Docking and chemoinformatic screens for new ligands and targets

Unlocking Benzosampangine's Potential: A Computational Approach to Investigating, Its Role as a PD-L1 Inhibitor in Tumor Immune Evasion via Molecular Docking, Dynamic Simulation, and ADMET Profiling

The interaction between programmed cell death protein 1 (PD-1) and its ligand PD-L1 plays a crucial role in tumor immune evasion, presenting a critical target for cancer immunotherapy. Despite being effective, current monoclonal antibodies present some drawbacks such as high costs, toxicity, and resistance development. Therefore, the development of small-molecule inhibitors is necessary, especially those derived from natural sources. In this study, benzosampangine is predicted as a promising PD-L1 inhibitor, with potential applications in cancer immunotherapy. Utilizing the high-resolution crystal structure of human PD-L1 (PDB ID: 5O45), we screened 511 natural compounds, identifying benzosampangine as a top candidate with exceptional inhibitory properties. Molecular docking predicted that benzosampangine exhibits a strong binding affinity for PD-L1 (-9.4 kcal/mol) compared with established controls such as CA-170 (-6.5 kcal/mol), BMS-202 (-8.6 kcal/mol), and pyrvinium (-8.9 kcal/mol). The compound's predicted binding efficacy is highlighted by robust interactions with key amino acids (ILE54, TYR56, GLN66, MET115, ILE116, SER117, ALA121, ASP122) within the active site, notably forming 3 Pi-sulfur interactions with MET115-an interaction absents in control inhibitors. In addition, ADMET profiling suggests that over the control molecules, benzosampangine has several key advantages, including favorable solubility, permeability, metabolic stability, and low toxicity, while adhering to Lipinski's rule of five. Molecular dynamic simulations predict the stability of the benzosampangine-PD-L1 complex, reinforcing its potential to sustain inhibition of the PD-1/PD-L1 pathway. MMGBSA analysis calculated a binding free energy (ΔGbind) of -39.39 kcal/mol for the benzosampangine-PD-L1 complex, with significant contributions from Coulombic, lipophilic, and Van der Waals interactions, validating the predicted docking results. This study investigates in silico benzosampangine, predicting its better molecular interactions and pharmacokinetic profile compared with several already known PD-L1 inhibitors.

How accurately can one predict drug binding modes using AlphaFold models?

Computational prediction of protein structure has been pursued intensely for decades, motivated largely by the goal of using structural models for drug discovery. Recently developed machine-learning methods such as AlphaFold 2 (AF2) have dramatically improved protein structure prediction, with reported accuracy approaching that of experimentally determined structures. To what extent do these advances translate to an ability to predict more accurately how drugs and drug candidates bind to their target proteins? Here, we carefully examine the utility of AF2 protein structure models for predicting binding poses of drug-like molecules at the largest class of drug targets, the G-protein-coupled receptors. We find that AF2 models capture binding pocket structures much more accurately than traditional homology models, with errors nearly as small as differences between structures of the same protein determined experimentally with different ligands bound. Strikingly, however, the accuracy of ligand-binding poses predicted by computational docking to AF2 models is not significantly higher than when docking to traditional homology models and is much lower than when docking to structures determined experimentally without these ligands bound. These results have important implications for all those who might use predicted protein structures for drug discovery.

Exploring the Fatty Acid Binding Pocket in the SARS-CoV-2 Spike Protein - Confirmed and Potential Ligands

Severe Acute Respiratory syndrome 2 (SARS-CoV-2) is a respiratory virus responsible for coronavirus disease 19 (COVID-19) and the still ongoing and unprecedented global pandemic. The key viral protein for cell infection is the spike glycoprotein, a surface-exposed fusion protein that both recognizes and mediates entry into host cells. Within the spike glycoprotein, a fatty acid binding pocket (FABP) was confirmed, with the crystallization of linoleic acid (LA) occupying a well-defined site. Importantly, when the pocket is occupied by a fatty acid, an inactive conformation is stabilized, and cell recognition is hindered. In this review, we discuss ligands reported so far for this site, correlating their activity predicted through in silico studies with antispike experimental activity, assessed by either binding assays or cell-infection assays. LA was the first confirmed ligand, cocrystallized in a cryo-EM structure of the spike protein, resulting in increased stability of the inactive conformation of the spike protein. The next identified ligand, lifitegrast, was also experimentally confirmed as a ligand with antiviral activity, suggesting the potential for diverse chemical scaffolds to bind this site. Finally, SPC-14 was also confirmed as a ligand, although no inhibition assays were performed. In this review, we identified 20 studies describing small-molecule compounds predicted to bind the pocket in in silico studies and with confirmed binding or in vitro activity, either inhibitory activity against the spike-ACE2 interaction or antiviral activity in cell-based assays. When considering all ligands confirmed with in vitro assays, a good overall occupation of the pocket should be complemented with the ability to make direct interactions, both hydrophilic and hydrophobic, with key amino acid residues defining the pocket surface. Among the active compounds, long flexible carbon chains are recurrent, with retinoids capable of binding the FABP, although bulkier systems are also capable of affecting viral fitness. Compounds able to bind this site with high affinity have the potential to stabilize the inactive conformation of the SARS-CoV-2 spike protein and therefore reduce the virus's ability to infect new cells. Since this pocket is conserved in highly pathogenic human coronaviruses, including MERS-CoV and SARS-CoV, this effect could be exploited for the development of new antiviral agents, with broad-spectrum anticoronavirus activity.

Elucidating disease-associated mechanisms triggered by pollutants via the epigenetic landscape using large-scale ChIP-Seq data

BACKGROUND

Despite well-documented effects on human health, the action modes of environmental pollutants are incompletely understood. Although transcriptome-based approaches are widely used to predict associations between chemicals and disorders, the molecular cues regulating pollutant-derived gene expression changes remain unclear. Therefore, we developed a data-mining approach, termed "DAR-ChIPEA," to identify transcription factors (TFs) playing pivotal roles in the action modes of pollutants.

METHODS

Large-scale public ChIP-Seq data (human, n = 15,155; mouse, n = 13,156) were used to predict TFs that are enriched in the pollutant-induced differentially accessible genomic regions (DARs) obtained from epigenome analyses (ATAC-Seq). The resultant pollutant-TF matrices were then cross-referenced to a repository of TF-disorder associations to account for pollutant modes of action. We subsequently evaluated the performance of the proposed method using a chemical perturbation data set to compare the outputs of the DAR-ChIPEA and our previously developed differentially expressed gene (DEG)-ChIPEA methods using pollutant-induced DEGs as input. We then adopted the proposed method to predict disease-associated mechanisms triggered by pollutants.

RESULTS

The proposed approach outperformed other methods using the area under the receiver operating characteristic curve score. The mean score of the proposed DAR-ChIPEA was significantly higher than that of our previously described DEG-ChIPEA (0.7287 vs. 0.7060; Q = 5.278 × 10-42; two-tailed Wilcoxon rank-sum test). The proposed approach further predicted TF-driven modes of action upon pollutant exposure, indicating that (1) TFs regulating Th1/2 cell homeostasis are integral in the pathophysiology of tributyltin-induced allergic disorders; (2) fine particulates (PM2.5) inhibit the binding of C/EBPs, Rela, and Spi1 to the genome, thereby perturbing normal blood cell differentiation and leading to immune dysfunction; and (3) lead induces fatty liver by disrupting the normal regulation of lipid metabolism by altering hepatic circadian rhythms.

