A virtual screen discovers novel, fragment-sized inhibitors of Mycobacterium tuberculosis InhA

Latent tuberculosis and computational biology: A less-talked affair

Tuberculosis (TB) is a pervasive and devastating air-borne disease caused by the organisms belonging to the Mycobacterium tuberculosis (Mtb) complex. Currently, it is the global leader in infectious disease-related death in adults. The proclivity of TB to enter the latent state has become a significant impediment to the global effort to eradicate TB. Despite decades of research, latent tuberculosis (LTB) mechanisms remain poorly understood, making it difficult to develop efficient treatment methods. In this review, we seek to shed light on the current understanding of the mechanism of LTB, with an accentuation on the insights gained through computational biology. We have outlined various well-established computational biology components, such as omics, network-based techniques, mathematical modelling, artificial intelligence, and molecular docking, to disclose the crucial facets of LTB. Additionally, we highlighted important tools and software that may be used to conduct a variety of systems biology assessments. Finally, we conclude the article by addressing the possible future directions in this field, which might help a better understanding of LTB progression.

Discovery of New Zika Protease and Polymerase Inhibitors through the Open Science Collaboration Project OpenZika

The Zika virus (ZIKV) is a neurotropic arbovirus considered a global threat to public health. Although there have been several efforts in drug discovery projects for ZIKV in recent years, there are still no antiviral drugs approved to date. Here, we describe the results of a global collaborative crowdsourced open science project, the OpenZika project, from IBM's World Community Grid (WCG), which integrates different computational and experimental strategies for advancing a drug candidate for ZIKV. Initially, molecular docking protocols were developed to identify potential inhibitors of ZIKV NS5 RNA-dependent RNA polymerase (NS5 RdRp), NS3 protease (NS2B-NS3pro), and NS3 helicase (NS3hel). Then, a machine learning (ML) model was built to distinguish active vs inactive compounds for the cytoprotective effect against ZIKV infection. We performed three independent target-based virtual screening campaigns (NS5 RdRp, NS2B-NS3pro, and NS3hel), followed by predictions by the ML model and other filters, and prioritized a total of 61 compounds for further testing in enzymatic and phenotypic assays. This yielded five non-nucleoside compounds which showed inhibitory activity against ZIKV NS5 RdRp in enzymatic assays (IC50 range from 0.61 to 17 μM). Two compounds thermally destabilized NS3hel and showed binding affinity in the micromolar range (Kd range from 9 to 35 μM). Moreover, the compounds LabMol-301 inhibited both NS5 RdRp and NS2B-NS3pro (IC50 of 0.8 and 7.4 μM, respectively) and LabMol-212 thermally destabilized the ZIKV NS3hel (Kd of 35 μM). Both also protected cells from death induced by ZIKV infection in in vitro cell-based assays. However, while eight compounds (including LabMol-301 and LabMol-212) showed a cytoprotective effect and prevented ZIKV-induced cell death, agreeing with our ML model for prediction of this cytoprotective effect, no compound showed a direct antiviral effect against ZIKV. Thus, the new scaffolds discovered here are promising hits for future structural optimization and for advancing the discovery of further drug candidates for ZIKV. Furthermore, this work has demonstrated the importance of the integration of computational and experimental approaches, as well as the potential of large-scale collaborative networks to advance drug discovery projects for neglected diseases and emerging viruses, despite the lack of available direct antiviral activity and cytoprotective effect data, that reflects on the assertiveness of the computational predictions. The importance of these efforts rests with the need to be prepared for future viral epidemic and pandemic outbreaks.

Design, synthesis and molecular docking studies of imidazole and benzimidazole linked ethionamide derivatives as inhibitors of InhA and antituberculosis agents

To explore effective antituberculosis agents, a new class of imidazoles and benzimidazoles linked ethionamide analogs were designed and synthesized. The elemental analysis, ¹H NMR, ¹³C NMR and mass spectral data were used to characterize all of the novel analogs. In vitro activity against Mycobacterium tuberculosis (Mtb) H37Rv was assessed for all of the target compounds. The hydroxy and nitrile moieties on the imidazole ring, as well as the hydroxy and methoxy groups on the benzimidazole ring connected to the ethionamide side chain, were shown to be advantageous. In our cell viability experiment against the Vero cell line, all of the compounds were non-cytotoxic even at 100 μM. To confirm the powerful analogs target identification, we investigated their in vitro inhibitory action on an M. tuberculosis InhA over-expressing (Mtb InhA-OE) strain, which yielded MICs nearly twice those of the Mtb H37Rv strain. Furthermore, the results of molecular docking confirmed the experimental findings. Additionally, the molecules were evaluated in silico for ADMET and drug similarity features. The experimental observation enables the newly generated ethionamide derivatives to be attractive candidates for the creation of newer and better anti-TB agents.

