Garbage in, garbage out: how reliable training data improved a virtual screening approach against SARS-CoV-2 MPro.

Introduction: The identification of chemical compounds that interfere with SARS-CoV-2 replication continues to be a priority in several academic and pharmaceutical laboratories. Computational tools and approaches have the power to integrate, process and analyze multiple data in a short time. However, these initiatives may yield unrealistic results if the applied models are not inferred from reliable data and the resulting predictions are not confirmed by experimental evidence. Methods: We undertook a drug discovery campaign against the essential major protease (MPro) from SARS-CoV-2, which relied on an in silico search strategy -performed in a large and diverse chemolibrary- complemented by experimental validation. The computational method comprises a recently reported ligand-based approach developed upon refinement/learning cycles, and structure-based approximations. Search models were applied to both retrospective (in silico) and prospective (experimentally confirmed) screening. Results: The first generation of ligand-based models were fed by data, which to a great extent, had not been published in peer-reviewed articles. The first screening campaign performed with 188 compounds (46 in silico hits and 100 analogues, and 40 unrelated compounds: flavonols and pyrazoles) yielded three hits against MPro (IC50 ≤ 25 µM): two analogues of in silico hits (one glycoside and one benzo-thiazol) and one flavonol. A second generation of ligand-based models was developed based on this negative information and newly published peer-reviewed data for MPro inhibitors. This led to 43 new hit candidates belonging to different chemical families. From 45 compounds (28 in silico hits and 17 related analogues) tested in the second screening campaign, eight inhibited MPro with IC50 = 0.12-20 µM and five of them also impaired the proliferation of SARS-CoV-2 in Vero cells (EC50 7-45 µM). Discussion: Our study provides an example of a virtuous loop between computational and experimental approaches applied to target-focused drug discovery against a major and global pathogen, reaffirming the well-known "garbage in, garbage out" machine learning principle.

Identification of Antibacterial Peptide Candidates Encrypted in Stress-Related and Metabolic Saccharomyces cerevisiae Proteins.

The protein-rich nature of Saccharomyces cerevisiae has led this yeast to the spotlight concerning the search for antimicrobial peptides. Herein, a <10 kDa peptide-rich extract displaying antibacterial activity was obtained through the autolysis of yeast biomass under mild thermal treatment with self-proteolysis by endogenous peptidases. Estimated IC50 for the peptide pools obtained by FPLC gel filtration indicated improved antibacterial activities against foodborne bacteria and bacteria of clinical interest. Similarly, the estimated cytotoxicity concentrations against healthy human fibroblasts, alongside selective indices ≥10, indicates the fractions are safe, at least in a mixture format, for human tissues. Nano-LC-MS/MS analysis revealed that the peptides in FPLC fractions could be derived from both induced-proteolysis and proteasome activity in abundant proteins, up-regulated under stress conditions during S. cerevisiae biomass manufacturing, including those coded by TDH1/2/3, HSP12, SSA1/2, ADH1/2, CDC19, PGK1, PPI1, PDC1, and GMP1, as well as by other non-abundant proteins. Fifty-eight AMP candidate sequences were predicted following an in silico analysis using four independent algorithms, indicating their possible contribution to the bacterial inactivation observed in the peptides pool, which deserve special attention for further validation of individual functionality. S. cerevisiae-biomass peptides, an unconventional but abundant source of pharmaceuticals, may be promissory adjuvants to treat infectious diseases that are poorly sensitive to conventional antibiotics.

Selective Separation and Preconcentration of Caffeine from Natural and Pharmaceutical Products using New Polyurethane Foams

