Discovery of new inhibitors of Mycobacterium tuberculosis InhA enzyme using virtual screening and a 3D-pharmacophore-based approach

New therapeutic strategies for Mycobacterium abscessus pulmonary diseases - untapping the mycolic acid pathway

INTRODUCTION

Treatment options against Mycobacterium abscessus infections are very limited. New compounds are needed to cure M. abscessus pulmonary diseases. While the mycolic acid biosynthetic pathway has been largely exploited for the treatment of tuberculosis, this metabolic process has been overlooked in M. abscessus, although it offers many potential drug targets for the treatment of this opportunistic pathogen.

AREAS COVERED

Herein, the authors review the role of the MmpL3 membrane protein and the enoyl-ACP reductase InhA involved in the transport and synthesis of mycolic acids, respectively. They discuss their importance as two major vulnerable drug targets in M. abscessus and report the activity of MmpL3 and InhA inhibitors. In particular, they focus on NITD-916, a direct InhA inhibitor against M. abscessus, particularly warranted in the context of multidrug resistance.

EXPERT OPINION

There is an increasing body of evidence validating the mycolic acid pathway as an attractive drug target to be further exploited for M. abscessus lung disease treatments. The NITD-916 studies provide a proof-of-concept that direct inhibitors of InhA are efficient in vitro, in macrophages and in zebrafish. Future work is now required to improve the activity and pharmacological properties of these inhibitors and their evaluation in pre-clinical models.

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.

Assay development and inhibition of the Mt-DprE2 essential reductase from Mycobacterium tuberculosis

DprE2 is an essential enzyme in the synthesis of decaprenylphosphoryl-β-d-arabinofuranose (DPA) and subsequently arabinogalactan, and is a significant new drug target for M. tuberculosis. Two compounds from the GSK-177 box set, GSK301A and GSK032A, were identified through Mt-DprE2-target overexpression studies. The Mt-DprE1-DprE2 complex was co-purified and a new in vitro DprE2 assay developed, based on the oxidation of the reduced nicotinamide adenine dinucleotide cofactor of DprE2 (NADH/NADPH). The Mt-DprE1-DprE2 complex showed interesting kinetics in both the DprE1 resazurin-based assay, where Mt-DprE2 was found to enhance Mt-DprE1 activity and reduce substrate inhibition; and also in the DprE2 assay, which similarly exhibited substrate inhibition and a difference in kinetics of the two potential cofactors, NADH and NADPH. Although, no inhibition was observed in the DprE2 assay by the two GSK set compounds, spontaneous mutant generation indicated a possible explanation in the form of a pro-drug activation pathway, involving fgd1 and fbiC.

Attempting to Increase the Effectiveness of the Antidepressant Trazodone Hydrochloride Drug Using π-Acceptors

Major depressive disorder is a prevalent mood illness that is mildly heritable. Cases with the highest familial risk had recurrence and onset at a young age. Trazodone hydrochloride is an antidepressant medicine that affects the chemical messengers in the brain known as neurotransmitters, which include acetylcholine, norepinephrine, dopamine, and serotonin. In the present research, in solid and liquid phases, the 1:1 charge-transfer complexes between trazodone hydrochloride (TZD) and six different π-acceptors were synthesized and investigated using different microscopic techniques. The relation of dative ion pairs [TZD+, A-], where A is the acceptor, was inferred via intermolecular charge-transfer complexes. Additionally, a molecular docking examination was utilized to compare the interactions of protein receptors (serotonin-6BQH) with the TZD alone or in combination with the six distinct acceptor charge-transfer complexes. To refine the docking results acquired from AutoDock Vina and to better examine the molecular mechanisms of receptor-ligand interactions, a 100 ns run of molecular dynamics simulation was used. All the results obtained in this study prove that the 2,6-dichloroquinone-4-chloroimide (DCQ)/TZD complex interacts with serotonin receptors more efficiently than reactant donor TZD only and that [(TZD)(DCQ)]-serotonin has the highest binding energy value of all π-acceptor complexes.

Identification of Novel Inhibitor of Enoyl-Acyl Carrier Protein Reductase (InhA) Enzyme in Mycobacterium tuberculosis from Plant-Derived Metabolites: An In Silico Study

Mycobacterium tuberculosis (M.tb.) enoyl-acyl carrier protein (ACP) reductase (InhA) is validated as a useful target for tuberculosis therapy and is considered an attractive enzyme to drug discovery. This study aimed to identify the novel inhibitor of the InhA enzyme, a potential target of M.tb. involved in the type II fatty acid biosynthesis pathway that controls mycobacterial cell envelope synthesis. We compiled 80 active compounds from Ruta graveolens and citrus plants belonging to the Rutaceae family for pharmacokinetics and molecular docking analyses. The chemical structures of the 80 phytochemicals and the 3D structure of the target protein were retrieved from the PubChem database and RCSB Protein Data Bank, respectively. The evaluation of druglikeness was performed based on Lipinski’s Rule of Five, while the computed phytochemical properties and molecular descriptors were used to predict the ADMET of the compounds. Amongst these, 11 pharmacokinetically-screened compounds were further examined by performing molecular docking analysis with an InhA target using AutoDock 4.2. The docking results showed that gravacridonediol, a major glycosylated natural alkaloid from Ruta graveolens, might possess a promising inhibitory potential against InhA, with a binding energy (B.E.) of −10.80 kcal/mole and inhibition constant (Ki) of 600.24 nM. These contrast those of the known inhibitor triclosan, which has a B.E. of −6.69 kcal/mole and Ki of 12.43 µM. The binding efficiency of gravacridonediol was higher than that of the well-known inhibitor triclosan against the InhA target. The present study shows that the identified natural compound gravacridonediol possesses drug-like properties and also holds promise in inhibiting InhA, a key target enzyme of M.tb.