CONCLUSIONS

Highlighting genome-wide chromatin change upon pollutant exposure to elucidate the epigenetic landscape of pollutant responses outperformed our previously described method that focuses on gene-adjacent domains only. Our approach has the potential to reveal pivotal TFs that mediate deleterious effects of pollutants, thereby facilitating the development of strategies to mitigate damage from environmental pollution.

Structure-based discovery of novel cruzain inhibitors with distinct trypanocidal activity profiles

Over 110 years after the first formal description of Chagas disease, the trypanocidal drugs thus far available have limited efficacy and several side effects. This encourages the search for novel treatments that inhibit T. cruzi targets. One of the most studied anti-T. cruzi targets is the cysteine protease cruzain; it is associated with metacyclogenesis, replication, and invasion of the host cells. We used computational techniques to identify novel molecular scaffolds that act as cruzain inhibitors. First, with a docking-based virtual screening, we identified compound 8, a competitive cruzain inhibitor with a K_i of 4.6 μM. Then, aided by molecular dynamics simulations, cheminformatics, and docking, we identified the analog compound 22 with a K_i of 27 μM. Surprisingly, despite sharing the same isoquinoline scaffold, compound 8 presented higher trypanocidal activity against the epimastigote forms, while compound 22, against the trypomastigotes and amastigotes. Taken together, compounds 8 and 22 represent a promising scaffold for further development of trypanocidal compounds as drug candidates for treating Chagas disease.

Structure-based discovery of conformationally selective inhibitors of the serotonin transporter

The serotonin transporter (SERT) removes synaptic serotonin and is the target of anti-depressant drugs. SERT adopts three conformations: outward-open, occluded, and inward-open. All known inhibitors target the outward-open state except ibogaine, which has unusual anti-depressant and substance-withdrawal effects, and stabilizes the inward-open conformation. Unfortunately, ibogaine's promiscuity and cardiotoxicity limit the understanding of inward-open state ligands. We docked over 200 million small molecules against the inward-open state of the SERT. Thirty-six top-ranking compounds were synthesized, and thirteen inhibited; further structure-based optimization led to the selection of two potent (low nanomolar) inhibitors. These stabilized an outward-closed state of the SERT with little activity against common off-targets. A cryo-EM structure of one of these bound to the SERT confirmed the predicted geometry. In mouse behavioral assays, both compounds had anxiolytic- and anti-depressant-like activity, with potencies up to 200-fold better than fluoxetine (Prozac), and one substantially reversed morphine withdrawal effects.

Allosteric modulators of solute carrier function: a theoretical framework

Large-scale drug screening is currently the basis for the identification of new chemical entities. This is a rather laborious approach, because a large number of compounds must be tested to cover the chemical space in an unbiased fashion. However, the structures of targetable proteins have become increasingly available. Thus, a new era has arguably been ushered in with the advent of methods, which allow for structure-based docking campaigns (i.e., virtual screens). Solute carriers (SLCs) are among the most promising drug targets. This claim is substantiated by the fact that a large fraction of the 400 solute carrier genes is associated with human diseases. The ability to dock large ligand libraries into selected structures of solute carriers has set the stage for rational drug design. In the present study, we show that these structure-based approaches can be refined by taking into account how solute carriers operate. We specifically address the feasibility of targeting solute carriers with allosteric modulators, because their actions differ fundamentally from those of ligands, which bind to the substrate binding site. For the pertinent analysis we used transition state theory in conjunction with the linear free energy relationship (LFER). These provide the theoretical framework to understand how allosteric modulators affect solute carrier function.

Structural similarities between SARS-CoV2 3CLpro and other viral proteases suggest potential lead molecules for developing broad spectrum antivirals

Considering the significant impact of the recent COVID-19 outbreak, development of broad-spectrum antivirals is a high priority goal to prevent future global pandemics. Antiviral development processes generally emphasize targeting a specific protein from a particular virus. However, some antiviral agents developed for specific viral protein targets may exhibit broad spectrum antiviral activity, or at least provide useful lead molecules for broad spectrum drug development. There is significant potential for repurposing a wide range of existing viral protease inhibitors to inhibit the SARS-CoV2 3C-like protease (3CLpro). If effective even as relatively weak inhibitors of 3CLpro, these molecules can provide a diverse and novel set of scaffolds for new drug discovery campaigns. In this study, we compared the sequence- and structure-based similarity of SARS-CoV2 3CLpro with proteases from other viruses, and identified 22 proteases with similar active-site structures. This structural similarity, characterized by secondary-structure topology diagrams, is evolutionarily divergent within taxonomically related viruses, but appears to result from evolutionary convergence of protease enzymes between virus families. Inhibitors of these proteases that are structurally similar to the SARS-CoV2 3CLpro protease were identified and assessed as potential inhibitors of SARS-CoV2 3CLpro protease by virtual docking. Several of these molecules have docking scores that are significantly better than known SARS-CoV2 3CLpro inhibitors, suggesting that these molecules are also potential inhibitors of the SARS-CoV2 3CLpro protease. Some have been previously reported to inhibit SARS-CoV2 3CLpro. The results also suggest that established inhibitors of SARS-CoV2 3CLpro may be considered as potential inhibitors of other viral 3C-like proteases.

A New Combination: Anti Glypican-3 scFv and Diphtheria Toxin with the Best Flexible Linker

Along with all cancer treatments, including chemotherapy, radiotherapy, and surgery, targeting therapy is a new treatment manner. Immunotoxins are new recombinant structures that kill cancer cells by targeting specific antigens. Immunotoxins are composed of two parts: toxin moiety, which disrupts protein synthesis process, and antigen binding moiety that bind to antigens on the surface of cancer cells. Glypican 3 (GPC3) is an oncofetal antigen on the surface of Hepatocellular carcinoma (HCC) cells. In this study, truncated Diphtheria toxin (DT389) was fused to humanized scFv YP7 by one, two and three repeats of GGGGS linkers (DT389-(GGGGS)_1-3YP7). In-silico and experimental investigation were performed to find out how many repeats of linker between toxin and scFv moieties are sufficient. Results of in-silico investigations revealed that the difference in the number of linkers does not have a significant effect on the main structures of the immunotoxin; however, the three-dimensional structure of two repeats of linker had a more appropriate structure compared to others with one and three linker replications. In addition, with enhancing the number of linkers, the probability of protein solubility has increased. Generally, the bioinformatics results of DT389-(GGGGS)₂-YP7 structure showed that expression and folding is suitable; and YP7 scFv has appropriate orientation to bind GPC3. The experimental investigations indicated that the fusion protein was expressed as near to 50% soluble. Due to the high binding affinity of YP7 scFv and the proven potency of diphtheria in inhibiting protein synthesis, the proposed DT389-(GGGGS)₂-YP7 immunotoxin is expected to function well in inhibiting HCC.

Epigenetic landscape of drug responses revealed through large-scale ChIP-seq data analyses

Background

Elucidating the modes of action (MoAs) of drugs and drug candidate compounds is critical for guiding translation from drug discovery to clinical application. Despite the development of several data-driven approaches for predicting chemical–disease associations, the molecular cues that organize the epigenetic landscape of drug responses remain poorly understood.

