Toward antituberculosis drugs: in silico screening of synthetic compounds against Mycobacterium tuberculosisl,d-transpeptidase 2

Copper transporter protein (MctB) as a therapeutic target to elicit antimycobacterial activity against tuberculosis

Tuberculosis (TB) is a prehistoric infection and major etiologic agent of TB, Mycobacterium tuberculosis, which is considered to have advanced from an early progenitor species found in Eastern Africa. By the 1800s, there were approximately 800 to 1000 fatality case reports per 100,000 people in Europe and North America. This research suggests an In-silico study to identify potential inhibitory compounds for the target Mycobacterial copper transport protein (Mctb). ADME-based virtual screening, molecular docking, and molecular dynamics simulations were conducted to find promising compounds to modulate the function of the target protein. Four chemical compounds, namely Anti-MCT1, Anti-MCT2, Anti-MCT3 and Anti-MCT4 out of 1500 small molecules from the Diverse-lib of MTiOpenScreen were observed to completely satisfy Lipinski rule of five and Veber's rule. Further, significantly steady interactions with the MctB target protein were observed. Docking experiments have presented 9 compounds with less than -9.0 kcal/mol free binding energies and further MD simulation eventually gave 4 compounds having potential interactions and affinity with target protein and favorable binding energy ranging from -9.2 to -9.3 kcal/mol. We may propose these compounds as an effective candidate to reduce the growth of M. tuberculosis and may also assist present a novel therapeutic approach for Tuberculosis. In vivo and In vitro validation would be needed to proceed further in this direction.Communicated by Ramaswamy H. Sarma.

Exploring the Antibacterial Potential of Artemisia judaica Compounds Targeting the Hydrolase/Antibiotic Protein in Klebsiella pneumoniae: In Vitro and In Silico Investigations

Carbapenem antibiotic resistance is an emerging medical concern. Bacteria that possess the Klebsiella pneumoniae carbapenemase (KPC) protein, an enzyme that catalyzes the degradation of carbapenem antibiotics, have exhibited remarkable resistance to traditional and even modern therapeutic approaches. This study aimed to identify potential natural drug candidates sourced from the leaves of Artemisia judaica (A. judaica). The phytoconstituents present in A. judaica dried leaves were extracted using ethanol 80%. A reasonable amount of the extract was used to identify these phytochemicals via gas chromatography/mass spectrometry (GC/MS). One hundred twenty-two bioactive compounds from A. judaica were identified and subjected to docking analysis against the target bacterial protein. Four compounds (PubChem CID: 6917974, 159099, 628694, and 482788) were selected based on favorable docking scores (-9, -7.8, -7.7, and -7.5 kcal/mol). This computational investigation highlights the potential of these four compounds as promising antibacterial candidates against the specific KPC protein. Additionally, in vitro antibacterial assays using A. judaica extracts were conducted. The minimum inhibitory concentration (MIC) against the bacterium K. pneumonia was 125 μg/mL. Well-disk diffusion tests exhibited inhibition zones ranging from 10.3 ± 0.5 mm to 17 ± 0.5 mm at different concentrations, and time-kill kinetics at 12 h indicated effective inhibition of bacterial growth by A. judaica leaf extracts. Our findings have revealed the pharmaceutical potential of Artemisia judaica as a natural source for drug candidates against carbapenem-resistant pathogens.

Molecular Dynamic Simulations and Molecular Docking as a Potential Way for Designed New Inhibitor Drug without Resistance

Mycobacterium tuberculosis is the cause of tuberculosis in humans and is responsible for more than 2 million deaths per year. Despite the development of anti-tuberculosis drugs (Isoniazid, Rifampicin, Ethambutol, pyrazinamide, streptomycin, etc.) and the TB vaccine, this disease has claimed the lives of many people around the world. Drug resistance in this disease is increasing day by day. Conventional methods for discovering and developing drugs are usually time-consuming and expensive. Therefore, a better method is needed to identify, design, and manufacture TB drugs without drug resistance. Bioinformatics applications in obtaining new drugs at the structural level include studies of the mechanism of drug resistance, detection of drug interactions, and prediction of mutant protein structure. In the present study, computer-based approaches including molecular dynamics simulation and molecular docking as a novel and efficient method for the identification and investigation of new cases as well as the investigation of mutated proteins and compounds will be examined .

