Deciphering Features of Metalloprotease Cleavage Targets Using Protein Structure Prediction

Metalloproteases are a class of enzymes that utilize metal ions within their active sites to catalyze the hydrolysis of peptide bonds in proteins. Among these, ADAM10 (A Disintegrin and Metalloproteinase 10), a member of the ADAM family, plays a crucial role in mediating intracellular signaling by cleaving specific substrates, thereby influencing a variety of physiological and pathological processes. The mechanisms underlying the activity of ADAM10 present significant opportunities for the development of novel therapeutic strategies aimed at disease intervention. However, the information available to identify the substrate and cleavage sites of ADAM10 is still insufficient. Therefore, it is essential to identify and classify the features of substrates and to elucidate cleavage sites through experimental approaches. However, these studies across numerous proteins present significant challenges.

To address the promise of these investigations, we developed a model that leverages protein structure prediction to decipher substrate features, classify substrates, and predict cleavage sites. Through the analysis of predicted protein complexes between ADAM10 and its substrates using PDB files, we evaluated protein-protein interaction (PPI) data, the structural information of cleavage sites, and the spatial relationships between the cleavage sites and metal ions.

Finally, we present a computational model that effectively classifies substrates and accurately predicts cleavage sites in this study. Our study demonstrates the potential for application not only to ADAM10 but also to other members of the ADAM family and, more broadly, to additional metalloproteases. By leveraging computed protein structural information, our approach offers a novel perspective for substrate classification.