Structure-based in silico approaches for drug discovery against Mycobacterium tuberculosis

Mycobacterium tuberculosis is the causative agent of TB and was estimated to cause 1.4 million death in 2019, alongside 10 million new infections. Drug resistance is a growing issue, with multi-drug resistant infections representing 3.3% of all new infections, hence novel antimycobacterial drugs are urgently required to combat this growing health emergency. Alongside this, increased knowledge of gene essentiality in the pathogenic organism and larger compound databases can aid in the discovery of new drug compounds. The number of protein structures, X-ray based and modelled, is increasing and now accounts for greater than > 80% of all predicted M. tuberculosis proteins; allowing novel targets to be investigated. This review will focus on structure-based in silico approaches for drug discovery, covering a range of complexities and computational demands, with associated antimycobacterial examples. This includes molecular docking, molecular dynamic simulations, ensemble docking and free energy calculations. Applications of machine learning onto each of these approaches will be discussed. The need for experimental validation of computational hits is an essential component, which is unfortunately missing from many current studies. The future outlooks of these approaches will also be discussed.

Decision Making in Structure-Based Drug Discovery: Visual Inspection of Docking Results

Molecular docking is a computational method widely used in drug discovery. Due to the inherent inaccuracies of molecular docking, visual inspection of binding modes is a crucial routine in the decision making process of computational medicinal chemists. Despite its apparent importance for medicinal chemistry projects, guidelines for the visual docking pose assessment have been hardly discussed in the literature. Here, we review the medicinal chemistry literature with the aim of identifying consistent principles for visual inspection, highlighting cases of its successful application, and discussing its limitations. In this context, we conducted a survey reaching experts in both academia and the pharmaceutical industry, which also included a challenge to distinguish native from incorrect poses. We were able to collect 93 expert opinions that offer valuable insights into visually supported decision-making processes. This perspective shall motivate discussions among experienced computational medicinal chemists and guide young scientists new to the field to stratify their compounds.

Pangenome Analysis of Mycobacterium tuberculosis Reveals Core-Drug Targets and Screening of Promising Lead Compounds for Drug Discovery

Tuberculosis, caused by Mycobacterium tuberculosis (M. tuberculosis), is one of the leading causes of human deaths globally according to the WHO TB 2019 report. The continuous rise in multi- and extensive-drug resistance in M. tuberculosis broadens the challenges to control tuberculosis. The availability of a large number of completely sequenced genomes of M. tuberculosis has provided an opportunity to explore the pangenome of the species along with the pan-phylogeny and to identify potential novel drug targets leading to drug discovery. We attempt to calculate the pangenome of M. tuberculosis that comprises a total of 150 complete genomes and performed the phylo-genomic classification and analysis. Further, the conserved core genome (1251 proteins) is subjected to various sequential filters (non-human homology, essentiality, virulence, physicochemical parameters, and pathway analysis) resulted in identification of eight putative broad-spectrum drug targets. Upon molecular docking analyses of these targets with ligands available at the DrugBank database shortlisted a total of five promising ligands with projected inhibitory potential; namely, 2′deoxy-thymidine-5′-diphospho-alpha-d-glucose, uridine diphosphate glucose, 2′-deoxy-thymidine-beta-l-rhamnose, thymidine-5′-triphosphate, and citicoline. We are confident that with further lead optimization and experimental validation, these lead compounds may provide a sound basis to develop safe and effective drugs against tuberculosis disease in humans.

Synthesis and Antimycobacterial Activity of 2,5-Disubstituted and 1,2,5-Trisubstituted Benzimidazoles