Rethinking the MtInhA tertiary and quaternary structure flexibility: a molecular dynamics view

Flexibility and function are related properties in the study of protein dynamics. Flexibility reflects in the conformational potential of proteins and thus in their functionalities. The presence of interactions between protein-ligands and protein-protein complexes, substrates, and environmental changes can alter protein plasticity, acting from the rearrangement of the side chains of amino acids to the folding/unfolding of large structural motifs. To evaluate the effects of the flexibility in protein systems, we defined the enzyme 2-trans-enoyl-ACP (CoA) reductase from Mycobacterium tuberculosis, or MtInhA, as our target system. MtInhA is biologically active as a tetramer in solution; however, computational studies commonly use the monomer justifying the independence of its active sites due to their distances. However, differences in flexibility between tertiary and quaternary structures could present impact on the size of the active site, influencing the drug discovery process. In this study, we investigated the influence of flexibility restrictions in A- and B-loops of the MtInhA in order to suggest a monomeric structure that describes the conformational behavior of the tetrameric system. Overall, we observed that simulations where restrictions were applied to the A- and B-loops present a more similar behavior to the native structure when compared to unrestricted simulations. Therefore, our work presents a monomeric model of MtInhA, which has conformational characteristics of the biologically active structure. Thus, the data obtained in this work can be applied to the MtInhA system for the generation of more reliable flexible models for molecular docking experiments, and also for the performance of longer simulations by molecular dynamics and with a lower computational cost.

Discovery of novel and potent InhA inhibitors by an in silico screening and pharmacokinetic prediction

Aim: In silico screening approaches were performed to discover novel InhA inhibitors. Methods: Candidate InhA inhibitors were obtained from the combination of virtual screening and pharmacokinetic prediction. In addition, molecular mechanics Poisson-Boltzmann surface area, molecular mechanics Generalized Born surface area and WaterSwap methods were performed to investigate the binding interactions and binding energy of candidate compounds. Results: Four candidate compounds with suitable physicochemical, pharmacokinetic and antibacterial properties are proposed. The crucial interactions of the candidate compounds were H-bond, pi-pi and sigma-pi interactions observed in the InhA binding site. The binding affinity of these compounds was improved by hydrophobic interactions with hydrophobic side chains in the InhA pocket. Conclusion: The four newly identified InhA inhibitors reported in this study could serve as promising hit compounds against Mycobacterium tuberculosis and may be considered for further experimental studies.

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.

Handling the Hurdles on the Way to Anti-tuberculosis Drug Development

The global epidemic of tuberculosis (TB) imposes a sustained epidemiologic vigilance and investments in research by governments. Mycobacterium tuberculosis, the main causative agent of TB in human beings, is a very successful pathogen, being the main cause of death in the population among infectious agents. In 2018, ~10 million individuals were contaminated with this bacillus and became ill with TB, and about 1.2 million succumbed to the disease. Most of the success of the M. tuberculosis to linger in the population comes from its ability to persist in an asymptomatic latent state into the host and, in fact, the majority of the individuals are unaware of being contaminated. Even though TB is a treatable disease and is curable in most cases, the treatment is lengthy and laborious. In addition, the rise of resistance to first-line anti-TB drugs elicits a response from TB research groups to discover new chemical entities, preferably with novel mechanisms of action. The pathway to find a new TB drug, however, is arduous and has many barriers that are difficult to overcome. Fortunately, several approaches are available today to be pursued by scientists interested in anti-TB drug development, which goes from massively testing chemical compounds against mycobacteria, to discovering new molecular targets by genetic manipulation. This review presents some difficulties found along the TB drug development process and illustrates different approaches that might be used to try to identify new molecules or targets that are able to impair M. tuberculosis survival.

Mycobacterial drug discovery

Mycobacterium tuberculosis is the causative pathogen of the pulmonary disease tuberculosis. Despite the availability of effective treatment programs, there is a global pursuit of new anti-tubercular agents to respond to the developing threat of drug resistance, in addition to reducing the extensive duration of chemotherapy and any associated toxicity. The route to mycobacterial drug discovery can be considered from two directions: target-to-drug and drug-to-target. The former approach uses conventional methods including biochemical assays along with innovative computational screens, but is yet to yield any drug candidates to the clinic, with a high attrition rate owing to lack of whole cell activity. In the latter approach, compound libraries are screened for efficacy against the bacilli or model organisms, ensuring whole cell activity, but here subsequent target identification is the rate-limiting step. Advances in a variety of scientific fields have enabled the amalgamation of aspects of both approaches in the development of novel drug discovery tools, which are now primed to accelerate the discovery of novel hits and leads with known targets and whole cell activity. This review discusses these traditional and innovative techniques, which are widely used in the quest for new anti-tubercular compounds.

