Table 1_Interpretable machine learning classifies β-lactamase subclasses and prioritizes boundary variants for antimicrobial resistance surveillance.docx

Projection into reduced descriptor space identified transitional variants near subclass boundaries, including CTX-M/SHV convergence zones and non-β-lactamase hydrolases with partial β-lactamase-like signatures. Representative case studies indicated that some apparent misclassifications may reflect annotation inconsistencies, scaffold convergence, or early adaptive trajectories, highlighting candidates for experimental follow-up.

External validation using 271 independent proteins annotated as non-β-lactamase hydrolases demonstrated 96.95% specificity, with only limited false-positive assignments. Most incorrect classifications showed weak statistical support, indicating that confidence filtering could further improve screening performance.</p>Discussion<p>Overall, this study demonstrates that compact descriptor-based models can provide accurate and interpretable classification of β-lactamase-related proteins while identifying emerging boundary variants relevant to antimicrobial resistance surveillance.

The framework offers a scalable approach for protein annotation, monitoring resistance evolution, and supporting future inhibitor development.</p>