The appearance of drug-resistant strains of Mycobacterium tuberculosis and the dramatic increase in infection rates worldwide evidences the urgency of developing new and effective compounds for treating tuberculosis. Benzimidazoles represent one possible source of new compounds given that antimycobacterial activity has already been documented for some derivatives, such as those bearing electron-withdrawing groups. The aim of this study was to synthesize two series of benzimidazoles, di- and trisubstituted derivatives, and evaluate their antimycobacterial activity. Accordingly, 5a and 5b were synthesized from hydroxymoyl halides 3a and 3b, and nitro-substituted o-phenylenediamine 4. Compound 11 was synthesized from an aromatic nitro compound, 4-chloro-1,2-phenylenediamine 9, mixed with 3-nitrobenzaldehyde 10, and bentonite clay. Although the synthesis of 11 has already been reported, its antimycobacterial activity is herein examined for the first time. 1,2,5-trisubstituted benzimidazoles 7a, 7b, and 12 were obtained from N-alkylation of 5a, 5b, and 11. All benzimidazole derivatives were characterized by FT-IR, NMR, and HR-MS, and then screened for their in vitro antimycobacterial effect against the M. tuberculosis H37Rv strain. The N-alkylated molecules (7a, 7b, and 12) generated very limited in vitro inhibition of mycobacterial growth. The benzimidazoles (5a, 5b, and 11) showed in vitro potency against mycobacteria, reflected in minimal inhibitory concentration (MIC) values in the range of 6.25-25 μg/mL. Consequently, only the 2,5-disubstituted benzimidazoles were assessed for biological activity on mouse macrophages infected with M. tuberculosis. A good effect was found for the three compounds. The cytotoxicity assay revealed very low toxicity for all the test compounds against the macrophage cell line. According to the docking study, 2,5-disubstituted benzimidazoles exhibit high affinity for an interdomain cleft that plays a key role in the GTP-dependent polymerization of the filamentous temperature-sensitive Z (FtsZ) protein. The ability of different benzimidazoles to impede FtsZ polymerization is reportedly related to their antimycobacterial activity. On the other hand, the 1,2,5-trisubstituted benzimidazoles docked to the N-terminal of the protein, close to the GTP binding domain, and did not show strong binding energies. Overall, 5a, 5b, and 11 proved to be good candidates for in vivo testing to determine their potential for treating tuberculosis.

Structural approaches to pathway-specific antimicrobial agents

This perspective provides an overview of the evolution of antibiotic discovery from a largely phenotypic-based effort, through an intensive structure-based design focus, to a more holistic approach today. The current focus on antibiotic development incorporates assay and discovery conditions that replicate the host environment as much as feasible. They also incorporate several strategies, including target identification and validation within the whole cell environment, a variety of target deconvolution methods, and continued refinement of structure-based design approaches.

Intrabacterial Metabolism Obscures the Successful Prediction of an InhA Inhibitor of Mycobacterium tuberculosis

Tuberculosis, caused by Mycobacterium tuberculosis (M. tuberculosis), kills 1.6 million people annually. To bridge the gap between structure- and cell-based drug discovery strategies, we are pioneering a computer-aided discovery paradigm that merges structure-based virtual screening with ligand-based, machine learning methods trained with cell-based data. This approach successfully identified N-(3-methoxyphenyl)-7-nitrobenzo[c][1,2,5]oxadiazol-4-amine (JSF-2164) as an inhibitor of purified InhA with whole-cell efficacy versus in vitro cultured M. tuberculosis. When the intrabacterial drug metabolism (IBDM) platform was leveraged, mechanistic studies demonstrated that JSF-2164 underwent a rapid F₄₂₀H₂-dependent biotransformation within M. tuberculosis to afford intrabacterial nitric oxide and two amines, identified as JSF-3616 and JSF-3617. Thus, metabolism of JSF-2164 obscured the InhA inhibition phenotype within cultured M. tuberculosis. This study demonstrates a new docking/Bayesian computational strategy to combine cell- and target-based drug screening and the need to probe intrabacterial metabolism when clarifying the antitubercular mechanism of action.

Molecular modeling of a series of dehydroquinate dehydratase type II inhibitors of Mycobacterium tuberculosis and design of new binders

Tuberculosis, caused by Mycobacterium tuberculosis (M. tuberculosis), is still responsible for a large number of fatal cases, especially in developing countries with alarming rates of incidence and prevalence worldwide. Mycobacterium tuberculosis has a remarkable ability to develop new resistance mechanisms to the conventional antimicrobials treatment. Because of this, there is an urgent need for novel bioactive compounds for its treatment. The dehydroquinate dehydratase II (DHQase II) is considered a key enzyme of shikimate pathway, and it can be used as a promising target for the design of new bioactive compounds with antibacterial action. The aim of this work was the construction of QSAR models to aid the design of new potential DHQase II inhibitors. For that purpose, various molecular modeling approaches, such as activity cliff, QSAR models and computer-aided ligand design were utilized. A predictive in silico 4D-QSAR model was built using a database comprising 86 inhibitors of DHQase II, and the model was used to predict the activity of the designed ligands. The obtained model proved to predict well the DHQase II inhibition for an external validation dataset ([Formula: see text] = 0.72). Also, the Activity Cliff analysis shed light on important structural features applied to the ligand design.

