In silico studies of alkaloids and their derivatives against N-acetyltransferase EIS protein from Mycobacterium tuberculosis

Abstract

Antibiotic resistance against Mycobacterium tuberculosis (M.tb.) has been a significant cause of death worldwide. The Enhanced intracellular survival (EIS) protein of the bacteria is an acetyltransferase that multiacetylates aminoglycoside antibiotics, preventing them from binding to the bacterial ribosome. To overcome the EIS-mediated antibiotics resistance of M.tb., we compiled 888 alkaloids and derivatives from five different databases and virtually screened them against the EIS receptor. The compound library was filtered down to 87 compounds, which underwent additional analysis and filtration. Moreover, the top 15 most prominent phytocompounds were obtained after the drug-likeness prediction and ADMET screening. Out of 15, nine compounds confirmed the maximum number of hydrogen bond interactions and reliable binding energies during molecular docking. Additionally, the Molecular dynamics (MD) simulation of nine compounds showed the three most stable complexes, further verified by re-docking with mutated protein. The density functional theory (DFT) calculation was performed to identify the HOMO-LUMO energy gaps of the selected three potential compounds. Finally, our selected top lead compounds i.e., Alkaloid AQC2 (PubChem85634496), Nobilisitine A (ChEbi68116), and N-methylcheilanthifoline (ChEbi140673) demonstrated more favourable outcomes when compared with reference compounds (i.e., 39b and 2i) in all parameters used in this study. Therefore, we anticipate that our findings will help to explore and develop natural compound therapy against multi and extensively drug-resistant strains of M.tb.

Communicated by Ramaswamy H. Sarma

Keywords:

• Mycobacterium tuberculosis
• enhanced intracellular survival
• alkaloids
• molecular docking
• structure-based virtual screening
• molecular dynamics simulation
• density functional theory
• phytochemicals
• antibiotics resistance

Acknowledgment

DBT (Department of Biotechnology)-eLibrary Consortium (DeLCON), is acknowledged for providing e-resources. SK acknowledges the BT/PR40146/BTIS/137/4/2020 project grant from the Department of Biotechnology (DBT), Government of India. D.G. is endowed with funding by the Department of Biotechnology, Government of India (BT/PR40151/BTIS/137/5/2021), India. The authors acknowledge the Computational Biology & Bioinformatics Facility (CBBF) of the National Institute of Plant Genome Research (NIPGR).

Author’s contributions

SPS performed all the Structure-Based Virtual Screening, Docking, Drug-likeness prediction, ADMET screening, DFT calculation, and other data analysis. NS and SS performed the MDS. SPS and SK wrote the original draft. SA and DG revised and edited the manuscript. SK designed all the analyses and experiments, conceptualized the study, and supervised the project. All authors read and approved the final version of the manuscript.

Disclosure statement

There are no competing interests that exist. All methods were carried out under relevant guidelines and regulations.

Additional information

Funding

This study was financially supported by the National Institute of Plant Genome Research (NIPGR) core grant in the laboratory of SK.