From MMP cliffs to binding interactions: An integrated Read-across and deep learning-based investigation of MMP-12 inhibitors to elucidate S1' pocket recognition.

Matrix metalloproteinase-12 (MMP-12) is a zinc-dependent endopeptidase that plays an important role in the pathogenesis of several inflammatory, pulmonary, cardiovascular, neurological, and cancer-associated disorders. Despite its therapeutic significance, the development of potent and selective MMP-12 inhibitors remains challenging because of the high structural similarity shared among various MMP family members. This study presents an integrated framework combining matched molecular pair (MMP) cliff analysis, quantitative read-across structure-activity relationship (qRASAR)- based modelling, deep learning-based analysis of binding interactions, and MD simulations to elucidate the structural determinants governing potent MMP-12 inhibition, with particular emphasis on recognition of the S1' pocket. Leveraging structural similarity with MMP-12 inhibitors, the final qRASAR MLR model showed satisfactory predictive performance (R2 = 0.710, Q2F1 = 0.734, Q2F2 = 0.734, and MAEtest = 0.563). A physics-aware, deep learning-based binding interaction analysis showed that potent inhibitors like C5 and C54 form favourable interactions with key residues in the S1' pocket, including P238, Y240, K241, and F248, whereas weak inhibitors like C475 exhibit comparatively weaker engagement within this S1' subsite. Subsequently, MD simulations further confirmed the enhanced stability, compactness, and reduced conformational flexibility of the MMP-12-C5 and MMP-12-C54 complexes relative to MMP-12-C475 and highlighted the critical role of persistent S1' pocket interactions in stabilizing the protein-ligand complexes and enhancing MMP-12 inhibitory potency. The findings emphasize the importance of effective S1' pocket recognition and provide valuable insights for the rational design of potent MMP-12 inhibitors in future.