article · mSystems
Yassir BoulaamaneTo address multidrug-resistant Acinetobacter baumannii infections, machine learning and structure-based virtual screening were used to identify new antimicrobial candidates targeting bacterial outer membrane protein W. A computational library of 11,648 natural compounds was screened using quantitative structure-activity relationship models alongside pharmacokinetic evaluations. Ten top-ranking candidates were identified, most of which were curcuminoids. Among these, demethoxycurcumin demonstrated stable binding and favourable pharmacokinetic properties. Laboratory testing confirmed that demethoxycurcumin exerts antibacterial activity against all tested Acinetobacter baumannii strains, functioning both independently and in combination with colistin. Target engagement was confirmed using an outer membrane protein W-deficient mutant, and the compound also exhibited anti-virulence characteristics by significantly reducing bacterial interactions with host cells.
Multidrug-resistant Acinetobacter baumannii poses a critical global public health threat, causing substantial hospital-acquired disease and high mortality rates. Traditional antibiotics increasingly fail against these infections. Utilising artificial intelligence to discover compounds that target outer membrane proteins provides a path towards novel antibacterial and anti-virulence therapies, potentially restoring treatment options against highly resistant pathogens.
The findings identify demethoxycurcumin as a potential candidate for pharmaceutical development targeting drug-resistant bacterial infections, potentially serving as a standalone therapy or an adjuvant with colistin. The primary beneficiaries would be biotechnology and pharmaceutical companies working on antimicrobial pipelines. Because the findings are based on computational screening and in vitro laboratory validation, the asset is at an early stage of drug discovery, requiring extensive preclinical testing and animal safety studies before commercial application.
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ABSTRACT Global challenges presented by multidrug-resistant Acinetobacter baumannii infections have stimulated the development of new treatment strategies. We reported that outer membrane protein W (OmpW) is a potential therapeutic target in A. baumannii . Here, a library of 11,648 natural compounds was subjected to a primary screening using quantitative structure-activity relationship (QSAR) models generated from a ChEMBL data set with >7,000 compounds with their reported minimal inhibitory concentration (MIC) values against A. baumannii followed by a structure-based virtual screening against OmpW. In silico pharmacokinetic evaluation was conducted to assess the drug-likeness of these compounds. The ten highest-ranking compounds were found to bind with an energy score ranging from −7.8 to −7.0 kcal/mol where most of them belonged to curcuminoids. To validate these findings, one lead compound exhibiting promising binding stability as well as favorable pharmacokinetics properties, namely demethoxycurcumin, was tested against a panel of A. baumannii strains to determine its antibacterial activity using microdilution and time-kill curve assays. To validate whether the compound binds to the selected target, an OmpW-deficient mutant was studied and compared with the wild type. Our results demonstrate that demethoxycurcumin in monotherapy and in combination with colistin is active against all A. baumannii strains. Finally, the compound was found to significantly reduce the A. baumannii interaction with host cells, suggesting its anti-virulence properties. Collectively, this study demonstrates machine learning as a promising strategy for the discovery of curcuminoids as antimicrobial agents for combating A. baumannii infections. IMPORTANCE Acinetobacter baumannii presents a severe global health threat, with alarming levels of antimicrobial resistance rates resulting in significant morbidity and mortality in the USA, ranging from 26% to 68%, as reported by the Centers for Disease Control and Prevention (CDC). To address this threat, novel strategies beyond traditional antibiotics are imperative. Computational approaches, such as QSAR models leverage molecular structures to predict biological effects, expediting drug discovery. We identified OmpW as a potential therapeutic target in A. baumannii and screened 11,648 natural compounds. We employed QSAR models from a ChEMBL bioactivity data set and conducted structure-based virtual screening against OmpW. Demethoxycurcumin, a lead compound, exhibited promising antibacterial activity against A. baumannii , including multidrug-resistant strains. Additionally, demethoxycurcumin demonstrated anti-virulence properties by reducing A. baumannii interaction with host cells. The findings highlight the potential of artificial intelligence in discovering curcuminoids as effective antimicrobial agents against A. baumannii infections, offering a promising strategy to address antibiotic resistance.
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DOI: 10.1128/msystems.00325-24
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