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01.12.2017 | Research | Ausgabe 1/2017 Open Access

Journal of Translational Medicine 1/2017

Structure based drug discovery for designing leads for the non-toxic metabolic targets in multi drug resistant Mycobacterium tuberculosis

Zeitschrift:
Journal of Translational Medicine > Ausgabe 1/2017
Autoren:
Divneet Kaur, Shalu Mathew, Chinchu G. S. Nair, Azitha Begum, Ashwin K. Jainanarayan, Mukta Sharma, Samir K. Brahmachari
Wichtige Hinweise

Electronic supplementary material

The online version of this article (https://​doi.​org/​10.​1186/​s12967-017-1363-9) contains supplementary material, which is available to authorized users.

Abstract

Background

The problem of drug resistance and bacterial persistence in tuberculosis is a cause of global alarm. Although, the UN’s Sustainable Development Goals for 2030 has targeted a Tb free world, the treatment gap exists and only a few new drug candidates are in the pipeline. In spite of large information from medicinal chemistry to ‘omics’ data, there has been a little effort from pharmaceutical companies to generate pipelines for the development of novel drug candidates against the multi drug resistant Mycobacterium tuberculosis.

Methods

In the present study, we describe an integrated methodology; utilizing systems level information to optimize ligand selection to lower the failure rates at the pre-clinical and clinical levels. In the present study, metabolic targets (Rv2763c, Rv3247c, Rv1094, Rv3607c, Rv3048c, Rv2965c, Rv2361c, Rv0865, Rv0321, Rv0098, Rv0390, Rv3588c, Rv2244, Rv2465c and Rv2607) in M. tuberculosis, identified using our previous Systems Biology and data-intensive genome level analysis, have been used to design potential lead molecules, which are likely to be non-toxic. Various in silico drug discovery tools have been utilized to generate small molecular leads for each of the 15 targets with available crystal structures.

Results

The present study resulted in identification of 20 novel lead molecules including 4 FDA approved drugs (droxidropa, tetroxoprim, domperidone and nemonapride) which can be further taken for drug repurposing. This comprehensive integrated methodology, with both experimental and in silico approaches, has the potential to not only tackle the MDR form of Mtb but also the most important persister population of the bacterium, with a potential to reduce the failures in the Tb drug discovery.

Conclusion

We propose an integrated approach of systems and structural biology for identifying targets that address the high attrition rate issue in lead identification and drug development We expect that this system level analysis will be applicable for identification of drug candidates to other pathogenic organisms as well.
Zusatzmaterial
Additional file 1: Figure S1. Docking with the best GSK molecule (docking score= −10.88) for folA (Rv2763c). Figure S2. E-pharmacophore of Trimethoprim (NS) Vs the library of compounds generated based on similar results forfolA (Rv2763c). Figure S3. Docking with NS and the best- docked molecule (docking score= −7.08) for folB (Rv3607c). Figure S4. Highest docking score with best binding pose match= −11.55 for tmk(Rv3247c). Figure S5. Top 2 poses for the best docked GSK molecule, docking score= −11.08 for tmk(Rv3247c). Figure S6. (a) Docking pattern of the lead compound ChEMBL533912 in to the enzyme BifunctionaldCTPdeaminase (Rv0321, PDB ID: 2QXX).ChEMBL533912 showed polar contacts with Tyr162, Ser167 and Ala167 along with NSThymidine-5’-Triphosphate –TTPrespectively.Hydrogen bond interactions are represented in yellow dotted lines. The Natural substrate is represented as red stick;(b) Docked conformation of protein Involved in Molybdopterin biosynthesis (Rv0865, PDB ID 2G4R) with top ranked ligand ChEMBL255979, showing the interaction with the crucial residues Ser13 with two hydrogen bond interactions and Val 11 in the active site. Figure S7. Best pose and interaction diagram for the best-docked non-GSK molecule (docking score= −7.41) for nrdF2 (Rv3048c). Figure S8. 2 Best poses with highest binding GSK compound and its interaction diagram (Docking score= −9.01) for nrdF2 (Rv3048c). Figure S9. Binding pattern of two lead compounds on to the hypothetical protein (Rv0098, PDB ID 2PFC); (a) ChEMBL3349754, showing the hydrophobic interaction with Asn83, Met118, Lue115 and Tyr87; (F)ChEMBL3037996Showed hydrophobic environment around binding site residues Met 118, Ile120 and Tyr87.Red color stick showing the NS Palmitic acid –PLM and hydrogen bond interactions are represented in yellow dotted lines. Figure S10. Best Docking score= −6.79 and interaction diagram obtained from the entire library of molecules for desA2 (Rv1094). Figure S11. Best poses for the top ranked GSK molecule for kdtB (Rv2965c). Figure S12. (a). Top poses of the best-docked GSK molecules, (b). Docking performed with 426 GSK molecules, (c). Interaction diagram for the best GSK molecule foruppS (Rv2361c). Figure S13. Docked complex of lead compounds into protein Rhodanese-related sulfurtransferase(Rv0390, PDB ID 2FSX); (a) ChEMBL217735Showed hydrophobic interaction with Phe64,His60,Ala66,Ilu65 and Asp62 represented by yellow dotted lines; (b) ChEMBL76817Showed hydrophobic interaction with binding site residues Arg 71are represented by yellow dotted lines. Figure S14. Generated Common Pharmacophore hypotheses for a set of 5 targets as: (a) ARR : (b) AAP, (c) AHR: (d) AAP: (e) AAH (green sphere/circle: hydrophobic group, orange ring: aromatic ring, Pink sphere/circle: hydrogen bond acceptor, light-blue sphere: hydrogen bond donors, blue sphere positively charged group). Figure S15. Pharmacophore feature(AHR hypothesis) mapping of external test set compounds Hydrophobic features (H7) mapped over carboxyl group, and the second hydrogen bond acceptor (A2), feature mapped over alkene group of Rifampicin with fit value of 4.74. Figure S16. Distributions of violations of Lipinski’s ro5 and Jorgensen’s ro3 within the compound library for Rv2763c. Figure S17. Scatter diagrams showing pair wise distribution of “drug-likeness” descriptors, MW against Predicted Octanol-Water Coefficient,QlogP for Rv2763c. Figure S18. Histogram plot of the count of the compounds VsMol. Wt. and Predicted Octanol-Water Coefficient, QlogP, Drug like range: −2.0–6.5, for Rv2763c.
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