Issue 34, 2019

Computational elucidation of the binding mechanisms of curcumin analogues as bacterial RecA inhibitors

Abstract

Antimicrobial resistance (AMR) presents as a serious threat to global public health, which urgently demands action to develop alternative antimicrobial strategies with minimized selective pressure. The bacterial SOS response regulator RecA has emerged as a promising target in the exploration of new classes of antibiotic adjuvants, as RecA has been implicated in bacterial mutagenesis and thus AMR development through its critical roles in error-prone DNA repair. The natural product curcumin has been reported to be an effective RecA inhibitor in several Gram-negative bacteria, but details on the underlying mechanisms are wanting. In order to bridge the gap in how curcumin operates as a RecA inhibitor, we used computational approaches to model interactions between RecA protein and curcumin analogues. We first identified potential binding sites on E. coli RecA protein and classified them into four major binding pockets based on biological literature and computational findings from multiple in silico calculations. In docking analysis, curcumin–thalidomide hybrids were predicted to be superior binders of RecA compared with bis-(arylmethylidene)acetone curcumin analogues, which was further confirmed by MMGBSA calculations. Overall, this work provides mechanistic insights into bacterial RecA protein as a target for curcumin-like compounds and offers a theoretical basis for rational design and development of future antibiotic adjuvants.

Graphical abstract: Computational elucidation of the binding mechanisms of curcumin analogues as bacterial RecA inhibitors

Supplementary files

Article information

Article type
Paper
Submitted
04 Jan 2019
Accepted
04 Jun 2019
First published
25 Jun 2019
This article is Open Access
Creative Commons BY-NC license

RSC Adv., 2019,9, 19869-19881

Computational elucidation of the binding mechanisms of curcumin analogues as bacterial RecA inhibitors

Z. Zhou, J. Yuan, Q. Pan, X. Mo, Y. Xie, F. Yin, Z. Li and N. Wong, RSC Adv., 2019, 9, 19869 DOI: 10.1039/C9RA00064J

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