Matrix metalloproteinase-12 in focus: Deciphering the structural prerequisites for therapeutic intervention through S1′ pocket recognition

Title : Matrix metalloproteinase-12 in focus: Deciphering the structural prerequisites for therapeutic intervention through S1′ pocket recognition

Abstract:

Matrix metalloproteinase-12 (MMP-12) is a zinc dependent endopeptidase that has been shown to play an important role in the pathogenesis of inflammatory, pulmonary, cardiovascular, neurological and cancer associated diseases and is therefore considered to be a good therapeutic target. Selective MMP-12 inhibitors have been difficult to develop, however, because of the high similarity in structure of the various MMP family members. The present study utilized an integrated computational approach to understand the structural features that govern selectivity for the inhibition of MMP-12 and focus on the S1′ specificity pocket. To find subtle structural changes responsible for the dramatic differences in inhibitory activity, matched molecular pair (MMP) cliff analysis was first done. Following, a quantitative read across structure activity relationship (qRASAR) model was built which was then supplemented by physics aware deep learning computational binding interaction analysis and 200 ns molecular dynamics (MD) simulation to elucidate the recognition and stability of complexes. 

The developed qRASAR model demonstrated satisfactory predictive performance (R² = 0.710, Q²F1 = 0.734, Q²F2 = 0.734, MAEtest = 0.563). Binding interaction analysis revealed that potent inhibitors, represented by compounds C5 and C54, established stable coordination with the catalytic Zn²⁺ ion together with persistent interactions involving key S1′ pocket residues P238, Y240, K241, and F248. In contrast, the weak inhibitor C475 exhibited comparatively poor engagement within the S1′ pocket, resulting in reduced binding stability. Molecular dynamics simulations further demonstrated enhanced structural stability, lower conformational fluctuations, and sustained protein ligand interactions for the C5 and C54 bound complexes, highlighting the importance of effective S1′ pocket recognition in MMP-12 inhibition. 

Biography:

Asmita Sensarma is an M.E student in Biomedical Engineering at Jadavpur University, India. She previously completed an M.Sc. in Molecular Biology and Biotechnology from Tezpur University. Her research focuses on computational drug discovery, machine learning, molecular modelling, molecular docking, and molecular dynamics simulations for the identification of novel therapeutic targets.