Results

With the use of a computational method, we attempted to elucidate the epigenetic landscape of drug responses, in terms of transcription factors (TFs), through large-scale ChIP-seq data analyses. In the algorithm, we systematically identified TFs that regulate the expression of chemically induced genes by integrating transcriptome data from chemical induction experiments and almost all publicly available ChIP-seq data (consisting of 13,558 experiments). By relating the resultant chemical–TF associations to a repository of associated proteins for a wide range of diseases, we made a comprehensive prediction of chemical–TF–disease associations, which could then be used to account for drug MoAs. Using this approach, we predicted that: (1) cisplatin promotes the anti-tumor activity of TP53 family members but suppresses the cancer-inducing function of MYCs; (2) inhibition of RELA and E2F1 is pivotal for leflunomide to exhibit antiproliferative activity; and (3) CHD8 mediates valproic acid-induced autism.

Conclusions

Our proposed approach has the potential to elucidate the MoAs for both approved drugs and candidate compounds from an epigenetic perspective, thereby revealing new therapeutic targets, and to guide the discovery of unexpected therapeutic effects, side effects, and novel targets and actions.

Artificial intelligence unifies knowledge and actions in drug repositioning

Drug repositioning aims to reuse existing drugs, shelved drugs, or drug candidates that failed clinical trials for other medical indications. Its attraction is sprung from the reduction in risk associated with safety testing of new medications and the time to get a known drug into the clinics. Artificial Intelligence (AI) has been recently pursued to speed up drug repositioning and discovery. The essence of AI in drug repositioning is to unify the knowledge and actions, i.e. incorporating real-world and experimental data to map out the best way forward to identify effective therapeutics against a disease. In this review, we share positive expectations for the evolution of AI and drug repositioning and summarize the role of AI in several methods of drug repositioning.

Toxicological insights of Spike fragments SARS-CoV-2 by exposure environment: A threat to aquatic health?

The Spike protein (S protein) is a critical component in the infection of the new coronavirus (SARS-CoV-2). The objective of this work was to evaluate whether peptides from S protein could cause negative impact in the aquatic animals. The aquatic toxicity of SARS-CoV-2 Spike protein peptides derivatives has been evaluated in tadpoles (n = 50 tadpoles/5 replicates of 10 animals) from species Physalaemus cuvieri (Leptodactylidae). After synthesis, purification, and characterization of peptides (PSDP2001, PSDP2002, PSDP2003) an aquatic contamination has been simulated with these peptides during 24 h of exposure in two concentrations (100 and 500 ng/mL). The control group ("C") was composed of tadpoles kept in polyethylene containers containing de-chlorinated water. Oxidative stress, antioxidant biomarkers and AChE activity were assessed. In both concentrations, PSPD2002 and PSPD2003 increased catalase and superoxide dismutase antioxidants enzymes activities, as well as oxidative stress (nitrite levels, hydrogen peroxide and reactive oxygen species). All three peptides also increased acetylcholinesterase activity in the highest concentration. These peptides showed molecular interactions in silico with acetylcholinesterase and antioxidant enzymes. Aquatic particle contamination of SARS-CoV-2 has cholinesterasic effect in P. cuvieri tadpoles. These findings indicate that the COVID-19 can constitute environmental impact or biological damage potential.

Can use of hydroxychloroquine and azithromycin as a treatment of COVID-19 affect aquatic wildlife? A study conducted with neotropical tadpole

The impacts on human health and the economic and social disruption caused by the pandemic COVID-19 have been devastating. However, its environmental consequences are poorly understood. Thus, to assess whether COVID-19 therapy based on the use of azithromycin (AZT) and hydroxychloroquine (HCQ) during the pandemic affects wild aquatic life, we exposed (for 72 h) neotropical tadpoles of the species Physalaemus cuvieri to the water containing these drugs to 12.5 μg/L. We observed that the increase in superoxide dismutase and catalase in tadpoles exposed to AZT (alone or in combination with HCQ) was predominant to keep the production of NO, ROS, TBARS and H2O2 equitable between the experimental groups. In addition, the uptake of AZT and the strong interaction of AZT with acetylcholinesterase (AChE), predicted by the molecular docking analysis, were associated with the anticholinesterase effect observed in the groups exposed to the antibiotic. However, the unexpected increase in butyrylcholinesterase (BChE) in these same groups suggests its constitutive role in maintaining cholinergic homeostasis. Therefore, taken together, our data provide a pioneering evidence that the exposure of P. cuvieri tadpoles to AZT (alone or in combination with HCQ) in a predictably increased environmental concentration (12.5 μg/L) elicits a compensatory adaptive response that can have, in the short period of exposure, guaranteed the survival of the animals. However, the high energy cost for maintaining physiological homeostasis, can compromise the growth and development of animals and, therefore, in the medium-long term, have a general negative effect on the health of animals. Thus, it is possible that COVID-19 therapy, based on the use of AZT, affects wild aquatic life, which requires greater attention to the impacts that this drug may represent.

High-Throughput Virtual Screening for a New Class of Antagonist Targeting LasR of Pseudomonas aeruginosa

Pseudomonas aeruginosa, an opportunistic human pathogen, causes fatal effects in patients with cystic fibrosis and immunocompromised individuals and leads to around 1000 deaths annually. The quorum sensing mechanism of P. aeruginosa plays a major role in promoting biofilm formation and expression of virulent genes. Hence, quorum sensing inhibition is a promising novel approach to treat these bacterial infections as these organisms show a wide range of antibiotic resistance. Among the interconnected quorum sensing network of P. aeruginosa, targeting the las system is of increased interest as its principal receptor protein LasR is the earliest activated gene. It is also shown to be involved in the regulation of other virulence-associated genes. In this study, we have applied high-throughput virtual screening, an in silico computational method to identify a new class of LasR inhibitors that could serve as potent antagonists to treat P. aeruginosa-associated infections. Three-tire structure-based virtual screening was performed on the Schrödinger small molecule database, which resulted in 12 top hit compounds with docking scores lesser than -11.0 kcal/mol. Three of these best-scored compounds CACPD2011a-0001928786 (C1), CACPD2011a-0001927437 (C2), and CACPD2011a-0000896051 (C3) were further analyzed. The binding free energies of these compounds in complex with the target protein LasR (3IX4) were evaluated, and the pharmacokinetic properties were determined. The stability of the docked complexes was assessed by running a molecular dynamics simulation for 100 ns. Molecular dynamics simulation analysis revealed that all three compounds were found to be in stable contact with the protein over the entire simulation period. The antagonistic effect of these compounds was validated using the LasR reporter gene assay in the presence of acyl homoserine lactone. Significant reduction in the β-galactosidase enzyme activity was achieved at 100 nM concentration for all three compounds pursued. Hence, the present study provides strong evidence that these three compounds could serve as quorum sensing inhibitors of P. aeruginosa LasR protein and can be a probable candidate to treat Pseudomonas-associated infections.

Hepatitis C virus drugs that inhibit SARS-CoV-2 papain-like protease synergize with remdesivir to suppress viral replication in cell culture

Effective control of COVID-19 requires antivirals directed against SARS-CoV-2. We assessed 10 hepatitis C virus (HCV) protease-inhibitor drugs as potential SARS-CoV-2 antivirals. There is a striking structural similarity of the substrate binding clefts of SARS-CoV-2 main protease (Mpro) and HCV NS3/4A protease. Virtual docking experiments show that these HCV drugs can potentially bind into the Mpro substrate-binding cleft. We show that seven HCV drugs inhibit both SARS-CoV-2 Mpro protease activity and SARS-CoV-2 virus replication in Vero and/or human cells. However, their Mpro inhibiting activities did not correlate with their antiviral activities. This conundrum is resolved by demonstrating that four HCV protease inhibitor drugs, simeprevir, vaniprevir, paritaprevir, and grazoprevir inhibit the SARS CoV-2 papain-like protease (PLpro). HCV drugs that inhibit PLpro synergize with the viral polymerase inhibitor remdesivir to inhibit virus replication, increasing remdesivir's antiviral activity as much as 10-fold, while those that only inhibit Mpro do not synergize with remdesivir.