Innovations in mycobacterial drug discovery to accelerate the identification of new drug candidates with confirmed targets and whole cell activity.

Molecular Docking: Challenges, Advances and its Use in Drug Discovery Perspective

Molecular docking is a process through which small molecules are docked into the macromolecular structures for scoring its complementary values at the binding sites. It is a vibrant research area with dynamic utility in structure-based drug-designing, lead optimization, biochemical pathway and for drug designing being the most attractive tools. Two pillars for a successful docking experiment are correct pose and affinity prediction. Each program has its own advantages and drawbacks with respect to their docking accuracy, ranking accuracy and time consumption so a general conclusion cannot be drawn. Moreover, users don't always consider sufficient diversity in their test sets which results in certain programs to outperform others. In this review, the prime focus has been laid on the challenges of docking and troubleshooters in existing programs, underlying algorithmic background of docking, preferences regarding the use of docking programs for best results illustrated with examples, comparison of performance for existing tools and algorithms, state of art in docking, recent trends of diseases and current drug industries, evidence from clinical trials and post-marketing surveillance are discussed. These aspects of the molecular drug designing paradigm are quite controversial and challenging and this review would be an asset to the bioinformatics and drug designing communities.

Structure-Based Virtual Screening of Pseudomonas aeruginosa LpxA Inhibitors Using Pharmacophore-Based Approach

Multidrug resistance in Pseudomonas aeruginosa is a noticeable and ongoing major obstacle for inhibitor design. In P. aeruginosa, uridine diphosphate N-acetylglucosamine (UDP-GlcNAc) acetyltransferase (PaLpxA) is an essential enzyme of lipid A biosynthesis and an attractive drug target. PaLpxA is a homotrimer, and the binding pocket for its substrate, UDP-GlcNAc, is positioned between the monomer A-monomer B interface. The uracil moiety binds at one monomer A, the GlcNAc moiety binds at another monomer B, and a diphosphate form bonds with both monomers. The catalytic residues are conserved and display a similar catalytic mechanism across orthologs, but some distinctions exist between pocket sizes, residue differences, substrate positioning and specificity. The analysis of diversified pockets, volumes, and ligand positions was determined between orthologues that could aid in selective inhibitor development. Thenceforth, a complex-based pharmacophore model was generated and subjected to virtual screening to identify compounds with similar pharmacophoric properties. Docking and general Born-volume integral (GBVI) studies demonstrated 10 best lead compounds with selective inhibition properties with essential residues in the pocket. For biological access, these scaffolds complied with the Lipinski rule, no toxicity and drug likeness properties, and were considered as lead compounds. Hence, these scaffolds could be helpful for the development of potential selective PaLpxA inhibitors.

Development of an in vitro screening assay for PIP5K1α lipid kinase and identification of potent inhibitors

The human phosphatidylinositol 4-phosphate 5-kinase type I α (hPIP5K1α) participates in the phosphoinositide-3-kinase/protein kinase B/mammalian target of rapamycin signaling pathway. Despite the evidence that hPIP5K1α plays a role in the development of prostate cancer (PCa), only one inhibitor is known to date. With the aim of identifying new inhibitors, a nonradiometric assay for measurement of the hPIP5K1α enzyme activity was developed. The assay is based on the separation of the fluorescently labeled substrate phosphatidylinositol-4-phosphate (PI(4)P) and the resulting product phosphatidylinositol-4,5-bisphosphate (PIP₂ ) by capillary electrophoresis (CE). Furthermore, an inactive mutant K261A of hPIP5K1α was generated by site-directed mutagenesis and used as a control. Michaelis-Menten analysis revealed a K_m value of 21.6 µm and V_max of 0.65 pmol·min^-1 for the cosubstrate ATP. The average Z' value was determined to be 0.86, indicating a high reliability of the assay. An in silico screening of an in-house compound library was performed employing the crystal structure of zebrafish PIP5K1α. By applying this strategy, three compounds with a 2-amino-3-cyano-4H-pyranobenzoquinone scaffold were identified and tested using the CE-based assay. These compounds inhibited hPIP5K1α to > 90% at a concentration of 50 µm. Subsequently, the inhibitory activity of all compounds with a pyranobenzoquinone scaffold (29) was tested on hPIP5K1α. Compound 4-(2-amino-3-cyano-6-hydroxy-5,8-dioxo-7-undecyl-5,8-dihydro-4H-chromen-4-yl)benzoic acid appeared to be the most potent inhibitor of hPIP5K1α identified so far with an IC₅₀ value of 1.55 µm, exhibiting a substrate-competitive mode of action. The effects of this compound on cell viability and the induction of apoptosis were investigated in LNCaP, DU145, and PC3 PCa cells.