The TATA-binding Protein DNA-binding domain of eukaryotic parasites is a potentially druggable target

The TATA-binding protein (TBP) is a central transcription factor in eukaryotes that interacts with a large number of different transcription factors; thus, affecting these interactions will be lethal for any living being. In this work, we present the first structural and dynamic computational study of the surface properties of the TBP DNA-binding domain for a set of parasites involved in diseases of worldwide interest. The sequence and structural differences of these TBPs, as compared with human TBP, were proposed to select representative ensembles generated from molecular dynamics simulations and to evaluate their druggability by molecular ensemble-based docking of drug-like molecules. We found that potential druggable sites correspond to the NC2-binding site, N-terminal tail, H2 helix, and the interdomain region, with good selectivity for Plasmodium falciparum, Necator americanus, Entamoeba histolytica, Candida albicans, and Taenia solium TBPs. The best hit compounds share structural similarity among themselves and have predicted dissociation constants ranging from nM to μM. These can be proposed as initial scaffolds for experimental testing and further optimization. In light of the obtained results, we propose TBP as an attractive therapeutic target for treatment of parasitic diseases.

Virtual Screening of Henna Compounds Library for Discovery of New Leads against Human Thymidine Phosphorylase, an Overexpressed Factor of Hand-Foot Syndrome

Background:

Capecitabine is one of the most effective and successful drugs for the treatment of uterine and colorectal cancer which has been limited in use due to occurrence of handfoot syndrome (HFS). Overexpression of human thymidine phosphorylase enzyme is predicted to be one of the main causes of this syndrome. Thymidine phosphorylase enzyme is involved in many cancers and inflammatory diseases and pyrimidine nucleoside phosphorylase family is found in a variety of organisms. Results of clinical studies have shown that topical usage of henna plant (Lawsonia inermis from the family of Lythraceae) could reduce the severity of HFS.

Methods:

By using in silico methods on reported compounds of henna, the present study is aimed at finding phytochemicals and chemical groups with the potential to efficiently interact with and inhibit human thymidine phosphorylase. Various compounds (825) of henna from different chemical groups (138) were virtually screened by the interface to AutoDock in YASARA Software package, against the enzyme structure obtained from X-ray crystallography and refined by homology modeling methods.

Results:

By virtual screening, i.e. docking of candidate ligands into the determined active site of hTP, followed by applying the scoring function of binding affinity, 71 compounds (out of 825 compounds) were estimated to have the likelihood to bind to the protein with an interaction energy higher than 10 kcal/mol (Concerning the sign of “binding energies”, please refer to the Methods section).

Conclusion:

Finally, diosmetin-3'-O-β-D-glucopyranoside (#219) and monoglycosylated naphthalene were respectively selected as the most potent phytochemicals and chemical groups. Flavonoid-like compounds with appropriate interaction energy were also considered as the most probable inhibitors. More investigations on henna compounds, are needed in order to approve their effectiveness and also to explore more anti-cancer, anti-inflammatory, anti-angiogenesis and even antibiotics.

Computational Approaches as Rational Decision Support Systems for Discovering Next-Generation Antitubercular Agents: Mini-Review

Tuberculosis, malaria, dengue, chikungunya, leishmaniasis etc. are a large group of neglected tropical diseases that prevail in tropical and subtropical countries, affecting one billion people every year. Minimal funding and grants for research on these scientific problems challenge many researchers to find a different way to reduce the extensive time and cost involved in the drug discovery cycle of these problems. Computer-aided drug design techniques have already been proved successful in the discovery of new molecules rationally by reducing the time and cost involved in the development of drugs. In the current minireview, we are highlighting on the molecular modeling studies published during 2010-2018 for target specific antitubercular agents. This review includes the studies of Structure-Based (SB) and Ligand-Based (LB) modeling and those involving Machine Learning (ML) techniques against different antitubercular targets such as dihydrofolate reductase (DHFR), enoyl Acyl Carrier Protein (ACP) reductase (InhA), catalase-peroxidase (KatG), enzyme antigen 85C, protein tyrosine phosphatases (PtpA and PtpB), dUTPase, thioredoxin reductase (MtTrxR), etc. The information presented in this review will help the researchers to get acquainted with the recent progress in the modeling studies of antitubercular agents.