Hepatitis C virus drugs that inhibit SARS-CoV-2 papain-like protease synergize with remdesivir to suppress viral replication in cell culture

Effective control of COVID-19 requires antivirals directed against SARS-CoV-2. We assessed 10 hepatitis C virus (HCV) protease-inhibitor drugs as potential SARS-CoV-2 antivirals. There is a striking structural similarity of the substrate binding clefts of SARS-CoV-2 main protease (M^pro) and HCV NS3/4A protease. Virtual docking experiments show that these HCV drugs can potentially bind into the M^pro substrate-binding cleft. We show that seven HCV drugs inhibit both SARS-CoV-2 M^pro protease activity and SARS-CoV-2 virus replication in Vero and/or human cells. However, their M^pro inhibiting activities did not correlate with their antiviral activities. This conundrum is resolved by demonstrating that four HCV protease inhibitor drugs, simeprevir, vaniprevir, paritaprevir, and grazoprevir inhibit the SARS CoV-2 papain-like protease (PL^pro). HCV drugs that inhibit PL^pro synergize with the viral polymerase inhibitor remdesivir to inhibit virus replication, increasing remdesivir's antiviral activity as much as 10-fold, while those that only inhibit M^pro do not synergize with remdesivir.

Structural Consequences of Variation in SARS-CoV-2 B.1.1.7

New globally circulating SARS-CoV-2 strains are causing concern about evolution of virus transmissibility, fitness and immune evasion mechanisms. A variant emerging from the United Kingdom called SARS-CoV-2 VUI 202012/01, or B.1.1.7, is thought to exhibit increased transmissibility that results from replication 4–10 times faster than the original Wuhan virus (Wuhan-Hu-1). Although this property is suspected to result from a specific mutation in the spike glycoprotein, D614G, there are 9 mutations that distinguish the UK variant B.1.1.7 from Wuhan-Hu-1 yet to be evaluated for functional effects. We asked if mutated positions fixed in UK variant B.1.1.7 may be involved in the virus life cycle, or evasion of the immune response, by modeling the UK variant spike protein and conducting structural analysis of mutations on the spike glycoprotein trimer (protomer) complexed to ACE2. Importantly, 4 out of 9 differences between the UK variant B.1.1.7 and Wuhan-Hu-1 spike protein alter direct intermolecular interactions. N501Y increased affinity between the spike protein and ACE2. The mutations at A570D, D614G and S982A reduced contact between individual chains of the trimeric spike protomer, potentially enhancing cleavage into S1 and S2 subunits, dynamic structural rearrangement and host cell fusion mechanisms. These data suggest that combined characteristics of mutations unique to UK variant B.1.1.7 enable high affinity binding to ACE2 and enhanced replication properties. The D614G mutation, associated with enhanced virus transmissibility, opens a potentially druggable structural pocket at the interface between spike glycoprotein subunits S1 and S2.

Molecular Modeling of Histamine Receptors-Recent Advances in Drug Discovery

The recent developments of fast reliable docking, virtual screening and other algorithms gave rise to discovery of many novel ligands of histamine receptors that could be used for treatment of allergic inflammatory disorders, central nervous system pathologies, pain, cancer and obesity. Furthermore, the pharmacological profiles of ligands clearly indicate that these receptors may be considered as targets not only for selective but also for multi-target drugs that could be used for treatment of complex disorders such as Alzheimer's disease. Therefore, analysis of protein-ligand recognition in the binding site of histamine receptors and also other molecular targets has become a valuable tool in drug design toolkit. This review covers the period 2014-2020 in the field of theoretical investigations of histamine receptors mostly based on molecular modeling as well as the experimental characterization of novel ligands of these receptors.

Drug Repurposing: An Emerging Tool for Drug Reuse, Recycling and Discovery

Drug repositioning or repurposing is a revolutionary breakthrough in drug development that focuses on rediscovering new uses for old therapeutic agents. Drug repositioning can be defined more precisely as the process of exploring new indications for an already approved drug while drug repurposing includes overall re-development approaches grounded in the identical chemical structure of the active drug moiety as in the original product. The repositioning approach accelerates the drug development process, curtails the cost and risk inherent to drug development. The strategy focuses on the polypharmacology of drugs to unlocks novel opportunities for logically designing more efficient therapeutic agents for unmet medical disorders. Drug repositioning also expresses certain regulatory challenges that hamper its further utilization. The review outlines the eminent role of drug repositioning in new drug discovery, methods to predict the molecular targets of a drug molecule, advantages that the strategy offers to the pharmaceutical industries, explaining how the industrial collaborations with academics can assist in the discovering more repositioning opportunities. The focus of the review is to highlight the latest applications of drug repositioning in various disorders. The review also includes a comparison of old and new therapeutic uses of repurposed drugs, assessing their novel mechanisms of action and pharmacological effects in the management of various disorders. Various restrictions and challenges that repurposed drugs come across during their development and regulatory phases are also highlighted.

Discovery and Design of Novel Small Molecule GSK-3 Inhibitors Targeting the Substrate Binding Site

The serine/threonine kinase, GSK-3, is a promising drug discovery target for treating multiple pathological disorders. Most GSK-3 inhibitors that were developed function as ATP competitive inhibitors, with typical limitations in specificity, safety and drug-induced resistance. In contrast, substrate competitive inhibitors (SCIs), are considered highly selective, and more suitable for clinical practice. The development of SCIs has been largely neglected in the past because the ambiguous, undefined nature of the substrate-binding site makes them difficult to design. In this study, we used our previously described structural models of GSK-3 bound to SCI peptides, to design a pharmacophore model and to virtually screen the “drug-like” Zinc database (~6.3 million compounds). We identified leading hits that interact with critical binding elements in the GSK-3 substrate binding site and are chemically distinct from known GSK-3 inhibitors. Accordingly, novel GSK-3 SCI compounds were designed and synthesized with IC₅₀ values of~1–4 μM. Biological activity of the SCI compound was confirmed in cells and in primary neurons that showed increased β-catenin levels and reduced tau phosphorylation in response to compound treatment. We have generated a new type of small molecule GSK-3 inhibitors and propose to use this strategy to further develop SCIs for other protein kinases.

Selenium overexposure induces insulin resistance: In silico study

BACKGROUND AND AIMS

Several studies raise concerns about the possible association of high selenium exposure with insulin resistance and type 2 diabetes. This in silico study proposes a possible mechanism of insulin resistance in the case of overexposure to selenium.

METHOD

A study was carried out using molecular modeling, where cysteines of the insulin-receptor are replaced by selenocysteines. Calculation of the interaction energy of the receptor was performed in both cases with Auto Dock Tools and Vina 4.2 software to predict whether the substitution of amino acid could lead to destabilization of the protein-ligand complex and therefore possibly insulin resistance. Finally, the docked complex was analyzed by using BIOVIA Discovery Studio Visualizer to show the type of interactions between the ligands and insulin-receptor, and to determine the distance of the ligands from the binding site on insulin-receptor.

RESULTS

The results show that the substitution of cysteine by selenocysteine in the insulin receptor does not lead to stabilization of the complex receptor/insulin, but to its disruption.In addition, the types and the number of bonds between insulin and its receptor in the two cases are different, where 7 strong bonds between insulin and its receptor were found in the case of the cysteine complex compared to 6 weak bonds in the second case.

