Topical Perspectives
Virtual screening and free energy estimation for identifying Mycobacterium tuberculosis flavoenzyme DprE1 inhibitors

https://doi.org/10.1016/j.jmgm.2020.107770Get rights and content

Highlights

  • Identifying the inhibitors for Mycobacterium tuberculosis cell wall biosynthesis flavoenzyme, DprE1.

  • Virtual screening of bioactive anti-tuberculosis molecules from ChEMBL database.

  • Multistage molecular docking and binding affinity analyses.

  • Bioavailability (ADME) and toxicity filtering of hit molecules.

  • Molecular Dynamics, MM-GBSA, MM-PBSA and MM-3D-RISM analysis.

Abstract

In Mycobacterium tuberculosis (MTB), the cell wall synthesis flavoenzyme decaprenylphosphoryl-β-d-ribose 2′-epimerase (DprE1) plays a crucial role in host pathogenesis, virulence, lethality and survival under stress. The emergence of different variants of drug resistant MTB are a major threat worldwide which essentially requires more effective new drug molecules with no major side effects. Here, we used structure based virtual screening of bioactive molecules from the ChEMBL database targeting DprE1, having bioactive 78,713 molecules known for anti-tuberculosis activity. An extensive molecular docking, binding affinity and pharmacokinetics profile filtering results in the selection four compounds, C5 (ChEMBL2441313), C6 (ChEMBL2338605), C8 (ChEMBL441373) and C10 (ChEMBL1607606) which may explore as potential drug candidates. The obtained results were validated with thirteen known DprE1 inhibitors. We further estimated the free-binding energy, solvation and entropy terms underlying the binding properties of DprE1-ligand interactions with the implication of MD simulation, MM/GBSA, MM/PBSA and MM/3D-RISM. Interestingly, we find that C6 shows the highest ΔG scores (−41.28 ± 3.51, −22.36 ± 3.17, −10.33 ± 5.70 kcal mol−1) in MM/GBSA, MM/PBSA and MM/3D-RISM assay, respectively. Whereas, the lowest ΔG scores (−35.31 ± 3.44, −13.67 ± 2.65, −3.40 ± 4.06 kcal mol−1) observed for CT319, the inhibitor co-crystallized with DprE1. Collectively, the results demonstrated that hit-molecules: C5, C6, C8 and C10 having better binding free energy and molecular affinity as compared to CT319. Thus, we proposed that selected compounds may be explored as lead molecules in MTB therapy.

Introduction

Mycobacterium tuberculosis (MTB) is a slow growing and widely spread pathogen, survive in both, intra-cellular and extracellular systems of patients, and infection may result in chronic and complex disease state. During the treatment, it can go to latency which revert to exponential growth on the immune defiance conditions of hosts [1,2]. In recent years, WHO reports suggested that around 10.0 million (range, 9.0–11.1 million) individuals infected and 1.3 million (range, 1.2–1.4 million) people died from tuberculosis (TB) [1]. Moreover, the infection of MTB is one of the major causes of death worldwide, possessing the global health crisis, especially for the immunocompromised and HIV patients [3]. Although, the specific treatment may cure MTB, however, it requires multiple drug therapy for a longer period [1,3]. Furthermore, the development of multi- and extensively-drug-resistant (MDR-TB and XDR-TB) MTB strains are the big challenges to control TB infections [4,5]. In several conditions, it may turn into totally drug-resistant (TDR) tuberculosis which may worsen the condition of patients and therapy [2,6]. Thus, the potential drug candidates, having minimal or no side effects are highly sought in MTB therapy [1,2].

