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Computational evaluation of phytocompounds for combating drug resistant tuberculosis by multi-targeted therapy

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Abstract

The cell wall of Mycobacterium tuberculosis interacts with the host counterpart during the pathogenesis of tuberculosis. L-rhamnosyl (L-Rha) residue, a linker connects the arabinogalactan and peptidoglycan moieties in the bacterial cell wall. The biosynthesis of L-rhamnose utilizes four successive enzymes RmlA, RmlB, RmlC and RmlD. Neither rhamnose nor the genes responsible for its synthesis are observed in humans. Thus, drugs inhibiting enzymes of this pathway are unlikely to interfere with metabolic pathways in humans. The adverse drug effects of first and second line drugs along with the development of multi-drug resistance tuberculosis have stimulated the research in search of new therapeutic drugs. Thus, it is attractive to hypothesize that inhibition of the biosynthesis of L-Rha would be lethal to the mycobacteria. Nature provides innumerable secondary metabolites with novel structural architectures with reported activity against M. tuberculosis. Combination of structure based virtual screening with physicochemical and pharmacokinetic studies against rhamnose pathway enzymes identified potential leads. The crucial screening studies recognized four phytocompounds butein, diospyrin, indicanine, and rumexneposide A with good binding affinity towards the rhamnose pathway proteins. Furthermore, the high throughput screening methods recognized butein, a secondary metabolite from Butea monosperma with strong anti-tubercular bioactive spectrum. Butein displayed promising anti-mycobacterial activity which is validated by Microplate alamar blue assay (MABA). The focus on novel agents like these phytocompounds which exhibit preference toward the successive enzymes of a single pathway can prevent the development of bacterial resistance.

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Acknowledgments

We acknowledge CDRI for assay service and VIT University for the computing facility. We thank all the anonymous reviewers for their valuable comments and suggestions.

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Authors and Affiliations

  1. Bioinformatics Division, School of Biosciences and Technology, Vellore Institute of Technology University, Vellore, Tamil Nadu, India, 632014

    Sudharsana Sundarrajan, Sajitha Lulu & Mohanapriya Arumugam

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  1. Sudharsana Sundarrajan
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  2. Sajitha Lulu
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  3. Mohanapriya Arumugam
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Corresponding author

Correspondence to Mohanapriya Arumugam.

Electronic supplementary material

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S. Fig. 1

Rhamnose biosynthesis pathway (DOCX 66 kb)

S. Fig. 2

2D structures of Phytocompounds used in the study (DOCX 434 kb)

S. Fig. 3

RmlC modeled using S. enterica template. (a) RmlC modeled (b) modeled RmlC superimposed with the crystal structure from M.tuberculosis (1UPI) (c) modeled RmlC superimposed with the crystal structure from S. enterica (1DZR). Cyan — modeled, purple — 1UPI, green — 1DZR (DOCX 315 kb)

S. Fig. 4

a: Backbone RMSD of the proteins and b: Potential energy plot of the proteins (brown — RmlA, violet — RmlB, cyan — RmlC, and orange — RmlD) (DOCX 99 kb)

S. Fig. 5

Multiple sequence alignment of RmlA sequences from Mycobacterium tuberculosis, Neisseria meningitides, Haemophilus parainfluenzae, Salmonella enterica, Klebsiella pneumoniae, Escherichia coli, Pseudomonas aeruginosa, and Streptococcus pneumoniae (DOCX 132 kb)

S. Fig. 6

Multiple sequence alignment of RmlB sequences from Mycobacterium tuberculosis, Neisseria meningitides, Haemophilus parainfluenzae, Salmonella enterica, Klebsiella pneumoniae, Escherichia coli, Pseudomonas aeruginosa, and Streptococcus pneumoniae (DOCX 269 kb)

S. Fig. 7

Multiple sequence alignment of RmlD sequences from Mycobacterium tuberculosis, Neisseria meningitides, Haemophilus parainfluenzae, Salmonella enterica, Klebsiella pneumoniae, Escherichia coli, Pseudomonas aeruginosa, and Streptococcus pneumonia (DOCX 221 kb)

S. Fig. 8

RMSD of the protein when bound to inhibitor and substrate. a: RmlA, b:RmlB, c: RmlC, and d: RmlD (DOCX 56 kb)

S. Fig. 9

RMSF of the binding pocket residues when bound to inhibitor and substrate. a: RmlA, b:RmlB, c: RmlC, and d: RmlD (DOCX 73 kb)

S. Fig. 10

Pair-wise alignment of RmlA sequences from H39Rv and H37Ra strains (DOCX 39 kb)

S. Fig. 11

Pair-wise alignment of RmlB sequences from H39Rv and H37Ra strains (DOCX 44 kb)

S. Fig. 12

Pair-wise alignment of RmlC sequences from H39Rv and H37Ra strains (DOCX 33 kb)

S. Fig. 13

Pair-wise alignment of RmlD sequences from H39Rv and H37Ra strains (DOCX 42 kb)

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Sundarrajan, S., Lulu, S. & Arumugam, M. Computational evaluation of phytocompounds for combating drug resistant tuberculosis by multi-targeted therapy. J Mol Model 21, 247 (2015). https://doi.org/10.1007/s00894-015-2785-z

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