QSAR Model of Indeno[1,2-b]indole Derivatives and Identification of N-isopentyl-2-methyl-4,9-dioxo-4,9-Dihydronaphtho[2,3-b]furan-3-carboxamide as a Potent CK2 Inhibitor

Casein kinase II (CK2) is an intensively studied enzyme, involved in different diseases, cancer in particular. Different scaffolds were used to develop inhibitors of this enzyme. Here, we report on the synthesis and biological evaluation of twenty phenolic, ketonic, and para-quinonic indeno[1,2-b]indole derivatives as CK2 inhibitors. The most active compounds were 5-isopropyl-1-methyl-5,6,7,8-tetrahydroindeno[1,2-b]indole-9,10-dione 4h and 1,3-dibromo-5-isopropyl-5,6,7,8-tetrahydroindeno[1,2-b]indole-9,10-dione 4w with identical IC₅₀ values of 0.11 µM. Furthermore, the development of a QSAR model based on the structure of indeno[1,2-b]indoles was performed. This model was used to predict the activity of 25 compounds with naphtho[2,3-b]furan-4,9-dione derivatives, which were previously predicted as CK2 inhibitors via a molecular modeling approach. The activities of four naphtho[2,3-b]furan-4,9-dione derivatives were determined in vitro and one of them (N-isopentyl-2-methyl-4,9-dioxo-4,9-dihydronaphtho[2,3-b]furan-3-carboxamide) turned out to inhibit CK2 with an IC₅₀ value of 2.33 µM. All four candidates were able to reduce the cell viability by more than 60% after 24 h of incubation using 10 µM.

Molecular modeling assisted identification and biological evaluation of potent cathepsin S inhibitors

Cathepsin S (CatS) is one of the cysteinyl cathepsins widely studied for its clinical significance and found to be a promising therapeutic target for several diseases; to name a few is arthritis, allergic inflammation, cancer, diabetes, obesity, and cystic fibrosis. Elevated CatS level is a contributing factor for related disorders, and therefore among different strategies to regulate the activity of CatS, one is to design a quality inhibitor. Earlier, we have demonstrated a highly selective CatS inhibitor, RO5444101 interacts primarily with the S2 pocket of the protein which is structurally unique in contrast to other variants of cathepsin. However, the molecular properties of RO5444101 can question its efficacy at the clinical level. In the present study, we have implemented a series of molecular modeling methods to screen the Maybridge library considering the pharmacophoric features of RO5444101 and other relevant inhibitors of CatS. Based on the priority list, eight hits were subjected to biological evaluation. Subsequently, KM07987 was found to be most potent, with the IC₅₀ of <5 μM. Molecular dynamics simulations also relate to our experimental findings and propose the importance of CatS's S2 pocket, which primarily interacts with the inhibitors. Based on the S2 pocket interactions, structural modifications of the promising hits can further be translated into novel scaffolds for improved inhibition of CatS.

Applications of Molecular Simulation in the Discovery of Antituberculosis Drugs: A Review

After decades of efforts, tuberculosis has been well controlled in most places. The existing drugs are no longer sufficient for the treatment of drug-resistant Mycobacterium tuberculosis due to significant toxicity and selective pressure, especially for XDR-TB. In order to accelerate the development of high-efficiency, low-toxic antituberculosis drugs, it is particularly important to use Computer Aided Drug Design (CADD) for rational drug design. Here, we systematically reviewed the specific role of molecular simulation in the discovery of new antituberculosis drugs. The purpose of this review is to overview current applications of molecular simulation methods in the discovery of antituberculosis drugs. Furthermore, the unique advantages of molecular simulation was discussed in revealing the mechanism of drug resistance. The comprehensive use of different molecular simulation methods will help reveal the mechanism of drug resistance and improve the efficiency of rational drug design. With the help of molecular simulation methods such as QM/MM method, the mechanisms of biochemical reactions catalyzed by enzymes at atomic level in Mycobacterium tuberculosis has been deeply analyzed. QSAR and virtual screening both accelerate the development of highefficiency, low-toxic potential antituberculosis drugs. Improving the accuracy of existing algorithms and developing more efficient new methods for CADD will always be a hot topic in the future. It is of great value to utilize molecular dynamics simulation to investigate complex systems that cannot be studied in experiments, especially for drug resistance of Mycobacterium tuberculosis.

The importance of the quaternary structure to represent conformational ensembles of the major Mycobacterium tuberculosis drug target

Flexibility is a feature intimately related to protein function, since conformational changes can be used to describe environmental changes, chemical modifications, protein-protein and protein-ligand interactions. In this study, we have investigated the influence of the quaternary structure of 2-trans-enoyl-ACP (CoA) reductase or InhA, from Mycobacterium tuberculosis, to its flexibility. We carried out classical molecular dynamics simulations using monomeric and tetrameric forms to elucidate the enzyme's flexibility. Overall, we observed statistically significant differences between conformational ensembles of tertiary and quaternary structures. In addition, the enzyme's binding site is the most affected region, reinforcing the importance of the quaternary structure to evaluate the binding affinity of small molecules, as well as the effect of single point mutations to InhA protein dynamics.