In recent years, several proteins involved in MTB survival and metabolism have been explored as potential drug targets and are progress in the drug development. During the evolution, mycobacteria have developed well-orchestrated and complex biosynthetic pathways to sustain a unique and thick cell wall which helps in maintaining the cellular integrity, survival under stress and dormancy, and eluding the host’s immune systems. In MTB, the cell wall consists of the polymers of mycolyl-arabinogalactan-peptidoglycan, covalently connected with peptidoglycan and trehalose dimycolate that protects from stress, antibiotics and the hots immune systems [7]. The flavoenzyme decaprenylphosphoryl-β-d-ribose 2′-epimerase (DprE1) involve in the biosynthesis of cell wall, plays critical role in formation of peptidoglycan-arbinogalactan-mycolic acid complex (PAM) and arabinogalactan and lipoarabinomannan (LAM) which are the essential building blocks and play crucial role in survival and host pathogenesis, virulence, and lethality. DprE1 catalyses the first stage of epimerization reaction especially in the presence of FAD, it oxidizes C2′ hydroxyl site of DPR to produce the keto intermediary decaprenyl-2′-keto-d-arabinose (DPX) and then DPA is formed by using decaprenyl-phosphoryl-D-2-keto-erythro-pentose reductase (DprE2) and reduced form of nicotinamide adenine dinucleotide (NADH) as a cofactor [[8], [9], [10]]. Thus, the catalytic activity of DprE1 is one of the potential drug targets in the development of tuberculosis therapy [2,4,7]. Recently, the benzothiazinones (BTZs) derivatives have shown higher potency for inhibition of DprE1, and efficacy against XDR and MDR mycobacterium clinical isolates.

To improve the pharmacological properties of the compounds, chemical scaffold piperazine was added to BTZ. Further, the lead optimization of PBTZ derivatives results in the discovery of more potent compounds which are currently in clinical trials [5,8,11]. In this view, several structurally distinct chemical scaffolds are in drug screening as DprE1 inhibitors. Broadly, these inhibitors can be categorized as covalent or noncovalent, distinctly involved in interaction at the catalytic domain of DprE1 [8,11]. To elucidate the action and interaction of BTZs compounds, Batt el al., solved the X-ray crystal structure of DprE1 in both, ligand free and bound form. He found that the structure of DprE1 consists of two functional domains, FAD binding domain and substrate binding domain. The co-factor was buried deeply in highly conserved FAD domain. The substrate binding extended for FAD, decorated largely with antiparallel β-strands (β10-16) and included disordered loops at surface which govern the wide and open active site. The nitroaromatic inhibitors (e.g., BTZ, VI-9376, nitroimidazole 377790) possesses nitro moiety which involved in covalent interaction at C387, whereas, the noncovalent inhibitors (e.g., TCA1, 1,4-azaindoles, pyrazolopyridones, 4-aminoquinolone piperidine amides, Ty38c) potentially inhibit the enzymatic function of DprE1 showed that hydrophobic, electrostatic, and van der Waals interactions are critical for the spatial stability of inhibitors at the active site of DprE1 [5,11]. Thus, the exploration of crystal structure of DprE1 has been largely facilitated the drug discovery efforts to tend the molecules effective against MDR and XDR strains [2,5,8,11]. Traditional approach for the development of broad-spectrum anti-tuberculosis drugs have been proven ineffective, due to the lack of three-dimensional structure molecular targets [12,13].

Recent studies on the development of DprE1 inhibitors suggested a major contribution of molecular modelling, high throughput screening, docking, functional genomics and proteomics in paradigm of identifying novel chemical scaffolds as potential molecules for TB chemotherapy [5,8,[11], [12], [13], [14]]. Although, molecular docking programs provide the description of protein-ligand interactions [14]. However, a better understanding of protein-ligands interactions requires an accurate description of the spatial orientation of ligands at the active site of protein, conformational dynamics of protein and active sites residues, interaction energy and molecular stability [[15], [16], [17]]. In this view, MD simulation is an efficient and well-established computational method which mimics the flexible nature of bio-molecules, protein conformational changes, protein-ligand interactions, structural perturbation and provide more realistic picture with atomic details in reference to time [4,13,[18], [19], [20]]. Moreover, the free binding energy estimation, effect of solvation and thermodynamic integration is the central focus to understand the molecular interactions which can be well achieved by the implication of MM/GBSA, MM/PBSA and MM/3D-RISM using the trajectories obtained from MD simulation [15,[21], [22], [23], [24]].

