Correlating Enzymatic Reactivity for Different Substrates using Transferable Data-Driven Collective Variables

Machine learning is transforming the investigation of complex biological processes. In enzymatic catalysis, one significant challenge is identifying the reactive conformations of the enzyme:substrate complex where the substrate assumes a precise arrangement in the active site necessary to initiate a reaction. Here, we applied machine learning techniques to address this challenge, focusing on human pancreatic α-amylase, a crucial enzyme in type-II diabetes treatment. Using machine learning-based collective variables, we correlated the probability of being in a reactive conformation with the experimental catalytic activity of several malto-oligosaccharide substrates. Our findings demonstrate a remarkable transferability of these collective variables across various compounds, significantly streamlining the modeling process and reducing both computational demand and manual intervention in setting up simulations for new substrates. This approach not only advances our understanding of enzymatic processes but also holds substantial potential for accelerating drug discovery by enabling rapid and accurate evaluation of drug efficacy across different generations of inhibitors.