CONCLUSION

Findings of this study suggest that misincorporation of selenocysteines in insulin receptor could lead to destabilization of the insulin-receptor complex and therefore may possibly cause an insulin resistance.

New dehydroabietic acid (DHA) derivatives with anticancer activity against HepG2 cancer cell lines as a potential drug targeting EGFR kinase domain. CoMFA study and virtual ligand-based screening

Liver cancer has become the third type of cancer that causes death; this is why the design of new chemotherapeutic drugs against this disease is a major need. With this idea, a series of Dehydroabietic Acid-Based Acylhydrazones have been used to generate a CoMFA model to design new anticancer agents. In this study, we employed a Comparative Molecular Field Analysis studies, we performed those methods on Dehydroabietic Acid-Based Acylhydrazones against HepG2 human cancer cell line. The statistical results are encouraging with Q2 equal to 0.527 and R2 equal to 0.962. The predictive ability of this model was determined using a test set of Dehydroabietic Acid-Based Acylhydrazones that gave an acceptable predictive correlation (R2test) value of 0.614. The developed model guides to design five new molecules with enhanced activity as potential drugs. On the other hand to determine a potential target to these ligands we have established a virtual screening using reverse docking with the most active molecule and 42 antiproliferative targets. Based on the affinity of complex ligand-Target, the intracellular domain of EGFR shows high stability. This suggests that our designed molecules can inhibit the target EGFR which is an important target on targeted therapy of many types of cancer.Communicated by Ramaswamy H. Sarma.

Structural Similarity of SARS-CoV2 Mpro and HCV NS3/4A Proteases Suggests New Approaches for Identifying Existing Drugs Useful as COVID-19 Therapeutics

During the current COVID-19 pandemic more than 160,000 people have died worldwide as of mid-April 2020, and the global economy has been crippled. Effective control of the SARS-CoV2 virus that causes the COVID-19 pandemic requires both vaccines and antivirals. Antivirals are particularly crucial to treat infected people during the period of time that an effective vaccine is being developed and deployed. Because the development of specific antiviral drugs can take a considerable length of time, an important approach is to identify existing drugs already approved for use in humans which could be repurposed as COVID-19 therapeutics. Here we focus on antivirals directed against the SARS-CoV2 Mpro protease, which is required for virus replication. A structural similarity search showed that the Hepatitis C virus (HCV) NS3/4A protease has a striking three-dimensional structural similarity to the SARS-CoV2 Mpro protease, particularly in the arrangement of key active site residues. We used virtual docking predictions to assess the hypothesis that existing drugs already approved for human use or clinical testing that are directed at the HCV NS3/4A protease might fit well into the active-site cleft of the SARS-CoV2 protease (Mpro). AutoDock docking scores for 12 HCV protease inhibitors and 9 HIV-1 protease inhibitors were determined and compared to the docking scores for an α-ketoamide inhibitor of Mpro, which has recently been shown to inhibit SARS-CoV2 virus replication in cell culture. We identified eight HCV protease inhibitors that bound to the Mpro active site with higher docking scores than the α-ketoamide inhibitor, suggesting that these protease inhibitors may effectively bind to the Mpro active site. These results provide the rationale for us to test the identified HCV protease inhibitors as inhibitors of the SARS-CoV2 protease, and as inhibitors of SARS-CoV2 virus replication. Subsequently these repurposed drugs could be evaluated as COVID-19 therapeutics.

Structural Similarity of SARS-CoV2 Mpro and HCV NS3/4A Proteases Suggests New Approaches for Identifying Existing Drugs Useful as COVID-19 Therapeutics

During the current COVID-19 pandemic more than 160,000 people have died worldwide as of mid-April 2020, and the global economy has been crippled. Effective control of the SARS-CoV2 virus that causes the COVID-19 pandemic requires both vaccines and antivirals. Antivirals are particularly crucial to treat infected people during the period of time that an effective vaccine is being developed and deployed. Because the development of specific antiviral drugs can take a considerable length of time, an important approach is to identify existing drugs already approved for use in humans which could be repurposed as COVID-19 therapeutics. Here we focus on antivirals directed against the SARS-CoV2 Mpro protease, which is required for virus replication. A structural similarity search showed that the Hepatitis C virus (HCV) NS3/4A protease has a striking three-dimensional structural similarity to the SARS-CoV2 Mpro protease, particularly in the arrangement of key active site residues. We used virtual docking predictions to assess the hypothesis that existing drugs already approved for human use or clinical testing that are directed at the HCV NS3/4A protease might fit well into the active-site cleft of the SARS-CoV2 protease (Mpro). AutoDock docking scores for 12 HCV protease inhibitors and 9 HIV-1 protease inhibitors were determined and compared to the docking scores for an α-ketoamide inhibitor of Mpro, which has recently been shown to inhibit SARS-CoV2 virus replication in cell culture. We identified eight HCV protease inhibitors that bound to the Mpro active site with higher docking scores than the α-ketoamide inhibitor, suggesting that these protease inhibitors may effectively bind to the Mpro active site. These results provide the rationale for us to test the identified HCV protease inhibitors as inhibitors of the SARS-CoV2 protease, and as inhibitors of SARS-CoV2 virus replication. Subsequently these repurposed drugs could be evaluated as COVID-19 therapeutics.

Structural Similarity of SARS-CoV2 Mpro and HCV NS3/4A Proteases Suggests New Approaches for Identifying Existing Drugs Useful as COVID-19 Therapeutics

During the current COVID-19 pandemic more than 160,000 people have died worldwide as of mid-April 2020, and the global economy has been crippled. Effective control of the SARS-CoV2 virus that causes the COVID-19 pandemic requires both vaccines and antivirals. Antivirals are particularly crucial to treat infected people during the period of time that an effective vaccine is being developed and deployed. Because the development of specific antiviral drugs can take a considerable length of time, an important approach is to identify existing drugs already approved for use in humans which could be repurposed as COVID-19 therapeutics. Here we focus on antivirals directed against the SARS-CoV2 M^pro protease, which is required for virus replication. A structural similarity search showed that the Hepatitis C virus (HCV) NS3/4A protease has a striking three-dimensional structural similarity to the SARS-CoV2 M^pro protease, particularly in the arrangement of key active site residues. We used virtual docking predictions to assess the hypothesis that existing drugs already approved for human use or clinical testing that are directed at the HCV NS3/4A protease might fit well into the active-site cleft of the SARS-CoV2 protease (M^pro). AutoDock docking scores for 12 HCV protease inhibitors and 9 HIV-1 protease inhibitors were determined and compared to the docking scores for an α-ketoamide inhibitor of M^pro, which has recently been shown to inhibit SARS-CoV2 virus replication in cell culture. We identified eight HCV protease inhibitors that bound to the M^pro active site with higher docking scores than the α-ketoamide inhibitor, suggesting that these protease inhibitors may effectively bind to the M^pro active site. These results provide the rationale for us to test the identified HCV protease inhibitors as inhibitors of the SARS-CoV2 protease, and as inhibitors of SARS-CoV2 virus replication. Subsequently these repurposed drugs could be evaluated as COVID-19 therapeutics.