Predictive Power of In Silico Approach to Evaluate Chemicals against M. tuberculosis: A Systematic Review

Mycobacterium tuberculosis (Mtb) is an endemic bacterium worldwide that causes tuberculosis (TB) and involves long-term treatment that is not always effective. In this context, several studies are trying to develop and evaluate new substances active against Mtb. In silico techniques are often used to predict the effects on some known target. We used a systematic approach to find and evaluate manuscripts that applied an in silico technique to find antimycobacterial molecules and tried to prove its predictive potential by testing them in vitro or in vivo. After searching three different databases and applying exclusion criteria, we were able to retrieve 46 documents. We found that they all follow a similar screening procedure, but few studies exploited equal targets, exploring the interaction of multiple ligands to 29 distinct enzymes. The following in vitro/vivo analysis showed that, although the virtual assays were able to decrease the number of molecules tested, saving time and money, virtual screening procedures still need to develop the correlation to more favorable in vitro outcomes. We find that the in silico approach has a good predictive power for in vitro results, but call for more studies to evaluate its clinical predictive possibilities.

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.

Identifying the novel inhibitors of lysine-specific demethylase 1 (LSD1) combining pharmacophore-based and structure-based virtual screening

Lysine-specific demethylase 1 (LSD1) has been reported to connect with a range of solid tumors. Thus, the exploration of LSD1 inhibitors has emerged as an effective strategy for cancer treatment. In this study, we constructed a pharmacophore model based on a series of flavin adenine dinucleotide (FAD)-competing inhibitors bearing triazole - dithiocarbamate scaffold combining docking, structure-activity relationship (SAR) study, and molecular dynamic (MD) simulation. Meanwhile, another pharmacophore model was also constructed manually, relying on several speculated substrate-competing inhibitors and reported putative vital interactions with LSD1. On the basis of the two pharmacophore models, multi-step virtual screenings (VSs) were performed against substrate-binding pocket and FAD-binding pocket, respectively, combining pharmacophore-based and structure-based strategy to exploit novel LSD1 inhibitors. After bioassay evaluation, four compounds among 21 hits with diverse and novel scaffolds exhibited inhibition activity at the range of 3.63-101.43 μM. Furthermore, substructure-based enrichment was performed, and four compounds with a more potent activity were identified. After that, the time-dependent assay proved that the most potent compound with IC₅₀ 2.21 μM inhibits LSD1 activity in a manner of time-independent. In addition, the compound exhibited a cellular inhibitory effect against LSD1 in MGC-803 cells and may inhibit cell migration and invasion by reversing EMT in cultured gastric cancer cells. Considering the binding mode and SAR of the series of compounds, we could roughly deem that these compounds containing 3-methylxanthine scaffold act through occupying substrate-binding pocket competitively. This study presented a new starting point to develop novel LSD1 inhibitors.

Mycobacterium tuberculosis Molecular Determinants of Infection, Survival Strategies, and Vulnerable Targets

Mycobacterium tuberculosis is the causative agent of tuberculosis, an ancient disease which, still today, represents a major threat for the world population. Despite the advances in medicine and the development of effective antitubercular drugs, the cure of tuberculosis involves prolonged therapies which complicate the compliance and monitoring of drug administration and treatment. Moreover, the only available antitubercular vaccine fails to provide an effective shield against adult lung tuberculosis, which is the most prevalent form. Hence, there is a pressing need for effective antitubercular drugs and vaccines. This review highlights recent advances in the study of selected M. tuberculosis key molecular determinants of infection and vulnerable targets whose structures could be exploited for the development of new antitubercular agents.

Rational design of drug-like compounds targeting Mycobacterium marinum MelF protein

The mycobacterial mel2 locus (mycobacterial enhanced infection locus, Rv1936-1941) is Mycobacterium marinum and M. tuberculosis specific, which can withstand reactive oxygen species (ROS) and reactive nitrogen species (RNS) induced stress. A library of over a million compounds was screened using in silico virtual ligand screening (VLS) to identify inhibitors against the modeled structure of MelF protein expressed by melF of mel2 locus so that M. marinum’s ability to withstand ROS/RNS stress could be reduced. The top ranked 1000 compounds were further screened to identify 178 compounds to maximize the scaffold diversity by manually evaluating the interaction of each compound with the target site. M. marinum melF was cloned, expressed and purified as maltose binding protein (MBP)-tagged recombinant protein in Escherichia coli. After establishing the flavin dependent oxidoreductase activity of MelF (~ 84 kDa), the inhibitors were screened for the inhibition of enzyme activity of whole cell lysate (WCL) and the purified MelF. Amongst these, 16 compounds could significantly inhibit the enzyme activity of purified MelF. For the six best inhibitory compounds, the minimal inhibitory concentration (MIC) was determined to be 3.4–19.4 μM and 13.5–38.8 μM for M. marinum and M. tuberculosis, respectively. Similarly, the minimal bactericidal concentration (MBC) was determined to be 6.8–38.8 μM and 27–38.8 μM against M. marinum and M. tuberculosis, respectively. One compound each in combination with isoniazid (INH) also showed synergistic inhibitory effect against M. marinum and M. tuberculosis with no cytotoxicity in HeLa cells. Interestingly, these inhibitors did not display any non-specific protein-structure destabilizing effect. Such inhibitors targeting the anti-ROS/RNS machinery may facilitate the efficient killing of replicating and nonreplicating mycobacteria inside the host cells.