Abstract Two polyurethane foam-based sorbents (PUF) were synthesized by imprinting and grafting techniques and examined for selective separation and preconcentration of caffeine (CAF) in some pharmaceutical products and in black tea. Molecularly imprinted PUF was synthesized based on hydrogen-bonding interactions between CAF and alizarin yellow G (AYG) and subsequent polymerization into PUF. The static experiments indicated optimum sorption conditions at pH=6.5 and 5.5 for imprinted PUF (AY-IPUF) and grafted PUF (AY-GPUF), respectively. In the online experiments, the suitable preconcentration time was found to be 40 and 20s for (AY-IPUF) and (AY-GPUF), respectively, at a flow rate of 1.75 mL.min-1. Desorption of CAF has been affected by passing 500 µL of 0.05, 0.01 mol.L−1 HCl eluent onto (AY-IPUF) and (AY-GPUF), respectively. The online methods have provided satisfactory enrichment factors of 8.4 and 10.5 for (AY-IPUF) and (AY-GPUF), respectively. The time consumed for preconcentartion, elution and determination steps was 1.48 and 1.05 min, thus, the throughput was 42 and 57 h-1, for (AY-IPUF) and (AY-GPUF), respectively. The developed sorbents were studied for the determination of CAF in pharmaceutical samples which will be helpful to minimize caffeinism. Finally, in silico bioactivity, ADMET and drug-likeness predictive computational studies of caffeine were also carried out

Chemistry, Biological Activities and In Silico Bioprospection of Sterols and Triterpenes from Mexican Columnar Cactaceae.

The Cactaceae family is an important source of triterpenes and sterols. The wide uses of those plants include food, gathering, medicinal, and live fences. Several studies have led to the isolation and characterization of many bioactive compounds. This review is focused on the chemistry and biological properties of sterols and triterpenes isolated mainly from some species with columnar and arborescent growth forms of Mexican Cactaceae. Regarding the biological properties of those compounds, apart from a few cases, their molecular mechanisms displayed are not still fully understand. To contribute to the above, computational chemistry tools have given a boost to traditional methods used in natural products research, allowing a more comprehensive exploration of chemistry and biological activities of isolated compounds and extracts. From this information an in silico bioprospection was carried out. The results suggest that sterols and triterpenoids present in Cactaceae have interesting substitution patterns that allow them to interact with some bio targets related to inflammation, metabolic diseases, and neurodegenerative processes. Thus, they should be considered as attractive leads for the development of drugs for the management of chronic degenerative diseases.

Application of In Silico and HTS Approaches to Identify Nuclear Import Inhibitors for Venezuelan Equine Encephalitis Virus Capsid Protein: A Case Study.

The development of new drugs is costly and time-consuming, with estimates of over $US1 billion and 15 years for a product to reach the market. As understanding of the molecular basis of disease improves, various approaches have been used to target specific molecular interactions in the search for effective drugs. These include high-throughput screening (HTS) for novel drug identification and computer-aided drug design (CADD) to assess the properties of putative drugs before experimental work begins. We have applied conventional HTS and CADD approaches to the problem of identifying antiviral compounds to limit infection by Venezuelan equine encephalitis virus (VEEV). Nuclear targeting of the VEEV capsid (CP) protein through interaction with the host nuclear import machinery has been shown to be essential for viral pathogenicity, with viruses incapable of this interaction being greatly attenuated. Our previous conventional HTS and in silico structure-based drug design (SBDD) screens were successful in identifying novel inhibitors of CP interaction with the host nuclear import machinery, thus providing a unique opportunity to assess the relative value of the two screening approaches directly. This focused review compares and contrasts the two screening approaches, together with the properties of the inhibitors identified, as a case study for parallel use of the two approaches to identify antivirals. The utility of SBDD screens, especially when used in parallel with traditional HTS, in identifying agents of interest to target the host-pathogen interface is highlighted.

In Silico Drug Repositioning for Chagas Disease.

Chagas disease is an infectious tropical disease included within the group of neglected tropical diseases. Though historically endemic to Latin America, it has lately spread to high-income countries due to human migration. At present, there are only two available drugs, nifurtimox and benznidazole, approved for this treatment, both with considerable side-effects (which often result in treatment interruption) and limited efficacy in the chronic stage of the disease in adults. Drug repositioning involves finding novel therapeutic indications for known drugs, including approved, withdrawn, abandoned and investigational drugs. It is today a broadly applied approach to develop innovative medications, since indication shifts are built on existing safety, ADME and manufacturing information, thus greatly shortening development timeframes. Drug repositioning has been signaled as a particularly interesting strategy to search for new therapeutic solutions for neglected and rare conditions, which traditionally present limited commercial interest and are mostly covered by the public sector and not-for-profit initiatives and organizations. Here, we review the applications of computer-aided technologies as systematic approaches to drug repositioning in the field of Chagas disease. In silico screening represents the most explored approach, whereas other rational methods such as network-based and signature-based approximations have still not been applied.