Structural Similarity of SARS-CoV2 Mpro and HCV NS3/4A Proteases Suggests New Approaches for Identifying Existing Drugs Useful as COVID-19 Therapeutics

During the current COVID-19 pandemic more than 160,000 people have died worldwide as of mid-April 2020, and the global economy has been crippled. Effective control of the SARS-CoV2 virus that causes the COVID-19 pandemic requires both vaccines and antivirals. Antivirals are particularly crucial to treat infected people during the period of time that an effective vaccine is being developed and deployed. Because the development of specific antiviral drugs can take a considerable length of time, an important approach is to identify existing drugs already approved for use in humans which could be repurposed as COVID-19 therapeutics. Here we focus on antivirals directed against the SARS-CoV2 M^pro protease, which is required for virus replication. A structural similarity search showed that the Hepatitis C virus (HCV) NS3/4A protease has a striking three-dimensional structural similarity to the SARS-CoV2 M^pro protease, particularly in the arrangement of key active site residues. We used virtual docking predictions to assess the hypothesis that existing drugs already approved for human use or clinical testing that are directed at the HCV NS3/4A protease might fit well into the active-site cleft of the SARS-CoV2 protease (M^pro). AutoDock docking scores for 12 HCV protease inhibitors and 9 HIV-1 protease inhibitors were determined and compared to the docking scores for an α-ketoamide inhibitor of M^pro, which has recently been shown to inhibit SARS-CoV2 virus replication in cell culture. We identified eight HCV protease inhibitors that bound to the M^pro active site with higher docking scores than the α-ketoamide inhibitor, suggesting that these protease inhibitors may effectively bind to the M^pro active site. These results provide the rationale for us to test the identified HCV protease inhibitors as inhibitors of the SARS-CoV2 protease, and as inhibitors of SARS-CoV2 virus replication. Subsequently these repurposed drugs could be evaluated as COVID-19 therapeutics.

Molecular docking analysis of apigenin and quercetin from ethylacetate fraction of Adansonia digitata with malaria-associated calcium transport protein: An in silico approach

Background

The investigation and knowledge of calcium handling mechanisms in the plasmodium has been considered as a potential biological target against malaria.

Objective

This study deals with the evaluation of inhibitory activity of secondary metabolites of ethylacetate partitioned-fraction of Adansonia digitata stem bark extract on malaria-associated protein using in silico docking studies.

Materials and methods

Molecular docking and virtual screening was performed to understand the mechanism of ligand binding and to identify potent calcium transporter inhibitors. The stem bark extracts of A. digitata contains rich sources of phytochemicals. The secondary metabolites were determined by HPLC-DAD and HRGC-MS analysis. The major chemical constituent present in the ethylacetate partitioned-fraction of A. digitata stem bark extract were examined for their antiplasmodial activity and were also involved in docking study.

Results

The secondary metabolites, quercetin and apigenin inhibited the formation of β-hematin. The results showed that all the selected compounds in the A. digitata showed binding energy ranging between -6.5 kcal/mol and -7.1 kcal/mol. Among the two chemical constituents, apigenin has the highest docking score along with the highest number of hydrogen bonds formed when compared to quercetin. Analysis of the results suggests that apigenin and quercetin could act as an anti-malaria agent.

Conclusion

Molecular docking analysis could lead to further development of potent calcium transporter inhibitors for the prevention and treatment of malaria and related conditions.

A Multi-Label Learning Framework for Drug Repurposing

Drug repurposing plays an important role in screening old drugs for new therapeutic efficacy. The existing methods commonly treat prediction of drug-target interaction as a problem of binary classification, in which a large number of randomly sampled drug-target pairs accounting for over 50% of the entire training dataset are necessarily required. Such a large number of negative examples that do not come from experimental observations inevitably decrease the credibility of predictions. In this study, we propose a multi-label learning framework to find new uses for old drugs and discover new drugs for known target genes. In the framework, each drug is treated as a class label and its target genes are treated as the class-specific training data to train a supervised learning model of l₂-regularized logistic regression. As such, the inter-drug associations are explicitly modelled into the framework and all the class-specific training data come from experimental observations. In addition, the data constraint is less demanding, for instance, the chemical substructures of a drug are no longer needed and the novel target genes are inferred only from the underlying patterns of the known genes targeted by the drug. Stratified multi-label cross-validation shows that 84.9% of known target genes have at least one drug correctly recognized, and the proposed framework correctly recognizes 86.73% of the independent test drug-target interactions (DTIs) from DrugBank. These results show that the proposed framework could generalize well in the large drug/class space without the information of drug chemical structures and target protein structures. Furthermore, we use the trained model to predict new drugs for the known target genes, identify new genes for the old drugs, and infer new associations between old drugs and new disease phenotypes via the OMIM database. Gene ontology (GO) enrichment analyses and the disease associations reported in recent literature provide supporting evidences to the computational results, which potentially shed light on new clinical therapies for new and/or old disease phenotypes.

From biomedicinal to in silico models and back to therapeutics: a review on the advancement of peptidic modeling

Bioactive peptides participate in numerous metabolic functions of living organisms and have emerged as potential therapeutics on a diverse range of diseases. Albeit peptide design does not go without challenges, overwhelming advancements on in silico methodologies have increased the scope of peptide-based drug design and discovery to an unprecedented amount. Within an in silico model versus an experimental validation scenario, this review aims to summarize and discuss how different in silico techniques contribute at present to the design of peptide-based molecules. Published in silico results from 2014 to 2018 were selected and discriminated in major methodological groups, allowing a transversal analysis, promoting a landscape vision and asserting its increasing value in drug design.

Crystal structure of yeast xylose reductase in complex with a novel NADP-DTT adduct provides insights into substrate recognition and catalysis

Aldose reductases (ARs) belonging to the aldo-keto reductase (AKR) superfamily catalyze the conversion of carbonyl substrates into their respective alcohols. Here we report the crystal structures of the yeast Debaryomyces nepalensis xylose reductase (DnXR, AKR2B10) in the apo form and as a ternary complex with a novel NADP-DTT adduct. Xylose reductase, a key enzyme in the conversion of xylose to xylitol, has several industrial applications. The enzyme displayed the highest catalytic efficiency for l-threose (138 ± 7 mm^-1 ·s^-1 ) followed by d-erythrose (30 ± 3 mm^-1 ·s^-1 ). The crystal structure of the complex reveals a covalent linkage between the C4N atom of the nicotinamide ring of the cosubstrate and the S1 sulfur atom of DTT and provides the first structural evidence for a protein mediated NADP-low-molecular-mass thiol adduct. We hypothesize that the formation of the adduct is facilitated by an in-crystallo Michael addition of the DTT thiolate to the specific conformation of bound NADPH in the active site of DnXR. The interactions between DTT, a four-carbon sugar alcohol analog, and the enzyme are representative of a near-cognate product ternary complex and provide significant insights into the structural basis of aldose binding and specificity and the catalytic mechanism of ARs. DATABASE: Structural data are available in the PDB under the accession numbers 5ZCI and 5ZCM.

Synthesis and antibacterial evaluation of 13-substituted cycloberberine derivatives as a novel class of anti-MRSA agents

A series of new 13-substituted cycloberberine (CBBR) derivatives were prepared and evaluated for their antibacterial activities against Gram-positive bacteria taking CBBR as the lead. Structure-activity relationship revealed that the introduction of a suitable electron-donating group at the 13-position in CBBR might be beneficial for the antibacterial potency. Among them, compounds 5b and 5w exhibited high potency against methicillin-sensitive (MSSA) and resistant strains of S. aureus (MRSA) with MIC values of 1-4 μg/mL. Both of them also displayed high stabilities in blood, and good in vivo safety profiles with LD₅₀ values of 65.6 and 41.2 mg kg^-1 in intravenous route respectively. Molecular docking analysis indicated that compound 5b might target FtsZ protein that could inhibit cell division, with the advantage of activity against multidrug resistant S. aureus. Therefore, we consider 13-substituted CBBR derivatives to be a novel class of anti-MRSA agents worthy of further investigation.

