Frontiers in microbiology · 2026
A metal-aware library-expansion and virtual-screening workflow for Pseudomonas aeruginosa ATCC 15692 (PAO1) MraY identifies compound 5311309 with stable binding and mid-micromolar activity.
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Shen Tao, Zhang Lichen, Chen Rui, Jia Shengnan, Zhengfu He
Аннотация
INTRODUCTION: Pseudomonas aeruginosa lung infections are difficult to eradicate because of biofilms and rising resistance. MraY (MRAY_PSEAE), an essential membrane enzyme in peptidoglycan biosynthesis, depends on divalent metals (notably Mg2+); therefore, metal coordination is critical for reliable structure-based prioritization.
METHODS: We built an explicit protein-ligand-metal model of MRAY_PSEAE and extracted pocket constraints from point-cloud geometry and polar/charged features. Candidates were generated through similarity-guided library expansion, combining PubChem 2D similarity retrieval with local rule-based BRICS enumeration. After canonicalization and 2D/property filtering, compounds were clustered by fingerprint similarity and prioritized through chemical-state plausibility, docking, Prime/MM-GBSA rescoring, and ligand strain filtering, followed by interaction analysis, 1-μs MD, and in silico ADMET.
RESULTS: Three hits (6675, 2733768, 5311309) were prioritized. In microsecond MD, compound 5311309 showed the most stable pose and key hotspot retention. ADMET indicated a permeability/efflux bottleneck along with liver-kidney and irritation/sensitization alerts, whereas hERG and AMES signals were favorable. In growth assays, compound 5311309 showed an IC50 of approximately 15.1 μM and an MIC of 64 μM.
CONCLUSION: Metal-aware pocket modeling plus multi-level screening improved plausibility and ranking for this metal-dependent target, identifying compound 5311309 as a practical lead optimization starting point.
METHODS: We built an explicit protein-ligand-metal model of MRAY_PSEAE and extracted pocket constraints from point-cloud geometry and polar/charged features. Candidates were generated through similarity-guided library expansion, combining PubChem 2D similarity retrieval with local rule-based BRICS enumeration. After canonicalization and 2D/property filtering, compounds were clustered by fingerprint similarity and prioritized through chemical-state plausibility, docking, Prime/MM-GBSA rescoring, and ligand strain filtering, followed by interaction analysis, 1-μs MD, and in silico ADMET.
RESULTS: Three hits (6675, 2733768, 5311309) were prioritized. In microsecond MD, compound 5311309 showed the most stable pose and key hotspot retention. ADMET indicated a permeability/efflux bottleneck along with liver-kidney and irritation/sensitization alerts, whereas hERG and AMES signals were favorable. In growth assays, compound 5311309 showed an IC50 of approximately 15.1 μM and an MIC of 64 μM.
CONCLUSION: Metal-aware pocket modeling plus multi-level screening improved plausibility and ranking for this metal-dependent target, identifying compound 5311309 as a practical lead optimization starting point.