Drug Discovery for Mycobacterium tuberculosis Using Structure-Based Computer-Aided Drug Design Approach

Developing new, more effective antibiotics against resistant Mycobacterium tuberculosis that inhibit its essential proteins is an appealing strategy for combating the global tuberculosis (TB) epidemic. Finding a compound that can target a particular cavity in a protein and interrupt its enzymatic activity is the crucial objective of drug design and discovery. Such a compound is then subjected to different tests, including clinical trials, to study its effectiveness against the pathogen in the host. In recent times, new techniques, which involve computational and analytical methods, enhanced the chances of drug development, as opposed to traditional drug design methods, which are laborious and time-consuming. The computational techniques in drug design have been improved with a new generation of software used to develop and optimize active compounds that can be used in future chemotherapeutic development to combat global tuberculosis resistance. This review provides an overview of the evolution of tuberculosis resistance, existing drug management, and the design of new anti-tuberculosis drugs developed based on the contributions of computational techniques. Also, we show an appraisal of available software and databases on computational drug design with an insight into the application of this software and databases in the development of anti-tubercular drugs. The review features a perspective involving machine learning, artificial intelligence, quantum computing, and CRISPR combination with available computational techniques as a prospective pathway to design new anti-tubercular drugs to combat resistant tuberculosis.

Anti-mycobacterial natural products and mechanisms of action

Covering: up to June, 2020Tuberculosis (TB) continues to be a major disease with high mortality and morbidity globally. Drug resistance and long duration of treatment make antituberculosis drug discovery more challenging. In this review, we summarize recent advances on anti-TB natural products (NPs) and their potential molecular targets in cell wall synthesis, protein production, energy generation, nucleic acid synthesis and other emerging areas. We highlight compounds with activity against drug-resistant TB, and reveal several novel targets including Mtb biotin synthase, ATP synthase, 1,4-dihydroxy-2-naphthoate prenyltransferase and biofilms. These anti-TB NPs and their targets could facilitate target-based screening and accelerate TB drug discovery.

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.

In Ovo and In Silico Evaluation of the Anti-Angiogenic Potential of Syringin

INTRODUCTION

Cancer is considered as one of the deadliest human diseases today. Angiogenesis, the propagation of new blood vessels from pre-existing vasculature, is a critical step in the progression of cancer as it is essential in the growth and metastasis of tumors. Hence, suppression of angiogenesis is a promising approach in cancer therapy. Syringin, a phenylpropanoid glycoside with a molecular formula of C17H24O9, has been found to exhibit chemopreventive effects. However, its anti-angiogenic activity and the underlying mechanism of action are still unknown.

METHODS

In this work, in ovo chorioallantoic membrane (CAM) assay has been conducted to evaluate the effect of syringin on neovascularization. Additionally, reverse molecular docking studies have been performed in order to identify the probable enzyme targets in the angiogenesis pathway.

RESULTS

Treatment with syringin showed significant dose-dependent inhibition of blood vessel length and junctions in the CAM of duck eggs; the anti-angiogenic activity of syringin at 100 µM and 200 µM is comparable with 200 µM of the positive control celecoxib. The results of reverse docking studies indicate that syringin binds the strongest to dihydrofolate reductase (DHFR) and, to some extent, with transforming growth factor-beta receptor type 1 (TGF-βR1), vascular endothelial growth factor receptor 2 (VEGFR2), and matrix metalloproteinase-2 (MMP-2). Furthermore, ADMET models revealed that syringin potentially possesses excellent pharmacokinetic and toxicity profiles.

CONCLUSION

This study demonstrates the potential of syringin as an anti-angiogenic agent and elicits further investigations to establish its application in cancer suppression.