In this context, we employed the structure based virtual screening for identification of promising chemical entities as DprE1 inhibitors from the ChEMBL database. We find that 78,713 small molecules at ChEMBL database suggested for the anti-mycobacterial activity. The three steps molecular docking and binding affinity estimation process lead to the selection of 10 hit-molecules. Similar procedures were applied on the selected 13 DprE1 inhibitors for the comparison of results with hit-molecules. Multiple MD simulations were performed on the DprE1 complex with hit-molecules and inhibitor (CT319) and the spatial stability of ligand molecules at active site of protein was estimated in terms of binding free energy using MM/PBSA/GBSA, and MM/3D-RISM [18,21,22]. The extensive evaluation of pharmacokinetic profile and drug-likeness properties analyses suggested that four chemical entities, compounds C5 (ChEMBL2441313), C6 (ChEMBL2338605), C8 (ChEMBL441373) and C10 (ChEMBL1607606) may be explored as potential lead molecules for the development of promising DprE1 inhibitors in MTB therapy.

Section snippets

Protein preparation

The X-ray structure of DprE1 with inhibitor CT319 and cofactor FAD (PDB ID: 4FDO) was taken from the protein data bank (www.rcsb.org) [25]. The structure of DprE1 consist of two domains, the FAD binding domain comprised with α/β folds (residues 7–196, 413–461) and another domain, substrate binding includes extended conformation and antiparallel β-sheets (residues 197–412). In the crystal structure, the spatial orientation of FAD-binding domain and residues involved in interactions were highly

Results and discussion

The drug development process involves several expansive steps and complex strategies. Recent advancement in the computational modelling techniques, molecular docking, high-throughput virtual screening, pharmacokinetic profile (ADME), toxicity and bioavailability analyses of the molecules have been perceived as well-established techniques to accelerate the drug development processes [16,37,[57], [58], [59], [60]]. Further, the integration of MD simulation and estimation of free-binding energy

Conclusion

In conclusion, we have explored structure based virtual screening for the identification of promising chemical entities as DprE1 inhibitors from ChEMBL database. Initial sorting of compounds results in the selection of 30,789 small molecules which are suggested for the anti-mycobacterial activity. The three steps molecular docking and binding affinity estimation processes lead to the selection of bioactive 10 hit-molecules. Similar procedures were applied on the selected 13 DprE1-inhibitors

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

NK is thankful to UGC (University Grants Commission) and RS (09/263(1039)/2014-EMR-I) is thankful to CSIR (Council of Scientific and Industrial Research) for providing fellowship. Authors gratefully acknowledge the computational facility funded by Science and Engineering Research Board (SERB), Government of India (Ref. No.: YSS/2015/000228/LS).

References (71)

  • G.S. Kolly et al.

    Assessing the essentiality of the decaprenyl-phospho-d-arabinofuranose pathway in Mycobacterium tuberculosis using conditional mutants

    Mol. Microbiol.

    (2014)
  • V. Makarov et al.

    Towards a new combination therapy for tuberculosis with next generation benzothiazinones

    EMBO Mol. Med.

    (2014)
  • S.M. Batt et al.

    Structural basis of inhibition of Mycobacterium tuberculosis DprE1 by benzothiazinone inhibitors

    Proc. Natl. Acad. Sci. U. S. A.

    (2012)
  • R. Reddyrajula et al.

    The bioisosteric modification of pyrazinamide derivatives led to potent antitubercular agents: synthesis via click approach and molecular docking of pyrazine-1,2,3-triazoles

    Bioorg. Med. Chem. Lett

    (2020)
  • N. Das et al.

    Arabinosyltransferase C enzyme of Mycobacterium tuberculosis, a potential drug target: an insight from molecular docking study

    Heliyon

    (2020)
  • B.M. Sahoo et al.

    Molecular Docking and Microwave Assisted Green Synthesis of Pyrimidine Derivatives as Potential Anti-tubercular Agent Proceedings of International Conference on Drug Discovery

    (2020)
  • S. Horoiwa et al.

