How and When Does an Enzyme React? Unraveling α-Amylase Catalytic Activity with Enhanced Sampling Techniques

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 within the active site, and highlight the role of water in the catalytic process and in the stepwise conversion of reactionready to non-reactive conformations. Overall, these insights offer atomistic details on the catalytic mechanism and dynamics of the active site, allowing one to shed light on two fundamental questions in enzymatic catalysis, that is how and when does an enzyme react?

Kinetic View of Enzyme Catalysis from Enhanced Sampling QM/MM Simulations

“…The active site water environment also plays a crucial role in distinguishing these two states of the Michaelis complex. 7 The combination of these effects results in a freeenergy cost of 6.5 kJ/mol for reaching RC (Table 1). The computed reaction time (τ RC→P ) is 1847 μs (95% confidence interval of 1420−3450 μs) and the estimated k cat , from eq 3, is 40.2 s −1 , which is in order-of-magnitude agreement with the experimental value of 408 ± 17 s −1 .…”

“…5,9,26−28 Our recent work showed the existence of an equilibrium between the deep free-energy minimum of the NRC and the shallower minimum corresponding to the RC. 7 This effect is implicitly included in the experimental k cat values and should be taken into account while modeling enzymatic reactivity. In this paper, we propose a general workflow to overcome these limitations by using adaptive bias enhanced sampling methods to explicitly sample the conformational space of the ES and to directly estimate the rate constant of the chemical transformation.…”

“…Reaching such a level of molecular resolution is extremely challenging from an experimental perspective. Nonetheless, it becomes more accessible through atomistic simulations, which can aid in differentiating between reactive and non-reactive configurations, predicting k cat and understanding the enzymatic mechanism. − …”

“…However, these approaches have some limitations regarding the appropriate estimation of the barrier height. Reactive QM/MM simulations are often initiated from reaction-ready configurations and not from the free-energy minimum corresponding to the most stable conformation of the ES. , Despite facilitating the sampling of the reactive events, this approach underestimates the barrier height because the starting point is already close to the transition state. Furthermore, the barrier heights obtained from the free-energy landscapes are strongly influenced by the choice of CVs.…”

Automated Exploration of Reaction Networks and Mechanisms Based on Metadynamics Nanoreactor Simulations

“…In addition, a suitable choice for the MTD setting is still not a fully trivial task. We can also apply other enhanced sampling methods − to generate reaction events. To reduce the computational cost even further, the employment of the machine-learning PES − is preferable in reactive MD.…”