Mycobacterium tuberculosis serine/threonine protein kinases: structural information for the design of their specific ATP-competitive inhibitors

In the last decades, human protein kinases (PKs) have been relevant as targets in the development of novel therapies against many diseases, but the study of Mycobacterium tuberculosis PKs (MTPKs) involved in tuberculosis pathogenesis began much later and has not yet reached an advanced stage of development. To increase knowledge of these enzymes, in this work we studied the structural features of MTPKs, with focus on their ATP-binding sites and their interactions with inhibitors. PknA, PknB, and PknG are the most studied MTPKs, which were previously crystallized; ATP-competitive inhibitors have been designed against them in the last decade. In the current work, reported PknA, PknB, and PknG inhibitors were extracted from literature and their orientations inside the ATP-binding site were proposed by using docking method. With this information, interaction fingerprints were elaborated, which reveal the more relevant residues for establishing chemical interactions with inhibitors. The non-crystallized MTPKs PknD, PknF, PknH, PknJ, PknK, and PknL were also studied; their three-dimensional structural models were developed by using homology modeling. The main characteristics of MTPK ATP-binding sites (the non-crystallized and crystallized MTPKs, including PknE and PknI) were accounted; schemes of the main polar and nonpolar groups inside their ATP-binding sites were constructed, which are suitable for a major understanding of these proteins as antituberculotic targets. These schemes could be used for establishing comparisons between MTPKs and human PKs in order to increase selectivity of MTPK inhibitors. As a key tool for guiding medicinal chemists interested in the design of novel MTPK inhibitors, our work provides a map of the structural elements relevant for the design of more selective ATP-competitive MTPK inhibitors.

Comparing and Validating Machine Learning Models for Mycobacterium tuberculosis Drug Discovery

Tuberculosis is a global health dilemma. In 2016, the WHO reported 10.4 million incidences and 1.7 million deaths. The need to develop new treatments for those infected with Mycobacterium tuberculosis ( Mtb) has led to many large-scale phenotypic screens and many thousands of new active compounds identified in vitro. However, with limited funding, efforts to discover new active molecules against Mtb needs to be more efficient. Several computational machine learning approaches have been shown to have good enrichment and hit rates. We have curated small molecule Mtb data and developed new models with a total of 18,886 molecules with activity cutoffs of 10 μM, 1 μM, and 100 nM. These data sets were used to evaluate different machine learning methods (including deep learning) and metrics and to generate predictions for additional molecules published in 2017. One Mtb model, a combined in vitro and in vivo data Bayesian model at a 100 nM activity yielded the following metrics for 5-fold cross validation: accuracy = 0.88, precision = 0.22, recall = 0.91, specificity = 0.88, kappa = 0.31, and MCC = 0.41. We have also curated an evaluation set ( n = 153 compounds) published in 2017, and when used to test our model, it showed the comparable statistics (accuracy = 0.83, precision = 0.27, recall = 1.00, specificity = 0.81, kappa = 0.36, and MCC = 0.47). We have also compared these models with additional machine learning algorithms showing Bayesian machine learning models constructed with literature Mtb data generated by different laboratories generally were equivalent to or outperformed deep neural networks with external test sets. Finally, we have also compared our training and test sets to show they were suitably diverse and different in order to represent useful evaluation sets. Such Mtb machine learning models could help prioritize compounds for testing in vitro and in vivo.

Therapeutic potential of promiscuous targets in Mycobacterium tuberculosis

In the field of tuberculosis drug development, the term 'promiscuous' was coined to collectively describe targets that repeatedly show up in whole-cell screenings. With the current climate leaning towards the exclusion of these targets in future drug screens, this review discusses and clarifies misconceptions surrounding this classification, the prospects of developing compounds targeting promiscuous targets, and their potential impact on tuberculosis drug development. The dominance of these targets in cell-based screens reflect not only bias introduced by experimental setup, but also some of the pathogen's greatest vulnerabilities. Coupled with favourable predictions of their in vivo efficacies and synergism with other TB drugs, these targets open opportunities to be explored for the development of rational drug combination for tuberculosis.

Protein structure-based drug design: from docking to molecular dynamics

Recent years have witnessed rapid developments of computer-aided drug design methods, which have reached accuracy that allows their routine practical applications in drug discovery campaigns. Protein structure-based methods are useful for the prediction of binding modes of small molecules and their relative affinity. The high-throughput docking of up to 10⁶ small molecules followed by scoring based on implicit-solvent force field can robustly identify micromolar binders using a rigid protein target. Molecular dynamics with explicit solvent is a low-throughput technique for the characterization of flexible binding sites and accurate evaluation of binding pathways, kinetics, and thermodynamics. In this review we highlight recent advancements in applications of ligand docking tools and molecular dynamics simulations to ligand identification and optimization.

Examining the role of protein structural dynamics in drug resistance in Mycobacterium tuberculosis

2D-IR spectroscopy reveals a role for protein structural dynamics in antimicrobial-resistance.

