In Silico Enzymolysis-Guided Mining of Aminopeptidases with Molecular Insights into Their Substrate Specificity Mechanism

Abstract

The palatability of protein-based food heavily relies on aminopeptidases with the ability for bitter peptides degradation. However, there is a lack of methods for rational mining of aminopeptidases toward different proteins as well as the evolution direction for substrate specificity remaining unclear. In this study, an in silico simulated enzymolysis-based method for aminopeptidases mining was developed with Crassostrea gigas protein as a model. Results indicated that Ala and Ile were the most frequently exposed hydrophobic amino acids, causing a bitter taste in C. gigas hydrolysates. Furthermore, an aminopeptidase APs1 with putative Ala specificity was heterologously expressed and characterized with high enzyme activity toward Ala (4.92 ± 0.136 U/mg) and Arg (3.50 ± 0.178 U/mg). Site-saturation mutation and molecular docking results revealed that changes in steric hindrance and salt bridge formation within the active site contribute to enhanced catalytic efficiency. Among the mutants, APs1(F316M) showed significantly improved activity toward tested hydrophobic amino acids, especially the activity toward Ala was increased to 14.50 ± 0.137 U/mg. This study presents a directional approach to aminopeptidase mining and evolution, contributing to the rapid selection and combination of protein-degrading enzymes for food quality improvement.