Modern Approaches for the Discovery of Anti-Infectious Drugs for the Treatment of Neglected Diseases.

Neglected diseases comprise a number of infectious diseases historically endemic to low- and middle-income countries, though recently they have spread to high-income countries due to human migrations. In the past, pharmaceutical companies have shown hesitant to invest in these health conditions, due to the limited return on investment. As a result, the role of the academic sector and not-for-profit organizations in the discovery of new drugs for neglected diseases has been particularly relevant. Here, we review recent applications of modern drug discovery technologies in the field of neglected diseases, including high-throughput screening, in silico screening and computer-aided drug design. The suitability and perspectives of each approach are discussed depending on the context, along with the technology and translational gaps influencing them.

Novel anti-prostate cancer scaffold identified by the combination of in silico and cell-based assays targeting the PI3K-AKT-mTOR pathway.

Phenotypic assays were performed in prostate cancer cell lines to describe the biological activity of PI3K-AKT-mTOR pathway inhibitors retrieved from the virtual screening initiative. These novel chemicals share in common the aminopyridine scaffold, hitting PC-3 cells in macromolar range, with selectivity index over fibroblast cell lines. Moreover, a preliminary study of the mode of action by flow cytometry assay pointed out that these compounds had a rapamycin-like response for the PI3K-AKT-mTOR pathway modulation.

Computational Identification of Dengue Virus MicroRNA-Like Structures and their Cellular Targets.

MicroRNAs (miRNAs) are small, noncoding RNA molecules that regulate transcriptional and posttranscriptional gene regulation of the cell. Experimental evidence shows that miRNAs have a direct role in different cellular processes, such as immune function, apoptosis, and tumorigenesis. In a viral infection context, miRNAs have been connected with the interplay between host and pathogen, occupying a major role in pathogenesis. While numerous viral miRNAs from DNA viruses have been identified, characterization of functional RNA virus-encoded miRNAs and their potential targets is still ongoing. Here, we used an in silico approach to analyze dengue Virus genome sequences. Pre-miRNAs were extracted through VMir software, and the identification of putative pre-miRNAs and mature miRNAs was accessed using Support Vector Machine web tools. The targets were scanned using miRanda software and functionally annotated using ClueGo. Via computational tools, eight putative miRNAs were found to hybridize with numerous targets of morphogenesis, differentiation, migration, and growth pathways that may play a major role in the interaction of the virus and its host. Future approaches will focus on experimental validation of their presence and target messenger RNA genes to further elucidate their biological functions in human and mosquito cells.

Validation of computationally predicted substrates for laccase

Present study reports the validation (oxidation) of computationally predicted oxidation of xenobiotic contaminants by commercially available pure laccase from Trametes versicolor. Selected contaminants were predicted as potential targets for laccase oxidation by using in-silico docking tool. The oxidation by laccase was measured by change in absorbance at specific λ max of each compound. Sinapic acid and tyrosine were taken as positive and negative controls, respectively. Oxidation was observed in m-chlorophenol, 2,4 di-chlorophenol, 2,4,6 tri-chlorophenol, captan, atrazine and thiodicarb, except malathion, which showed no activity. It could be speculated that the predicted substrates showing oxidation shared homology at structural and chemical level with positive control compounds. In case of malathion, structural non-homology with sinapic acid could be attributed to its inactivity towards laccase that required further structural analysis. Thus, a remediation tool proposing an advanced remediation approach combining the application of theoretical in-silico method and subsequent experimental validation using pure laccase could be proposed. As number and type of xenobiotics increase, the unfeasibility to screen them experimentally for bioremediation also rise. This approach would be useful to reduce the time and cost required in other screening methods.