Antimicrobial resistance represents a growing global health problem. The emergence of novel resistance mechanisms necessitates the development of alternative approaches to investigate the molecular fundamentals of resistance, leading ultimately to new strategies for counteracting them. To gain deeper insight into antibiotic–target interactions, the binding of the frontline anti-tuberculosis drug isoniazid (INH) to a target enzyme, InhA, from Mycobacterium tuberculosis was studied using ultrafast two-dimensional infrared (2D-IR) spectroscopy and molecular simulations. Comparing wild-type InhA with a series of single point mutations, it was found that binding of the INH–NAD inhibitor to susceptible forms of the enzyme increased the vibrational coupling between residues located in the Rossmann fold co-factor binding site of InhA and suppressed dynamic fluctuations of the enzyme structure. The effect correlated with biochemical assay data, being reduced in the INH-resistant S94A mutant and absent in the biochemically-inactive P193A control. Molecular dynamics simulations and calculations of inter–residue couplings indicate that the changes in coupling and dynamics are not localised to the co-factor binding site, but permeate much of the protein. We thus propose that the resistant S94A mutation circumvents subtle changes in global structural dynamics caused by INH upon binding to the wild-type enzyme that may impact upon the formation of important protein–protein complexes in the fatty acid synthase pathway of M. tuberculosis.

QSAR based therapeutic management of M. tuberculosis

Mycobacterium tuberculosis is responsible for severe mortality and morbidity worldwide but, under-developed and developing countries are more prone to infection. In search of effective and wide-spectrum anti-tubercular agents, interdisciplinary approaches are being explored. Of the several approaches used, computer based quantitative structure activity relationship (QSAR) have gained momentum. Structure-based drug design and discovery implies a combined knowledge of accurate prediction of ligand poses with the good prediction and interpretation of statistically validated models derived from the 3D-QSAR approach. The validated models are generally used to screen a small combinatorial library of potential synthetic candidates to identify hits which further subjected to docking to filter out compounds as novel potential emerging drug molecules to address multidrug-resistant tuberculosis. Several newer models are integrated to QSAR methods which include different types of chemical and biological data, and simultaneous prediction of pharmacological activities including toxicities and/or other safety profiles to get new compounds with desired activity. In the process, several newer molecules have been identified which are now being assessed for their clinical efficacy. Present review deals with the advances made in the field highlighting overall future prospects of the development of anti-tuberculosis drugs.

Bioinformatics approach to prioritize known drugs towards repurposing for tuberculosis

New drugs are urgently needed to cure tuberculosis (TB) in a short period of time without causing any adverse effects since currently used drugs for the treatment of multi drug-resistant TB cause several adverse effects with poor success rate. Therefore, we aimed to prioritize known drugs towards repurposing for TB by employing bioinformatics approach in the present study. A total of 1554 FDA approved drugs were obtained from DrugBank. Serine/threonine-protein kinase, pknB (Rv0014c) of Mycobacterium tuberculosis (Mtb) was selected as the drug target since it involves in several vital functions of the Mtb. All of the 1554 drugs were subjected to molecular docking with pknB. Glide and AutoDock Vina were employed using rigid docking followed by induced fit docking protocol for prioritization of drugs. Out of 14 drugs prioritized, six are suggested as high-confident drugs towards repurposing for TB as they were consistently found within top 10 ranks of both methods, and strongly binding in the active site of the pknB. We also found atorvastatin as one of the high-confident drugs, which has already been demonstrated to be active against Mtb under in vitro conditions by other researchers. Therefore, we propose that the prioritized six high-confident drugs as potential candidates for repurposing for TB and suggest for further experimental studies. We also suggest that the bioinformatics procedure we have employed in this study could be effectively applied for prioritization of drugs for other diseases.