Interaction of Isocitrate Lyase with Proteins Involved in the Energetic Metabolism in Paracoccidioides lutzii

Background: Systemic mycosis is a cause of death of immunocompromised subjects. The treatment directed to evade fungal pathogens shows severe limitations, such as time of drug exposure and side effects. The paracoccidioidomycosis (PCM) treatment depends on the severity of the infection and may last from months to years. Methods: To analyze the main interactions of Paracoccidioides lutzii isocitrate lyase (ICL) regarding the energetic metabolism through affinity chromatography, we performed blue native PAGE and co-immunoprecipitation to identify ICL interactions. We also performed in silico analysis by homology, docking, hot-spot prediction and contact preference analysis to identify the conformation of ICL complexes. Results: ICL interacted with 18 proteins in mycelium, 19 in mycelium-to-yeast transition, and 70 in yeast cells. Thirty complexes were predicted through docking and contact preference analysis. ICL has seven main regions of interaction with protein partners. Conclusions: ICL seems to interfere with energetic metabolism of P. lutzii, regulating aerobic and anaerobic metabolism as it interacts with proteins from glycolysis, gluconeogenesis, TCA and methylcitrate cycles, mainly through seven hot-spot residues.

Structure and Function of L,D- and D,D-Transpeptidase Family Enzymes from Mycobacterium tuberculosis

Peptidoglycan, the exoskeleton of bacterial cell and an essential barrier that protects the cell, is synthesized by a pathway where the final steps are catalysed by transpeptidases. Knowledge of the structure and function of these vital enzymes that generate this macromolecule in M. tuberculosis could facilitate the development of potent lead compounds against tuberculosis. This review summarizes the experimental and computational studies to date on these aspects of transpeptidases in M. tuberculosis that have been identified and validated. The reported structures of L,D- and D,D-transpeptidases, as well as their functionalities, are reviewed and the proposed enzymatic mechanisms for L,D-transpeptidases are summarized. In addition, we provide bioactivities of known tuberculosis drugs against these enzymes based on both experimental and computational approaches. Advancing knowledge about these prominent targets supports the development of new drugs with novel inhibition mechanisms overcoming the current need for new drugs against tuberculosis.

Breaking down the cell wall: Strategies for antibiotic discovery targeting bacterial transpeptidases

The enzymes involved in bacterial cell wall synthesis are established antibiotic targets, and continue to be a central focus for antibiotic development. Bacterial penicillin-binding proteins (and, in some bacteria, l,d-transpeptidases) form essential peptide cross-links in the cell wall. Although the β-lactam class of antibiotics target these enzymes, bacterial resistance threatens their clinical use, and there is an urgent unmet need for new antibiotics. However, the search for new antibiotics targeting the bacterial cell wall is hindered by a number of obstacles associated with screening the enzymes involved in peptidoglycan synthesis. This review describes recent approaches for measuring the activity and inhibition of penicillin-binding proteins and l,d-transpeptidases, highlighting strategies that are poised to serve as valuable tools for high-throughput screening of transpeptidase inhibitors, supporting the development of new antibiotics.

In Silico Strategies in Tuberculosis Drug Discovery

Tuberculosis (TB) remains a serious threat to global public health, responsible for an estimated 1.5 million mortalities in 2018. While there are available therapeutics for this infection, slow-acting drugs, poor patient compliance, drug toxicity, and drug resistance require the discovery of novel TB drugs. Discovering new and more potent antibiotics that target novel TB protein targets is an attractive strategy towards controlling the global TB epidemic. In silico strategies can be applied at multiple stages of the drug discovery paradigm to expedite the identification of novel anti-TB therapeutics. In this paper, we discuss the current TB treatment, emergence of drug resistance, and the effective application of computational tools to the different stages of TB drug discovery when combined with traditional biochemical methods. We will also highlight the strengths and points of improvement in in silico TB drug discovery research, as well as possible future perspectives in this field.

