Perspectives on Computational Enzyme Modeling: From Mechanisms to Design and Drug Development

Abstract

Understanding enzyme mechanisms is essential for unraveling the complex molecular machinery of life. In this review, we survey the field of computational enzymology, highlighting key principles governing enzyme mechanisms and discussing ongoing challenges and promising advances. Over the years, computer simulations have become indispensable in the study of enzyme mechanisms, with the integration of experimental and computational exploration now established as a holistic approach to gain deep insights into enzymatic catalysis. Numerous studies have demonstrated the power of computer simulations in characterizing reaction pathways, transition states, substrate selectivity, product distribution, and dynamic conformational changes for various enzymes. Nevertheless, significant challenges remain in investigating the mechanisms of complex multistep reactions, large-scale conformational changes, and allosteric regulation. Beyond mechanistic studies, computational enzyme modeling has emerged as an essential tool for computer-aided enzyme design and the rational discovery of covalent drugs for targeted therapies. Overall, enzyme design/engineering and covalent drug development can greatly benefit from our understanding of the detailed mechanisms of enzymes, such as protein dynamics, entropy contributions, and allostery, as revealed by computational studies. Such a convergence of different research approaches is expected to continue, creating synergies in enzyme research. This review, by outlining the ever-expanding field of enzyme research, aims to provide guidance for future research directions and facilitate new developments in this important and evolving field.

Figure 1

Topics in computational enzymology covered in this review and representative computational approaches. The synergistic connection with experiments is also indicated. Abbreviations: MD: molecular dynamics; QM/MM: quantum mechanical and molecular mechanical method; FE: free energy.

Figure 2

(A) Free energy landscapes visualizing the hierarchy of protein motions on different time scales. State A represents the reactant state in which multiple substates exist in equilibrium. The rate constants for exchange between substates occur on time scales ranging from picoseconds (ps) to nanoseconds (ns). The catalytic reaction takes place on microsecond (μs) ∼ millisecond (ms) time scales, forming products represented as state B. (B) Enzyme catalysis from the perspective of conformational substates, where the free energy landscape is plotted as a function of the catalytic reaction coordinate (ξ) and conformational substate (z). In each substate, the catalytic barrier and reaction free energy can be calculated by applying the QM/MM free energy simulation methods. Then, the overall reaction rate can be expressed as the sum of the population-weighted rates (eq 3).

Figure 3

Schematic representation of the free energy changes and thus, the allosteric mechanisms of (A) the IGF-1RK conformational change that is shifted toward the activated state following phosphorylation, (B) increased substrate binding affinity following phosphorylation, and (C) increased phosphoryl transfer reaction partly caused by changes in underlying protein dynamics after phosphorylation. In each diagram, the changes of the free energy are indicated with the arrow, and the red and blue lines represent the free energy profiles of fully activated and inactive form kinases, respectively. Adapted with permission from ref (243). Copyright 2017 Royal Society of Chemistry.

Figure 4

(A) Proposed two-step binding of a covalent ligand. The first step is noncovalent binding with barrier ΔG_b^⧧, and the second step is covalent bond formation with ΔG_c^⧧ as the associated barrier. The effectiveness of the covalent binders can be determined by the free energy barrier (ΔG_c^⧧) of the second step relative to the noncovalent binding free energy (ΔG_nc) and/or the unbinding free energy barrier (ΔG_ub^⧧). Too high a barrier can result in premature release of the binder prior to covalent bond formation, while too low a barrier renders the binder reversible or ineffective as a covalent inhibitor. In the latter case, the warhead may be too reactive, leading to nonspecific binding. Therefore, modulation of both ΔG_nc and ΔG_c^⧧ is critical for the design of effective covalent binders, where the binding pocket of the target protein/enzyme provides an environment for nonspecific binding and functional modifications of the warhead control the reactivity of the binders. (B) SARS-CoV-2 Mpro with bound ligand, N3 inhibitor (PDB ID: 7BQY). The bound ligand is colored yellow, and water molecules are pink. The protein surface appears in gray. The Cys 145-His41 dyad is shown in deep teal. Adapted with permission from ref (404). Copyright 2021 American Chemical Society.