Illustrating and homology modeling the proteins of the Zika virus

The Zika virus (ZIKV) is a flavivirus of the family Flaviviridae, which is similar to dengue virus, yellow fever and West Nile virus. Recent outbreaks in South America, Latin America, the Caribbean and in particular Brazil have led to concern for the spread of the disease and potential to cause Guillain-Barré syndrome and microcephaly. Although ZIKV has been known of for over 60 years there is very little in the way of knowledge of the virus with few publications and no crystal structures. No antivirals have been tested against it either in vitro or in vivo. ZIKV therefore epitomizes a neglected disease. Several suggested steps have been proposed which could be taken to initiate ZIKV antiviral drug discovery using both high throughput screens as well as structure-based design based on homology models for the key proteins. We now describe preliminary homology models created for NS5, FtsJ, NS4B, NS4A, HELICc, DEXDc, peptidase S7, NS2B, NS2A, NS1, E stem, glycoprotein M, propeptide, capsid and glycoprotein E using SWISS-MODEL. Eleven out of 15 models pass our model quality criteria for their further use. While a ZIKV glycoprotein E homology model was initially described in the immature conformation as a trimer, we now describe the mature dimer conformer which allowed the construction of an illustration of the complete virion. By comparing illustrations of ZIKV based on this new homology model and the dengue virus crystal structure we propose potential differences that could be exploited for antiviral and vaccine design. The prediction of sites for glycosylation on this protein may also be useful in this regard. While we await a cryo-EM structure of ZIKV and eventual crystal structures of the individual proteins, these homology models provide the community with a starting point for structure-based design of drugs and vaccines as well as a for computational virtual screening.

Computational protein-ligand docking and virtual drug screening with the AutoDock suite

Computational docking can be used to predict bound conformations and free energies of binding for small-molecule ligands to macromolecular targets. Docking is widely used for the study of biomolecular interactions and mechanisms, and it is applied to structure-based drug design. The methods are fast enough to allow virtual screening of ligand libraries containing tens of thousands of compounds. This protocol covers the docking and virtual screening methods provided by the AutoDock suite of programs, including a basic docking of a drug molecule with an anticancer target, a virtual screen of this target with a small ligand library, docking with selective receptor flexibility, active site prediction and docking with explicit hydration. The entire protocol will require ∼5 h.

Docking Screens for Novel Ligands Conferring New Biology

It is now plausible to dock libraries of 10 million molecules against targets over several days or weeks. When the molecules screened are commercially available, they may be rapidly tested to find new leads. Although docking retains important liabilities (it cannot calculate affinities accurately nor even reliably rank order high-scoring molecules), it can often can distinguish likely from unlikely ligands, often with hit rates above 10%. Here we summarize the improvements in libraries, target quality, and methods that have supported these advances, and the open access resources that make docking accessible. Recent docking screens for new ligands are sketched, as are the binding, crystallographic, and in vivo assays that support them. Like any technique, controls are crucial, and key experimental ones are reviewed. With such controls, docking campaigns can find ligands with new chemotypes, often revealing the new biology that may be docking's greatest impact over the next few years.

Illustrating and homology modeling the proteins of the Zika virus

The Zika virus (ZIKV) is a flavivirus of the family Flaviviridae, which is similar to dengue virus, yellow fever and West Nile virus. Recent outbreaks in South America, Latin America, the Caribbean and in particular Brazil have led to concern for the spread of the disease and potential to cause Guillain-Barré syndrome and microcephaly. Although ZIKV has been known of for over 60 years there is very little in the way of knowledge of the virus with few publications and no crystal structures. No antivirals have been tested against it either in vitro or in vivo. ZIKV therefore epitomizes a neglected disease. Several suggested steps have been proposed which could be taken to initiate ZIKV antiviral drug discovery using both high throughput screens as well as structure-based design based on homology models for the key proteins. We now describe preliminary homology models created for NS5, FtsJ, NS4B, NS4A, HELICc, DEXDc, peptidase S7, NS2B, NS2A, NS1, E stem, glycoprotein M, propeptide, capsid and glycoprotein E using SWISS-MODEL. Eleven out of 15 models pass our model quality criteria for their further use. While a ZIKV glycoprotein E homology model was initially described in the immature conformation as a trimer, we now describe the mature dimer conformer which allowed the construction of an illustration of the complete virion. By comparing illustrations of ZIKV based on this new homology model and the dengue virus crystal structure we propose potential differences that could be exploited for antiviral and vaccine design. The prediction of sites for glycosylation on this protein may also be useful in this regard. While we await a cryo-EM structure of ZIKV and eventual crystal structures of the individual proteins, these homology models provide the community with a starting point for structure-based design of drugs and vaccines as well as a for computational virtual screening.

1001 Ways to run AutoDock Vina for virtual screening

Large-scale computing technologies have enabled high-throughput virtual screening involving thousands to millions of drug candidates. It is not trivial, however, for biochemical scientists to evaluate the technical alternatives and their implications for running such large experiments. Besides experience with the molecular docking tool itself, the scientist needs to learn how to run it on high-performance computing (HPC) infrastructures, and understand the impact of the choices made. Here, we review such considerations for a specific tool, AutoDock Vina, and use experimental data to illustrate the following points: (1) an additional level of parallelization increases virtual screening throughput on a multi-core machine; (2) capturing of the random seed is not enough (though necessary) for reproducibility on heterogeneous distributed computing systems; (3) the overall time spent on the screening of a ligand library can be improved by analysis of factors affecting execution time per ligand, including number of active torsions, heavy atoms and exhaustiveness. We also illustrate differences among four common HPC infrastructures: grid, Hadoop, small cluster and multi-core (virtual machine on the cloud). Our analysis shows that these platforms are suitable for screening experiments of different sizes. These considerations can guide scientists when choosing the best computing platform and set-up for their future large virtual screening experiments.

