In-silico identification of potential inhibitors against FabI protein in Klebsiella pneumoniae

Abstract

The development of new antimicrobial drugs is needed to combat multi-drug resistant and novel hypervirulent strains of Klebsiella pneumoniae (KPN) that are associated with increased morbidity and mortality globally. The FabI protein plays a crucial role in fatty acid biosynthesis and has been identified as an important target for in-silico, in-vitro, and in-vivo drug discovery. In this study we have used computer integrated-drug discovery approaches and binding-free energy calculations to identify three novel inhibitors (21272541, 67724550, and 67724551) of the FabI protein. All inhibitors showed strong affinity including van der Waals energy, electrostatic energy, polar and non-polar energies; however, the 21272541 compound was the most effective inhibitor and bound with the strongest affinity (ΔG_bind −59.02 kcal/mol) to the FabI protein. Nevertheless, all three inhibitors are promising targets for new novel antimicrobial drugs that could contribute to the management of antimicrobial resistant KPN infections based on various computational analysis. Additional in-vitro and in-vivo clinical studies will be needed to confirm drug effectiveness for the treatment of KPN infections.

Communicated by Ramaswamy H. Sarma

Keywords:

• Antimicrobial resistance
• FabI
• Klebsiella pneumoniae
• novel drug discovery
• bioinformatics

Author contributions

SK conceptualised and drafted the main manuscript, prepared figures, and generated all data required to conduct the research with input from CO. CO and SAM supervised the project. SK wrote the first draft of the paper and all authors contributed to subsequent drafts, read, and approved the final version of the manuscript.

Data availability

The data used/generated to support the findings of this study are available from the corresponding author on reasonable request.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This project was supported by University of the Witwatersrand and the National Research Foundation (NRF) through the South African Research Chairs Initiative of the Department of Science and Technology for a postdoctoral research fellowship. The authors would like to acknowledge the Centre for High Performance Computing (CHPC) server for providing supercomputing facilities based in Cape Town, South Africa to generate and analyse the data.