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Published on: February 23, 2021
Machine learning-guided discovery of covalent sortase A inhibitors targeting MRSA virulence
Xu-Liang Xu1, Ti-Ti Ying1, Xiao-Wen Wu1
1College of Pharmaceutical Science & Collaborative Innovation Center of Yangtze River Delta Region Green Pharmaceuticals, Zhejiang-Egypt Joint Laboratory on Intelligent Discovery of Marine Drugs, Zhejiang University of Technology, Hangzhou, 310014, China.
Abstract:
The global rise of methicillin-resistant Staphylococcus aureus (MRSA) has highlighted the urgent need for alternative therapeutic strategies beyond conventional bactericidal antibiotics. Targeting bacterial virulence rather than viability represents a promising approach to mitigate selective pressure and delay resistance development. Sortase A (SrtA), a membrane-associated transpeptidase responsible for anchoring virulence-associated surface proteins, is an attractive anti-virulence target due to its non-essential role in bacterial survival. Here, we report a machine learning-guided strategy for the discovery of novel covalent SrtA inhibitors based on a 1,2-benzoselenazol-3-one (BSEA) scaffold featuring a tunable electrophilic Se-N bond. A scaffold-aware classification model with a Tanimoto similarity constraint trained on 529 SrtA inhibitors enabled prospective virtual screening of over 35,000 BSEA and BTA derivatives, leading to a high hit rate of 89% upon experimental validation. Representative compounds exhibited submicromolar SrtA inhibition (IC50 = 0.84-1.04 μM) while showing minimal effects on bacterial growth (MIC = 8-32 μM), indicating effective functional decoupling of virulence and viability. Mechanistic studies demonstrated time-dependent irreversible inhibition kinetics, supported by jump dilution assays and Nano-LC-MS/MS identification of covalent modification at the catalytic residue Cys184. These inhibitors effectively disrupted MRSA biofilm formation at sub-inhibitory concentrations and significantly improved host survival in a Galleria mellonella infection model. Collectively, this study establishes a data-driven framework integrating machine learning and covalent chemistry for anti-virulence drug discovery and provides promising lead compounds targeting SrtA to combat MRSA infections.
Insights
Researchers developed novel anti-virulence drugs targeting Sortase A (SrtA) in methicillin-resistant Staphylococcus aureus (MRSA). Machine learning guided the discovery of covalent inhibitors that reduce MRSA virulence without harming bacteria, improving infection outcomes.
Area of Science:
- Microbiology
- Medicinal Chemistry
- Computational Biology
Background:
- Methicillin-resistant Staphylococcus aureus (MRSA) poses a significant global health threat, necessitating novel therapeutic strategies beyond traditional antibiotics.
- Targeting bacterial virulence factors, such as Sortase A (SrtA), offers a promising approach to combat antibiotic resistance by disarming pathogens without promoting resistance.
- Sortase A (SrtA) is a key transpeptidase for anchoring surface virulence proteins in Staphylococcus aureus and is a viable anti-virulence target due to its non-essential role in bacterial survival.
Purpose of the Study:
- To discover novel covalent inhibitors of Sortase A (SrtA) using a machine learning-guided approach.
- To identify compounds that selectively inhibit MRSA virulence without affecting bacterial viability, thereby mitigating resistance development.
- To validate the efficacy of identified SrtA inhibitors in vitro and in vivo models of MRSA infection.
Main Methods:
- A scaffold-aware machine learning model was developed and trained on existing SrtA inhibitors.
- Prospective virtual screening of over 35,000 compounds based on a 1,2-benzoselenazol-3-one (BSEA) scaffold was performed.
- Experimental validation, biochemical assays, mechanistic studies (including Nano-LC-MS/MS), and in vivo infection models (Galleria mellonella) were employed.
Main Results:
- The machine learning strategy achieved an 89% hit rate in experimental validation.
- Identified covalent SrtA inhibitors demonstrated submicromolar potency (IC50 = 0.84–1.04 μM) with minimal impact on bacterial growth (MIC = 8–32 μM).
- Inhibitors effectively disrupted MRSA biofilm formation and significantly improved host survival in a Galleria mellonella infection model, confirming functional decoupling of virulence and viability.
Conclusions:
- A data-driven framework integrating machine learning and covalent chemistry was established for anti-virulence drug discovery.
- Novel covalent SrtA inhibitors were identified as promising lead compounds for combating MRSA infections.
- This approach offers a viable strategy to develop new therapeutics against antibiotic-resistant bacteria by targeting virulence factors.
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