Abstract
Enzymatic catalysis is a complex process that can involve multiple conformations of the enzyme:substrate complex and several competitive reaction pathways, resulting in a multi-dimensional free energy landscape. The study of enzymatic activity often requires deep knowledge of the system to establish the catalytic mechanism and identify the possible reactive conformations of the complex. Here, we present an enhanced sampling and machine learning-based approach to explore the catalytic reaction space and characterize the transformation from reactive to non-reactive conformations with minimal a priori knowledge of the system. We applied this approach to study the rate-determining step of the glycolysis reaction of maltopentose catalyzed by human pancreatic α-amylase, an important enzyme in glucose production as well as a major drug target for the treatment of type-II diabetes. We unravel the complexity of the enzymatic reaction, reveal three binding modes of the substrate in the catalytic pocket, and highlight the role of water in the catalytic process and in the stepwise conversion of reaction-ready to non-reactive conformations. Overall, these insights offer atomistic details on the catalytic mechanism and dynamics of the active site, allowing to shed light on two fundamental questions in enzymatic catalysis, that is how and when does an enzyme react?
Supplementary materials
Title
Supplementary Information
Description
Computational details (system preparation, classical and QM/MM MD simulations); Enhanced sampling simulations (reaction discovery, Deep-TDA and Path CVs, GNAC estimation and FES convergence); Supplementary figures and tables.
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