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.

Functional, thermodynamics, structural and biological studies of in silico-identified inhibitors of Mycobacterium tuberculosis enoyl-ACP(CoA) reductase enzyme

Novel chemotherapeutics agents are needed to kill Mycobacterium tuberculosis, the main causative agent of tuberculosis (TB). The M. tuberculosis 2-trans-enoyl-ACP(CoA) reductase enzyme (MtInhA) is the druggable bona fide target of isoniazid. New chemotypes were previously identified by two in silico approaches as potential ligands to MtInhA. The inhibition mode was determined by steady-state kinetics for seven compounds that inhibited MtInhA activity. Dissociation constant values at different temperatures were determined by protein fluorescence spectroscopy. van't Hoff analyses of ligand binding to MtInhA:NADH provided the thermodynamic signatures of non-covalent interactions (ΔH°, ΔS°, ΔG°). Phenotypic screening showed that five compounds inhibited in vitro growth of M. tuberculosis H37Rv strain. Labio_16 and Labio_17 compounds also inhibited the in vitro growth of PE-003 multidrug-resistant strain. Cytotoxic effects on Hacat, Vero and RAW 264.7 cell lines were assessed for the latter two compounds. The Labio_16 was bacteriostatic and Labio_17 bactericidal in an M. tuberculosis-infected macrophage model. In Zebrafish model, Labio_16 showed no cardiotoxicity whereas Labio_17 showed dose-dependent cardiotoxicity. Accordingly, a model was built for the MtInhA:NADH:Labio_16 ternary complex. The results show that the Labio_16 compound is a direct inhibitor of MtInhA, and it may represent a hit for the development of chemotherapeutic agents to treat TB.

Anti-tubercular drug discovery: in silico implications and challenges

Tuberculosis (TB) has been reported as a major public health concern, especially in the developing countries. WHO report on tuberculosis 2016 shows a high mortality rate caused by TB leading to 1.8 million deaths worldwide (including deaths due to TB in HIV positive individuals), which is one of the top 10 causes of mortality in 2015. However, the main therapy used for the treatment of TB is still the Direct Observed Therapy Short-course (DOTS) that consists of four main first-line drugs. Due to the prolonged and unorganized use of these drugs, Mycobacterium tuberculosis (Mtb) has developed drug-resistance against them. To overcome this drug-resistance, efforts are continuously being made to develop new therapeutics. New drug-targets of Mtb are pursued by the researchers to develop their inhibitors. For this, new methodologies that comprise of the computational drug designing techniques are vigorously applied. A major limitation that is found with these techniques is the inability of the newly identified target-based inhibitors to inhibit the whole cell bacteria. A foremost factor for this limitation is the inability of these inhibitors to penetrate the bacterial cell wall. In this regard, various strategies to overcome this limitation have been discussed in detail in this review, along with new targets and new methodologies. A bunch of in silico tools available for the prediction of physicochemical properties that need to be explored to deal with the permeability issue of the Mtb inhibitors has also been discussed.

Development of Pharmacophore Model for Indeno[1,2-b]indoles as Human Protein Kinase CK2 Inhibitors and Database Mining

Protein kinase CK2, initially designated as casein kinase 2, is an ubiquitously expressed serine/threonine kinase. This enzyme, implicated in many cellular processes, is highly expressed and active in many tumor cells. A large number of compounds has been developed as inhibitors comprising different backbones. Beside others, structures with an indeno[1,2-b]indole scaffold turned out to be potent new leads. With the aim of developing new inhibitors of human protein kinase CK2, we report here on the generation of common feature pharmacophore model to further explain the binding requirements for human CK2 inhibitors. Nine common chemical features of indeno[1,2-b]indole-type CK2 inhibitors were determined using MOE software (Chemical Computing Group, Montreal, Canada). This pharmacophore model was used for database mining with the aim to identify novel scaffolds for developing new potent and selective CK2 inhibitors. Using this strategy several structures were selected by searching inside the ZINC compound database. One of the selected compounds was bikaverin (6,11-dihydroxy-3,8-dimethoxy-1-methylbenzo[b]xanthene-7,10,12-trione), a natural compound which is produced by several kinds of fungi. This compound was tested on human recombinant CK2 and turned out to be an active inhibitor with an IC₅₀ value of 1.24 µM.

