In silico study directed towards identification of the key structural features of GyrB inhibitors targeting MTB DNA gyrase: HQSAR, CoMSIA and molecular dynamics simulations

ABSTRACT

Mycobacterium tuberculosis DNA gyrase subunit B (GyrB) has been identified as a promising target for rational drug design against fluoroquinolone drug-resistant tuberculosis. In this study, we attempted to identify the key structural feature for highly potent GyrB inhibitors through 2D-QSAR using HQSAR, 3D-QSAR using CoMSIA and molecular dynamics (MD) simulations approaches on a series of thiazole urea core derivatives. The best HQSAR and CoMSIA models based on IC₅₀ and MIC displayed the structural basis required for good activity against both GyrB enzyme and mycobacterial cell. MD simulations and binding free energy analysis using MM-GBSA and waterswap calculations revealed that the urea core of inhibitors has the strongest interaction with Asp79 via hydrogen bond interactions. In addition, cation-pi interaction and hydrophobic interactions of the R₂ substituent with Arg82 and Arg141 help to enhance the binding affinity in the GyrB ATPase binding site. Thus, the present study provides crucial structural features and a structural concept for rational design of novel DNA gyrase inhibitors with improved biological activities against both enzyme and mycobacterial cell, and with good pharmacokinetic properties and drug safety profiles.

KEYWORDS:

• GyrB inhibitors
• binding free energy
• CoMSIA
• HQSAR
• MD simulations
• DNA gyrase

Acknowledgements

This research was supported by the Thailand Research Fund (MRG6180147 and RSA5980057), the Office of the Higher Education Commission, and the Health Systems Research Institute (HSRI.60.083). This work has been facilitated by the BrisSynBio Biosuite (UK Biotechnology and Biological Sciences (BBSRC) and Engineering and Physical Sciences (EPSRC) Research Councils, BB/L01386X/1) and the BBSRC ALERT14 equipment initiative (BB/M012107/1). AJM and JS acknowledge funding from the BristolBridge antimicrobial resistance network (EPSRC EP/M027546/1). We thank CCP-BioSim (grant number EP/M022609/1) for funding. The University of Bristol is gratefully acknowledged for computational resource support of this research. Nakhon Phanom University, Ubon Ratchathani University, NECTEC and University of Bristol are gratefully acknowledged for supporting this research.

Disclosure statement

No potential conflict of interest was reported by the authors.

Supplementary material

Suplementary data for this article can be accessed at: https://doi.org/10.1080/1062936X.2019.1658218.

Additional information

Funding

This work was supported by the Office of the Higher Education Commission [MRG6180147]; CCP-BioSim [EP/M022609/1]; The Thailand Research Fund [RSA5980057, MRG6180147]; The Health Systems Research Institute [HSRI.60.083]; BrisSynBio Biosuite [BB/L01386X/1]; BBSRC ALERT14 equipment initiative [BB/M012107/1]; BristolBridge antimicrobial resistance network [EPSRC EP/M027546/1].