生物膜
铜绿假单胞菌
虚拟筛选
微生物学
化学
生物
细菌
遗传学
生物化学
药物发现
作者
Christabel Ming Ming Koh,Siaw San Hwang,Lau Bee Theng,Enzo A. Palombo,Irine Runnie Henry Ginjom,Christopher Heng Xuan Ha,Taufiq Rahman,Xavier Chee Wezen
出处
期刊:ACS Infectious Diseases
[American Chemical Society]
日期:2024-10-18
标识
DOI:10.1021/acsinfecdis.4c00549
摘要
Pseudomonas aeruginosa is the predominant bacterium found in many chronic biofilm infections. Over the past few decades, biofilm-related infections have posed a significant challenge to medical practice due to the increasing emergence of multidrug resistance. Cis-2-decenoic acid (CDA), a small molecule found in P. aeruginosa, has been shown to disperse biofilms formed by various bacteria and to work in synergy with common antibiotics. Despite that, the binding mechanism between CDA and the predicted cyclases/histidine kinases associated sensory extracellular (CHASE) domain of sensor protein DspS remains unknown in the absence of a crystallized protein structure. Moreover, the therapeutic potential of CDA is limited by its susceptibility to oxidative degradation and isomerization. In this work, we propose a structural model for the DspS CHASE domain. The resulting model displays an overall topology reminiscent of the sensor protein PcrK in Xanthomonas campestris. Through molecular dynamics simulations, a stable potential binding site for CDA was further identified. Virtual screening against the predicted site of DspS CHASE using our developed pipeline discovered two promising compounds, compounds 2 and 9, capable of dislodging 7-day P. aeruginosa biofilms at 50 μM without affecting bacterial growth. These compounds also enhanced the effects of ciprofloxacin against P. aeruginosa, reduced the survival of dispersed cells, and increased the expression of matrix-degrading enzyme genes pelA, pslG, and eddA. This study provides insights into CDA recognition by DspS and represents the first large-scale effort to uncover first-in-class DspS activators. At the same time, this work also underscores the effectiveness of a computational-aided drug discovery process in finding new activators, even without a known protein structure.
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