Computer-Aided Drug Discovery Approaches against the Tropical Infectious Diseases Malaria, Tuberculosis, Trypanosomiasis, and Leishmaniasis

Despite the tremendous improvement in overall global health heralded by the adoption of the Millennium Declaration in the year 2000, tropical infections remain a major health problem in the developing world. Recent estimates indicate that the major tropical infectious diseases, namely, malaria, tuberculosis, trypanosomiasis, and leishmaniasis, account for more than 2.2 million deaths and a loss of approximately 85 million disability-adjusted life years annually. The crucial role of chemotherapy in curtailing the deleterious health and economic impacts of these infections has invigorated the search for new drugs against tropical infectious diseases. The research efforts have involved increased application of computational technologies in mainstream drug discovery programs at the hit identification, hit-to-lead, and lead optimization stages. This review highlights various computer-aided drug discovery approaches that have been utilized in efforts to identify novel antimalarial, antitubercular, antitrypanosomal, and antileishmanial agents. The focus is largely on developments over the past 5 years (2010-2014).

Design, Synthesis, and Antimycobacterial Activity of Novel Theophylline-7-Acetic Acid Derivatives With Amino Acid Moieties

The theophylline-7-acetic acid (7-TAA) scaffold is a promising novel lead compound for antimycobacterial activity. Here, we derive a model for antitubercular activity prediction based on 14 7-TAA derivatives with amino acid moieties and their methyl esters. The model is applied to a combinatorial library, consisting of 40 amino acid and methyl ester derivatives of 7-TAA. The best three predicted compounds are synthesized and tested against Mycobacterium tuberculosis H37Rv. All of them are stable, non-toxic against human cells and show antimycobacterial activity in the nanomolar range being 60 times more active than ethambutol.

Combining Metabolite-Based Pharmacophores with Bayesian Machine Learning Models for Mycobacterium tuberculosis Drug Discovery

Integrated computational approaches for Mycobacterium tuberculosis (Mtb) are useful to identify new molecules that could lead to future tuberculosis (TB) drugs. Our approach uses information derived from the TBCyc pathway and genome database, the Collaborative Drug Discovery TB database combined with 3D pharmacophores and dual event Bayesian models of whole-cell activity and lack of cytotoxicity. We have prioritized a large number of molecules that may act as mimics of substrates and metabolites in the TB metabolome. We computationally searched over 200,000 commercial molecules using 66 pharmacophores based on substrates and metabolites from Mtb and further filtering with Bayesian models. We ultimately tested 110 compounds in vitro that resulted in two compounds of interest, BAS 04912643 and BAS 00623753 (MIC of 2.5 and 5 μg/mL, respectively). These molecules were used as a starting point for hit-to-lead optimization. The most promising class proved to be the quinoxaline di-N-oxides, evidenced by transcriptional profiling to induce mRNA level perturbations most closely resembling known protonophores. One of these, SRI58 exhibited an MIC = 1.25 μg/mL versus Mtb and a CC₅₀ in Vero cells of >40 μg/mL, while featuring fair Caco-2 A-B permeability (2.3 x 10⁻⁶ cm/s), kinetic solubility (125 μM at pH 7.4 in PBS) and mouse metabolic stability (63.6% remaining after 1 h incubation with mouse liver microsomes). Despite demonstration of how a combined bioinformatics/cheminformatics approach afforded a small molecule with promising in vitro profiles, we found that SRI58 did not exhibit quantifiable blood levels in mice.

The future for early-stage tuberculosis drug discovery

There is an urgent need for new and better drugs to treat tuberculosis due to lengthy and complex treatment regimens and a rising problem of drug resistance. Drug discovery efforts have increased over the past few years, with a larger focus on modern high-throughput screening technologies. A combination of target-based approaches, with the traditional empirical means of drug identification, has been complemented by the use of target-based phenotypic screens only recently made possibly with newer genetic tools. Using these approaches, a number of promising compound series have been discovered. However, significant problems remain in developing these into drugs. This review highlights recent advances in TB drug discovery, including an overview of screening campaigns, lessons learned and future directions.

An Effective Approach for Clustering InhA Molecular Dynamics Trajectory Using Substrate-Binding Cavity Features

Protein receptor conformations, obtained from molecular dynamics (MD) simulations, have become a promising treatment of its explicit flexibility in molecular docking experiments applied to drug discovery and development. However, incorporating the entire ensemble of MD conformations in docking experiments to screen large candidate compound libraries is currently an unfeasible task. Clustering algorithms have been widely used as a means to reduce such ensembles to a manageable size. Most studies investigate different algorithms using pairwise Root-Mean Square Deviation (RMSD) values for all, or part of the MD conformations. Nevertheless, the RMSD only may not be the most appropriate gauge to cluster conformations when the target receptor has a plastic active site, since they are influenced by changes that occur on other parts of the structure. Hence, we have applied two partitioning methods (k-means and k-medoids) and four agglomerative hierarchical methods (Complete linkage, Ward's, Unweighted Pair Group Method and Weighted Pair Group Method) to analyze and compare the quality of partitions between a data set composed of properties from an enzyme receptor substrate-binding cavity and two data sets created using different RMSD approaches. Ensembles of representative MD conformations were generated by selecting a medoid of each group from all partitions analyzed. We investigated the performance of our new method for evaluating binding conformation of drug candidates to the InhA enzyme, which were performed by cross-docking experiments between a 20 ns MD trajectory and 20 different ligands. Statistical analyses showed that the novel ensemble, which is represented by only 0.48% of the MD conformations, was able to reproduce 75% of all dynamic behaviors within the binding cavity for the docking experiments performed. Moreover, this new approach not only outperforms the other two RMSD-clustering solutions, but it also shows to be a promising strategy to distill biologically relevant information from MD trajectories, especially for docking purposes.