Proposing drug fragments for dengue virus NS5 protein

Dengue fever is a febrile illness caused by Dengue Virus, which belongs to the Flaviviridae family. Among its proteome, the nonstructural protein 5 (NS5) is the biggest and most conserved. It has a primer-independent RNA-dependent RNA polymerase (RdRp) domain at its C-Terminus. Zou et al. studied the biological relevance of the two conserved cavities (named A and B) within the NS5 proteins of dengue virus (DENV) and West Nile Virus (WNV) using mutagenesis and revertant analysis and found four mutations located at cavity B having effects on viral replication. They recommended Cavity B, but not Cavity A as a potential target for drugs against flavivirus RdRp. In this study, we virtually screened the MayBridge drug fragments dataset for potential small molecule binders of cavity B using both AutoDock Vina, the standard docking tool, and QuickVina 2, our previously developed tool. We selected 16 fragments that appeared in the top 100 docking results of each of the representative structures of NS5. Visual inspection suggests that they have reasonable binding poses. The 16 predicted fragments are plausible drug candidates and should be considered for further validation, optimization, and linking to come up with a suitable inhibitor of dengue virus.

Translational -omics: Future potential and current challenges in precision medicine

Rapid advances in computational science and biotechnology are paving the way for precision medicine - a vision in next-generation healthcare that promises to provide a care package uniquely tailored to each individual's molecular make-up. Until relatively recently, the focus has been firmly centred around the genome; however, over the past two decades there has been a surge in the study of molecular activity within other biological domains (proteome/transcriptome/metabolome) involved in health and pathogenesis. The term '-omics' is broadly applied to these disciplines and 'translational -omics' refers to clinical utilisation of data derived from these scientific approaches. Translational -omics represents the cornerstone of the precision medicine initiative and offers positively disruptive solutions in global healthcare from a humanitarian, scientific and economic standpoint. However, there are unique challenges anticipated for all stakeholders within the precision medicine community, and addressing these early on in the adoption of precision approaches is critical. Herein, we outline the potential for translational -omics in precision medicine, highlight key roadblocks to successful implementation and propose potential solutions to current and expected problems.

Binding-Site Compatible Fragment Growing Applied to the Design of β2-Adrenergic Receptor Ligands

Fragment-based drug discovery is intimately linked to fragment extension approaches that can be accelerated using software for de novo design. Although computers allow for the facile generation of millions of suggestions, synthetic feasibility is however often neglected. In this study we computationally extended, chemically synthesized, and experimentally assayed new ligands for the β₂-adrenergic receptor (β₂AR) by growing fragment-sized ligands. In order to address the synthetic tractability issue, our in silico workflow aims at derivatized products based on robust organic reactions. The study started from the predicted binding modes of five fragments. We suggested a total of eight diverse extensions that were easily synthesized, and further assays showed that four products had an improved affinity (up to 40-fold) compared to their respective initial fragment. The described workflow, which we call "growing via merging" and for which the key tools are available online, can improve early fragment-based drug discovery projects, making it a useful creative tool for medicinal chemists during structure-activity relationship (SAR) studies.

Computational drug repositioning for rare diseases in the era of precision medicine

There are tremendous unmet needs in drug development for rare diseases. Computational drug repositioning is a promising approach and has been successfully applied to the development of treatments for diseases. However, how to utilize this knowledge and effectively conduct and implement computational drug repositioning approaches for rare disease therapies is still an open issue. Here, we focus on the means of utilizing accumulated genomic data for accelerating and facilitating drug repositioning for rare diseases. First, we summarize the current genome landscape of rare diseases. Second, we propose several promising bioinformatics approaches and pipelines for computational drug repositioning for rare diseases. Finally, we discuss recent regulatory incentives and other enablers in rare disease drug development and outline the remaining challenges.

Drug repositioning for enzyme modulator based on human metabolite-likeness

BACKGROUND

Recently, the metabolite-likeness of the drug space has emerged and has opened a new possibility for exploring human metabolite-like candidates in drug discovery. However, the applicability of metabolite-likeness in drug discovery has been largely unexplored. Moreover, there are no reports on its applications for the repositioning of drugs to possible enzyme modulators, although enzyme-drug relations could be directly inferred from the similarity relationships between enzyme's metabolites and drugs.

METHODS

We constructed a drug-metabolite structural similarity matrix, which contains 1,861 FDA-approved drugs and 1,110 human intermediary metabolites scored with the Tanimoto similarity. To verify the metabolite-likeness measure for drug repositioning, we analyzed 17 known antimetabolite drugs that resemble the innate metabolites of their eleven target enzymes as the gold standard positives. Highly scored drugs were selected as possible modulators of enzymes for their corresponding metabolites. Then, we assessed the performance of metabolite-likeness with a receiver operating characteristic analysis and compared it with other drug-target prediction methods. We set the similarity threshold for drug repositioning candidates of new enzyme modulators based on maximization of the Youden's index. We also carried out literature surveys for supporting the drug repositioning results based on the metabolite-likeness.

RESULTS

In this paper, we applied metabolite-likeness to repurpose FDA-approved drugs to disease-associated enzyme modulators that resemble human innate metabolites. All antimetabolite drugs were mapped with their known 11 target enzymes with statistically significant similarity values to the corresponding metabolites. The comparison with other drug-target prediction methods showed the higher performance of metabolite-likeness for predicting enzyme modulators. After that, the drugs scored higher than similarity score of 0.654 were selected as possible modulators of enzymes for their corresponding metabolites. In addition, we showed that drug repositioning results of 10 enzymes were concordant with the literature evidence.

CONCLUSIONS

This study introduced a method to predict the repositioning of known drugs to possible modulators of disease associated enzymes using human metabolite-likeness. We demonstrated that this approach works correctly with known antimetabolite drugs and showed that the proposed method has better performance compared to other drug target prediction methods in terms of enzyme modulators prediction. This study as a proof-of-concept showed how to apply metabolite-likeness to drug repositioning as well as potential in further expansion as we acquire more disease associated metabolite-target protein relations.

Modification of S-Adenosyl-l-Homocysteine as Inhibitor of Nonstructural Protein 5 Methyltransferase Dengue Virus Through Molecular Docking and Molecular Dynamics Simulation

Dengue fever is still a major threat worldwide, approximately threatening two-fifths of the world's population in tropical and subtropical countries. Nonstructural protein 5 (NS5) methyltransferase enzyme plays a vital role in the process of messenger RNA capping of dengue by transferring methyl groups from S-adenosyl-l-methionine to N7 atom of the guanine bases of RNA and the RNA ribose group of 2'OH, resulting in S-adenosyl-l-homocysteine (SAH). The modification of SAH compound was screened using molecular docking and molecular dynamics simulation, along with computational ADME-Tox (absorption, distribution, metabolism, excretion, and toxicity) test. The 2 simulations were performed using Molecular Operating Environment (MOE) 2008.10 software, whereas the ADME-Tox test was performed using various software. The modification of SAH compound was done using several functional groups that possess different polarities and properties, resulting in 3460 ligands to be docked. After conducting docking simulation, we earned 3 best ligands (SAH-M331, SAH-M2696, and SAH-M1356) based on ΔG_binding and molecular interactions, which show better results than the standard ligands. Moreover, the results of molecular dynamics simulation show that the best ligands are still able to maintain the active site residue interaction with the binding site until the end of the simulation. After a series of molecular docking and molecular dynamics simulation were performed, we concluded that SAH-M1356 ligand is the most potential SAH-based compound to inhibit NS5 methyltransferase enzyme for treating dengue fever.