Predicting Mouse Liver Microsomal Stability with "Pruned" Machine Learning Models and Public Data

PURPOSE

Mouse efficacy studies are a critical hurdle to advance translational research of potential therapeutic compounds for many diseases. Although mouse liver microsomal (MLM) stability studies are not a perfect surrogate for in vivo studies of metabolic clearance, they are the initial model system used to assess metabolic stability. Consequently, we explored the development of machine learning models that can enhance the probability of identifying compounds possessing MLM stability.

METHODS

Published assays on MLM half-life values were identified in PubChem, reformatted, and curated to create a training set with 894 unique small molecules. These data were used to construct machine learning models assessed with internal cross-validation, external tests with a published set of antitubercular compounds, and independent validation with an additional diverse set of 571 compounds (PubChem data on percent metabolism).

RESULTS

"Pruning" out the moderately unstable / moderately stable compounds from the training set produced models with superior predictive power. Bayesian models displayed the best predictive power for identifying compounds with a half-life ≥1 h.

CONCLUSIONS

Our results suggest the pruning strategy may be of general benefit to improve test set enrichment and provide machine learning models with enhanced predictive value for the MLM stability of small organic molecules. This study represents the most exhaustive study to date of using machine learning approaches with MLM data from public sources.

Charting a Path to Success in Virtual Screening

Docking is commonly applied to drug design efforts, especially high-throughput virtual screenings of small molecules, to identify new compounds that bind to a given target. Despite great advances and successful applications in recent years, a number of issues remain unsolved. Most of the challenges and problems faced when running docking experiments are independent of the specific software used, and can be ascribed to either improper input preparation or to the simplified approaches applied to achieve high-throughput speed. Being aware of approximations and limitations of such methods is essential to prevent errors, deal with misleading results, and increase the success rate of virtual screening campaigns. In this review, best practices and most common issues of docking and virtual screening will be discussed, covering the journey from the design of the virtual experiment to the hit identification.

Radiolabelling and positron emission tomography of PT70, a time-dependent inhibitor of InhA, the Mycobacterium tuberculosis enoyl-ACP reductase

PT70 is a diaryl ether inhibitor of InhA, the enoyl-ACP reductase in the Mycobacterium tuberculosis fatty acid biosynthesis pathway. It has a residence time of 24 min on the target, and also shows antibacterial activity in a mouse model of tuberculosis infection. Due to the interest in studying target tissue pharmacokinetics of PT70, we developed a method to radiolabel PT70 with carbon-11 and have studied its pharmacokinetics in mice and baboons using positron emission tomography.

Open Source Bayesian Models. 2. Mining a "Big Dataset" To Create and Validate Models with ChEMBL

In an associated paper, we have described a reference implementation of Laplacian-corrected naïve Bayesian model building using extended connectivity (ECFP)- and molecular function class fingerprints of maximum diameter 6 (FCFP)-type fingerprints. As a follow-up, we have now undertaken a large-scale validation study in order to ensure that the technique generalizes to a broad variety of drug discovery datasets. To achieve this, we have used the ChEMBL (version 20) database and split it into more than 2000 separate datasets, each of which consists of compounds and measurements with the same target and activity measurement. In order to test these datasets with the two-state Bayesian classification, we developed an automated algorithm for detecting a suitable threshold for active/inactive designation, which we applied to all collections. With these datasets, we were able to establish that our Bayesian model implementation is effective for the large majority of cases, and we were able to quantify the impact of fingerprint folding on the receiver operator curve cross-validation metrics. We were also able to study the impact that the choice of training/testing set partitioning has on the resulting recall rates. The datasets have been made publicly available to be downloaded, along with the corresponding model data files, which can be used in conjunction with the CDK and several mobile apps. We have also explored some novel visualization methods which leverage the structural origins of the ECFP/FCFP fingerprints to attribute regions of a molecule responsible for positive and negative contributions to activity. The ability to score molecules across thousands of relevant datasets across organisms also may help to access desirable and undesirable off-target effects as well as suggest potential targets for compounds derived from phenotypic screens.