Molecular docking and structure-based drug design strategies

Pharmaceutical research has successfully incorporated a wealth of molecular modeling methods, within a variety of drug discovery programs, to study complex biological and chemical systems. The integration of computational and experimental strategies has been of great value in the identification and development of novel promising compounds. Broadly used in modern drug design, molecular docking methods explore the ligand conformations adopted within the binding sites of macromolecular targets. This approach also estimates the ligand-receptor binding free energy by evaluating critical phenomena involved in the intermolecular recognition process. Today, as a variety of docking algorithms are available, an understanding of the advantages and limitations of each method is of fundamental importance in the development of effective strategies and the generation of relevant results. The purpose of this review is to examine current molecular docking strategies used in drug discovery and medicinal chemistry, exploring the advances in the field and the role played by the integration of structure- and ligand-based methods.

Identification of Novel Inhibitors of Mycobacterium tuberculosis PknG Using Pharmacophore Based Virtual Screening, Docking, Molecular Dynamics Simulation, and Their Biological Evaluation

PknG is a Ser/thr protein kinase that plays a crucial role in regulatory processes within the mycobacterial cell and signaling cascade of the infected host cell. The essentiality of PknG in mycobacterial virulence by blocking phagosome-lysosome fusion as well as its role in intrinsic antibiotic resistance makes it an attractive drug target. However, only very few compounds have been reported as Mycobacterium tuberculosis PknG (MtPknG) inhibitors so far. Therefore, in an effort to find potential inhibitors against MtPknG, we report here a sequential pharmacophore-based virtual screening workflow, 3-fold docking with different search algorithms, and molecular dynamic simulations for better insight into the predicted binding mode of identified hits. After detailed analysis of the results, six ligands were selected for in vitro analysis. Three of these molecules showed significant inhibitory activity against MtPknG. In addition, inhibitory studies of mycobacterial growth in infected THP-1 macrophages demonstrated considerable growth inhibition of M. bovis BCG induced through compound NRB04248 without any cytotoxic effect against host macrophages. Our results suggest that the compound NRB04248 can be explored for further design and optimization of MtPknG inhibitors.

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

Isoniazid (INH) is usually administered to treat latent Mycobacterium tuberculosis (Mtb) infections and is used in combination therapy to treat active tuberculosis (TB). Unfortunately, resistance to this drug is hampering its clinical effectiveness. INH is a prodrug that must be activated by Mtb catalase-peroxidase (KatG) before it can inhibit InhA (Mtb enoyl-acyl-carrier-protein reductase). Isoniazid-resistant cases of TB found in clinical settings usually involve mutations in or deletion of katG, which abrogate INH activation. Compounds that inhibit InhA without requiring prior activation by KatG would not be affected by this resistance mechanism and hence would display continued potency against these drug-resistant isolates of Mtb. Virtual screening experiments versus InhA in the GO Fight Against Malaria (GO FAM) project were designed to discover new scaffolds that display base-stacking interactions with the NAD cofactor. GO FAM experiments included targets from other pathogens, including Mtb, when they had structural similarity to a malaria target. Eight of the 16 soluble compounds identified by docking against InhA plus visual inspection were modest inhibitors and did not require prior activation by KatG. The best two inhibitors discovered are both fragment-sized compounds and displayed Ki values of 54 and 59 μM, respectively. Importantly, the novel inhibitors discovered have low structural similarity to known InhA inhibitors and thus help expand the number of chemotypes on which future medicinal chemistry efforts can be focused. These new fragment hits could eventually help advance the fight against INH-resistant Mtb strains, which pose a significant global health threat.

Hierarchical virtual screening approaches in small molecule drug discovery

Virtual screening has played a significant role in the discovery of small molecule inhibitors of therapeutic targets in last two decades. Various ligand and structure-based virtual screening approaches are employed to identify small molecule ligands for proteins of interest. These approaches are often combined in either hierarchical or parallel manner to take advantage of the strength and avoid the limitations associated with individual methods. Hierarchical combination of ligand and structure-based virtual screening approaches has received noteworthy success in numerous drug discovery campaigns. In hierarchical virtual screening, several filters using ligand and structure-based approaches are sequentially applied to reduce a large screening library to a number small enough for experimental testing. In this review, we focus on different hierarchical virtual screening strategies and their application in the discovery of small molecule modulators of important drug targets. Several virtual screening studies are discussed to demonstrate the successful application of hierarchical virtual screening in small molecule drug discovery.