Fragment Database FDB-17

To better understand chemical space we recently enumerated the database GDB-17 containing 166.4 billion possible molecules up to 17 atoms of C, N, O, S and halogen following the simple rules of chemical stability and synthetic feasibility. However, due to the combinatorial explosion caused by systematic enumeration GDB-17 is strongly biased toward the largest, functionally and stereochemically most complex molecules and far too large for most virtual screening tools. Herein we selected a much smaller subset of GDB-17, called the fragment database FDB-17, which contains 10 million fragmentlike molecules evenly covering a broad value range for molecular size, polarity, and stereochemical complexity. The database is available at www.gdb.unibe.ch for download and free use, together with an interactive visualization application and a Web-based nearest neighbor search tool to facilitate the selection of new fragment-sized molecules for chemical synthesis.

A comparative molecular docking study of curcumin and methotrexate to dihydrofolate reductase

Interaction of curcumin (CUR) with the enzyme dihydrofolate reductase (DHFR) was studied by molecular docking using AutoDock 4.2 as the docking software application. AutoDock 4.2 software serves as a valid and acceptable docking application to study the interactions of small compounds with proteins. Interactions of curcumin with DHFR were compared to those of methotrexate (MTX), a known inhibitor of the enzyme. The calculated free energy of binding (ΔG binding) shows that curcumin (ΔG = -9.02 kcal/mol; Ki = 243 nM) binds with affinity comparable to or better than MTX (ΔG = -8.78 kcal/mol; Ki = 363 nM). Binding interactions of curcumin with active site residues of the enzyme are also predicted. Curcumin appears to bind in a bent conformation making extensive VDW contacts in the active site of the enzyme. Hydrogen bonding and pi-pi interaction with key active site residues are also observed. Thus, curcumin can be considered as a good lead compound in the development of new inhibitors of DHFR, which is a potential target of anti-cancer drugs. The results of these studies can serve as a starting point for further computational and experimental studies.

WebMolCS: A Web-Based Interface for Visualizing Molecules in Three-Dimensional Chemical Spaces

The concept of chemical space provides a convenient framework to analyze large collections of molecules by placing them in property spaces where distances represent similarities. Here we report webMolCS, a new type of web-based interface visualizing up to 5000 user-defined molecules in six different three-dimensional (3D) chemical spaces obtained by principal component analysis or similarity mapping of multidimensional property spaces describing composition (MQN: 42D molecular quantum numbers, SMIfp: 34D SMILES fingerprint), shapes and pharmacophores (APfp: 20D atom pair fingerprint, Xfp: 55D category extended atom pair fingerprint), and substructures (Sfp: 1024D binary substructure fingerprint, ECfp4:1024D extended connectivity fingerprint). Each molecule is shown as a sphere, and its structure appears on mouse over. The sphere is color-coded by similarity to the first compound in the list, by the list rank, or by a user-defined value, which reveals the relationship between any property encoded by these values and structural similarities. WebMolCS is freely available at www.gdb.unibe.ch .

Medicinal Chemistry Profiling of Monocyclic 1,2-Azaborines

The first examples of biologically active monocyclic 1,2-azaborines have been synthesized and demonstrated to exhibit not only improved in vitro aqueous solubility in comparison with their corresponding carbonaceous analogues, but in the context of a CDK2 inhibitor, also improved biological activity and better in vivo oral bioavailability. This proof-of-concept study establishes the viability of monocyclic 1,2-azaborines as a novel pharmacophore with distinct pharmacological profiles that can help address challenges associated with solubility in drug development research.

Ligand and Target Discovery by Fragment-Based Screening in Human Cells

Advances in the synthesis and screening of small-molecule libraries have accelerated the discovery of chemical probes for studying biological processes. Still, only a small fraction of the human proteome has chemical ligands. Here, we describe a platform that marries fragment-based ligand discovery with quantitative chemical proteomics to map thousands of reversible small molecule-protein interactions directly in human cells, many of which can be site-specifically determined. We show that fragment hits can be advanced to furnish selective ligands that affect the activity of proteins heretofore lacking chemical probes. We further combine fragment-based chemical proteomics with phenotypic screening to identify small molecules that promote adipocyte differentiation by engaging the poorly characterized membrane protein PGRMC2. Fragment-based screening in human cells thus provides an extensive proteome-wide map of protein ligandability and facilitates the coordinated discovery of bioactive small molecules and their molecular targets.

Regulation of Virulence in Staphylococcus aureus: Molecular Mechanisms and Remaining Puzzles

The agr locus encodes a quorum-sensing (QS) circuit required for the virulence of a spectrum of Gram-positive pathogens and is, therefore, regarded as an important target for the development of chemotherapeutics. In recent years, many of the biochemical events in the Staphylococcus aureus agr circuit have been reconstituted and subject to quantitative analysis in vitro. This work, in conjunction with structural studies on several key players in the signaling circuit, has furnished mechanistic insights into the regulation and evolution of the agr QS system. Here, we review this progress and discuss the remaining open questions in the area. We also highlight advances in the discovery of small-molecule agr modulators and how the newly available biochemical and structural information might be leveraged for the design of next-generation therapeutics targeting the agr system.

Application of computational methods for anticancer drug discovery, design, and optimization

Developing a novel drug is a complex, risky, expensive and time-consuming venture. It is estimated that the conventional drug discovery process ending with a new medicine ready for the market can take up to 15 years and more than a billion USD. Fortunately, this scenario has recently changed with the arrival of new approaches. Many novel technologies and methodologies have been developed to increase the efficiency of the drug discovery process, and computational methodologies have become a crucial component of many drug discovery programs. From hit identification to lead optimization, techniques such as ligand- or structure-based virtual screening are widely used in many discovery efforts. It is the case for designing potential anticancer drugs and drug candidates, where these computational approaches have had a major impact over the years and have provided fruitful insights into the field of cancer. In this paper, we review the concept of rational design presenting some of the most representative examples of molecules identified by means of it. Key principles are illustrated through case studies including specifically successful achievements in the field of anticancer drug design to demonstrate that research advances, with the aid of in silico drug design, have the potential to create novel anticancer drugs.

Application of computational methods for anticancer drug discovery, design, and optimization

Developing a novel drug is a complex, risky, expensive and time-consuming venture. It is estimated that the conventional drug discovery process ending with a new medicine ready for the market can take up to 15 years and more than a billion USD. Fortunately, this scenario has recently changed with the arrival of new approaches. Many novel technologies and methodologies have been developed to increase the efficiency of the drug discovery process, and computational methodologies have become a crucial component of many drug discovery programs. From hit identification to lead optimization, techniques such as ligand- or structure-based virtual screening are widely used in many discovery efforts. It is the case for designing potential anticancer drugs and drug candidates, where these computational approaches have had a major impact over the years and have provided fruitful insights into the field of cancer. In this paper, we review the concept of rational design presenting some of the most representative examples of molecules identified by means of it. Key principles are illustrated through case studies including specifically successful achievements in the field of anticancer drug design to demonstrate that research advances, with the aid of in silico drug design, have the potential to create novel anticancer drugs.