different scoring functions fusion strategies for maximizing the initial virtual screening enrichment of known dual ligands., Results: The developed methodology provides high values of enrichment of known ligands, which outperform that of the individual scoring functions. At the same time, the obtained ensemble can be translated in a sequence of steps that should be followed to maximize the enrichment of dual target Adenosine A2A Receptor antagonists and Monoamine Oxidase B inhibitors., Conclusion: Information relative to docking scores to both targets have to be combined for achieving high dual ligands enrichment. Combining the rankings derived from different scoring functions proved to be a valuable strategy for improving the enrichment relative to single scoring function in virtual screening experiments., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.)

Background: In the context of the current drug discovery efforts to find disease modifying therapies for Parkinson's disease (PD) the current single target strategy has proved inefficient. Consequently, the search for multi-potent agents is attracting more and more attention due to the multiple pathogenetic factors implicated in PD. Multiple evidences points to the dual inhibition of the monoamine oxidase B (MAO-B), as well as adenosine A2A receptor (A2AAR) blockade, as a promising approach to prevent the neurodegeneration involved in PD. Currently, only two chemical scaffolds has been proposed as potential dual MAO-B inhibitors/A2AAR antagonists (caffeine derivatives and benzothiazinones)., Methods: In this study, we conduct a series of chemoinformatics analysis in order to evaluate and advance the potential of the chromone nucleus as a MAO-B/A2AAR dual binding scaffold., Results: The information provided by SAR data mining analysis based on network similarity graphs and molecular docking studies support the suitability of the chromone nucleus as a potential MAOB/ A2AAR dual binding scaffold. Additionally, a virtual screening tool based on a group fusion similarity search approach was developed for the prioritization of potential MAO-B/A2AAR dual binder candidates. Among several data fusion schemes evaluated, the MEAN-SIM and MIN-RANK GFSS approaches demonstrated to be efficient virtual screening tools. Then, a combinatorial library potentially enriched with MAO-B/A2AAR dual binding chromone derivatives was assembled and sorted by using the MIN-RANK and then the MEAN-SIM GFSS VS approaches., Conclusion: The information and tools provided in this work represent valuable decision making elements in the search of novel chromone derivatives with a favorable dual binding profile as MAOB inhibitors and A2AAR antagonists with the potential to act as a disease-modifying therapeutic for Parkinson's disease., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.)

Background: The systemic information enclosed in microarray data encodes relevant clues to overcome the poorly understood combination of genetic and environmental factors in Parkinson's disease (PD), which represents the major obstacle to understand its pathogenesis and to develop disease-modifying therapeutics. While several gene prioritization approaches have been proposed, none dominate over the rest. Instead, hybrid approaches seem to outperform individual approaches., Methods: A consensus strategy is proposed for PD related gene prioritization from mRNA microarray data based on the combination of three independent prioritization approaches: Limma, machine learning, and weighted gene co-expression networks., Results: The consensus strategy outperformed the individual approaches in terms of statistical significance, overall enrichment and early recognition ability. In addition to a significant biological relevance, the set of 50 genes prioritized exhibited an excellent early recognition ability (6 of the top 10 genes are directly associated with PD). 40 % of the prioritized genes were previously associated with PD including well-known PD related genes such as SLC18A2, TH or DRD2. Eight genes (CCNH, DLK1, PCDH8, SLIT1, DLD, PBX1, INSM1, and BMI1) were found to be significantly associated to biological process affected in PD, representing potentially novel PD biomarkers or therapeutic targets. Additionally, several metrics of standard use in chemoinformatics are proposed to evaluate the early recognition ability of gene prioritization tools., Conclusions: The proposed consensus strategy represents an efficient and biologically relevant approach for gene prioritization tasks providing a valuable decision-making tool for the study of PD pathogenesis and the development of disease-modifying PD therapeutics.

Background: Virtual Screening methodologies have emerged as efficient alternatives for the discovery of new drug candidates. At the same time, ensemble methods are nowadays frequently used to overcome the limitations of employing a single model in ligand-based drug design. However, many applications of ensemble methods to this area do not consider important aspects related to both virtual screening and the modeling process. During the application of ensemble methods to virtual screening the proper validation of the models in virtual screening conditions is often neglected. No analysis of the diversity of the ensemble members is performed frequently or no considerations regarding the applicability domain of the base models are being made., Methods: In this research, we review basic concepts and definitions related to virtual screening. We comment recent applications of ensemble methods to ligand-based virtual screening and highlight their advantages and limitations., Results: Next, we propose a method based on genetic algorithms optimization for the generation of virtual screening tailored ensembles which address the previously identified problems in the current applications of ensemble methods to virtual screening., Conclusion: Finally, the proposed methodology is successfully applied to the generation of ensemble models for the ligand-based virtual screening of dual target A2A adenosine receptor antagonists and MAO-B inhibitors as potential Parkinson's disease therapeutics.