    Structure-based virtual screening for insect ecdysone receptor ligands using MM/PBSA

    Bioorg. Med. Chem.

    (2019)
  • A. Prakash et al.

    Receptor chemoprint derived pharmacophore model for development of CAIX inhibitors

    J. Carcinog. Mutagen.

    (2013)
  • E. Lionta et al.

    Structure-based virtual screening for drug discovery: principles, applications and recent advances

    Curr. Top. Med. Chem.

    (2014)
  • A. Prakash et al.

    Elucidation of stable intermediates in urea-induced unfolding pathway of human carbonic anhydrase IX

    J. Biomol. Struct. Dyn.

    (2018)
  • I. Sarkar et al.

    Quantitative structure–activity relationship (QSAR) study of some DNA-intercalating anticancer drugs

    Computational Advancement in Communication Circuits and Systems

    (2020)
  • S. Genheden et al.

    An MM/3D-RISM approach for ligand binding affinities

    J. Phys. Chem. B

    (2010)
  • R. Kumari et al.

    g_mmpbsa--a GROMACS tool for high-throughput MM-PBSA calculations

    J. Chem. Inf. Model.

    (2014)
  • J. Wang et al.

    Develop and test a solvent accessible surface area-based model in conformational entropy calculations

    J. Chem. Inf. Model.

    (2012)
  • H.M. Berman et al.

    The protein data bank

    Nucleic Acids Res.

    (2000)
  • E.F. Pettersen et al.

    UCSF Chimera--a visualization system for exploratory research and analysis

    J. Comput. Chem.

    (2004)
  • G.M. Sastry et al.

    Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments

    J. Comput. Aided Mol. Des.

    (2013)
  • A. Gaulton et al.

    ChEMBL: a large-scale bioactivity database for drug discovery

    Nucleic Acids Res.

    (2012)
  • D. Weininger

    SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules

    J. Chem. Inf. Comput. Sci.

    (1988)
  • J. Sadowski et al.

    Comparison of automatic three-dimensional model builders using 639 X-ray structures

    J. Chem. Inf. Comput. Sci.

    (1994)
  • G. Guillemette et al.

    Two Ca2+ transport systems are distinguished on the basis of their Mg2+ dependency in a post-nuclear particulate fraction of bovine adrenal cortex

    Cell Calcium

    (1991)
  • J.C. Shelley et al.

    Epik: a software program for pK( a ) prediction and protonation state generation for drug-like molecules

    J. Comput. Aided Mol. Des.

    (2007)
  • W.L. Jorgensen et al.

    Potential energy functions for atomic-level simulations of water and organic and biomolecular systems

    Proc. Natl. Acad. Sci. U. S. A.

    (2005)
  • W.L. Jorgensen et al.

    Development and testing of the OPLS all-atom force field on conformational energetics and properties of organic liquids

    J. Am. Chem. Soc.

    (1996)
  • T.A. Halgren et al.

    Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening

    J. Med. Chem.

    (2004)
  • Cited by (11)

    • Insights into development of Decaprenyl-phosphoryl-β-D-ribose 2′-epimerase (DprE1) inhibitors as antitubercular agents: A state of the art review

      2022, Indian Journal of Tuberculosis
      Citation Excerpt :

      ChEMBL database was used for screening. Molecular docking and dynamics were performed to confirm binding affinity.48 Beteck et al, synthesized novel nitro quinolone-based compounds and tested them in vitro against Mtb and some other species for antibacterial activity.49

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

      2021, Computational and Structural Biotechnology Journal
      Citation Excerpt :

      The calculation of drug-protein binding energies using MMPB(GB)SA has been applied to several drug discovery attempts against M. tuberculosis proteins. These include: LipU [77], GlnA1 [79], DprE1[80], PknA [81], NarL [52] , PanC [141], MurB [68] and MurE [68]. In the majority of cases, Glide was used to perform the initial virtual screen.

    View all citing articles on Scopus
    1

    Authors contributed equally.

    View full text