Identification of potent L,D-transpeptidase 5 inhibitors for Mycobacterium tuberculosis as potential anti-TB leads: virtual screening and molecular dynamics simulations

Virtual screening is a useful in silico approach to identify potential leads against various targets. It is known that carbapenems (doripenem and faropenem) do not show any reasonable inhibitory activities against L,D-transpeptidase 5 (Ldt_Mt5) and also an adduct of meropenem exhibited slow acylation. Since these drugs are active against L,D-transpeptidase 2 (Ldt_Mt2), understanding the differences between these two enzymes is essential. In this study, a ligand-based virtual screening of 12,766 compounds followed by molecular dynamics (MD) simulations was applied to identify potential leads against Ldt_Mt5. To further validate the obtained virtual screening ranking for Ldt_Mt5, we screened the same libraries of compounds against Ldt_Mt2 which had more experimetal and calculated binding energies reported. The observed consistency between the binding affinities of Ldt_Mt2 validates the obtained virtual screening binding scores for Ldt_Mt5. We subjected 37 compounds with docking scores ranging from - 7.2 to - 9.9 kcal mol^-1 obtained from virtual screening for further MD analysis. A set of compounds (n = 12) from four antibiotic classes with ≤ - 30 kcal mol^-1 molecular mechanics/generalized born surface area (MM-GBSA) binding free energies (ΔG_bind) was characterized. A final set of that, all β-lactams (n = 4), was considered. The outcome of this study provides insight into the design of potential novel leads for Ldt_Mt5. Graphical abstract.

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.

Advances and challenges in drug design against tuberculosis: application of in silico approaches

INTRODUCTION

Tuberculosis (TB) caused by Mycobacterium tuberculosis (Mtb) remains the deadliest infectious disease in the world with one-third of the world's population thought to be infected. Over the years, TB mortality rate has been largely reduced; however, this progress has been threatened by the increasing appearance of multidrug-resistant Mtb. Considerable recent efforts have been undertaken to develop new generation antituberculosis drugs. Many of these attempts have relied on in silico approaches, which have emerged recently as powerful tools complementary to biochemical attempts. Areas covered: The authors review the status of pharmaceutical drug development against TB with a special emphasis on computational work. They focus on those studies that have been validated by in vitro and/or in vivo experiments, and thus, that can be considered as successful. The major goals of this review are to present target protein systems, to highlight how in silico efforts compliment experiments, and to aid future drug design endeavors. Expert opinion: Despite having access to all of the gene and protein sequences of Mtb, the search for new optimal treatments against this deadly pathogen are still ongoing. Together with the geometric growth of protein structural and sequence databases, computational methods have become a powerful technique accelerating the successful identification of new ligands.

In silico docking and molecular dynamics simulation of 3-dehydroquinate synthase (DHQS) from Mycobacterium tuberculosis

The shikimate pathway is as an attractive target because it is present in bacteria, algae, fungi, and plants but does not occur in mammals. In Mycobacterium tuberculosis (MTB), the shikimate pathway is integral to the biosynthesis of naphthoquinones, menaquinones, and mycobactin. In these study, novel inhibitors of 3-dehydroquinate synthase (DHQS), an enzyme that catalyzes the second step of the shikimate pathway in MTB, were determined. 12,165 compounds were selected from two public databases through virtual screening and molecular docking analysis using PyRx 8.0 and Autodock 4.2, respectively. A total of 18 compounds with the best binding energies (-13.23 to -8.22 kcal/mol) were then selected and screened for absorption, distribution, metabolism, excretion, and toxicity (ADMET) analysis, and nine of those compounds were found to satisfy all of the ADME and toxicity criteria. Among those nine, the three compounds-ZINC633887 (binding energy = -10.29 kcal/mol), ZINC08983432 (-9.34 kcal/mol), and PubChem73393 (-8.61 kcal/mol)-with the best binding energies were further selected for molecular dynamics (MD) simulation analysis. The results of the 50-ns MD simulations showed that the two compounds ZINC633887 and PubChem73393 formed stable complexes with DHQS and that the structures of those two ligands remained largely unchanged at the ligand-binding site during the simulations. These two compounds identified through docking and MD simulation are potential candidates for the treatment of TB, and should undergo validation in vivo and in vitro.

