Rational design of dual-atom nanozymes for deep infection healing guided by an interpretable intrinsic descriptor

Abstract

Deep infections (DIs) continue to pose substantial threats to global public health and represent a critical challenge requiring a new generation of antibacterial agents. Oxidase (OXD)-like nanozymes, which directly activate oxygen, offer a promising approach for treating DIs. However, the vast design space has limited progress in discovering efficient OXD-like nanozymes. In this study, we developed a simple, interpretable intrinsic descriptor φ_ABC = minθ^0.2_d × minχ^0.3 × (θ_d·1 × θ_d·2)^0.1 by integrating density functional theory calculations, machine learning, and experimental validation. The descriptor φ_ABC offered deep physical insights by incorporating atomic properties (A), bimetallic synergistic effects (B), and coordination environments (C). Guided by this descriptor, we discovered FeZn dual-atom nanozymes (DAzymes) exhibiting a k_cat value of 0.59 s⁻¹ and a k_cat/K_m value of 1.43 × 10¹⁰ mM⁻¹ s⁻¹ for OXD-like reactions. This performance surpassed that of previously reported state-of-the-art nanozymes by three orders of magnitude. With the aid of near-infrared (NIR) irradiation, FeZn DAzyme achieved a methicillin-resistant Staphylococcus aureus (MRSA) clearance rate of 99.99% and exhibited a superior healing effect compared with vancomycin, effectively overcoming bacterial resistance, eliminating bacterial biofilms, and promoting DI recovery. Our work integrates descriptor-guided design with biological applications, which ultimately provides a new paradigm for screening nanozymes and elucidating structure–mechanism–activity–therapeutic relationships.