Design and virtual screening of novel fluoroquinolone analogs as effective mutant DNA GyrA inhibitors against urinary tract infection-causing fluoroquinolone resistant Escherichia coli

Fluoroquinolones (FQs) belong to the class of quinolone drugs that are used to treat Urinary tract infections (UTIs) through inhibition of E. coli DNA gyrase. Resistance to FQs poses a serious problem in the treatment against resistant strains of E. coli which are associated with Ser83 to Leu and Asp87 to Asn mutations at the quinolone resistance determining region (QRDR) of the GyrA subunit of DNA gyrase. Mutant DNA GyrA (mtDNA GyrA) is deemed to be a significant target for the development of novel FQ drugs. Due to resistance to FQ drugs, discovery or development of novel FQs is crucial to inhibit the mtDNA GyrA. Hence, the present study attempts to design and develop novel FQs that are efficient against resistant E. coli strains. A three-dimensional structure of the mtDNA GyrA protein was developed by homology modeling, following which 204 novel FQ analogs were designed using target based SAR. The designed ligands were then screened using molecular docking studies, through which the pattern of interaction between the ligands and the target protein was studied. As expected, the results of the docking study revealed that the molecules FQ-147, FQ-151 and FQ-37 formed hydrogen bonding and Van der Waals interactions with Leu83 and Asn87 (mutated residues), respectively. Further, the wild-type (WT), mtDNA GyrA and docking complex were studied by molecular dynamics (MD) simulations. Subsequently, all the screened compounds were subjected to a structure and ligand based pharmacophore study followed by ADMET and toxicity (TOPKAT) prediction. Finally, eighteen hit FQ analogs which showed good results for the following properties, viz., best binding score, estimated activity (MIC value) and calculated drug-like properties, and least toxicity, were shortlisted and identified as potential leads to treat UTI caused by FQ resistant E. coli. Apart from development of novel drug candidates for inhibition of mtDNA GyrA, the present study also contributes towards a superior comprehension of the interaction pattern of ligands in the target protein. To a more extensive degree, the present work will be useful for the rational design of novel and potent drugs for UTIs.

Design and development of novel fluoroquinolones analogs using target (mutant DNA GyrA), ligand-based SAR and virtual screening techniques.

In silico discovery and in vitro activity of inhibitors against Mycobacterium tuberculosis 7,8-diaminopelargonic acid synthase (Mtb BioA)

Computer-aided drug discovery and development approaches such as virtual screening, molecular docking, and in silico drug property calculations have been utilized in this effort to discover new lead compounds against tuberculosis. The enzyme 7,8-diaminopelargonic acid aminotransferase (BioA) in Mycobacterium tuberculosis (Mtb), primarily involved in the lipid biosynthesis pathway, was chosen as the drug target due to the fact that humans are not capable of synthesizing biotin endogenously. The computational screening of 4.5 million compounds from the Enamine REAL database has ultimately yielded 45 high-scoring, high-affinity compounds with desirable in silico absorption, distribution, metabolism, excretion, and toxicity properties. Seventeen of the 45 compounds were subjected to bioactivity validation using the resazurin microtiter assay. Among the 4 actives, compound 7 ((Z)-N-(2-isopropoxyphenyl)-2-oxo-2-((3-(trifluoromethyl)cyclohexyl)amino)acetimidic acid) displayed inhibitory activity up to 83% at 10 μg/mL concentration against the growth of the Mtb H37Ra strain.

Exploring the Molecular Basis for Binding of Inhibitors by Threonyl-tRNA Synthetase from Brucella abortus: A Virtual Screening Study

Targeting threonyl-tRNA synthetase (ThrRS) of Brucella abortus is a promising approach to developing small-molecule drugs against bovine brucellosis. Using the BLASTp algorithm, we identified ThrRS from Escherichia coli (EThrRS, PDB ID 1QF6), which is 51% identical to ThrRS from Brucella abortus (BaThrRS) at the amino acid sequence level. EThrRS was used as the template to construct a BaThrRS homology model which was optimized using molecular dynamics simulations. To determine the residues important for substrate ATP binding, we identified the ATP-binding regions of BaThrRS, docked ATP to the protein, and identified the residues whose side chains surrounded bound ATP. We then used the binding site of ATP to virtually screen for BaThrRS inhibitors and got seven leads. We further characterized the BaThrRS-binding site of the compound with the highest predicted inhibitory activity. Our results should facilitate future experimental effects to find novel drugs for use against bovine brucellosis.