COVID-19, caused by severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2), represents a global crisis. Key to SARS-CoV-2 therapeutic development is unraveling the mechanisms driving high infectivity, broad tissue tropism and severe pathology. Our 2.85 Å cryo-EM structure of SARS-CoV-2 spike (S) glycoprotein reveals that the receptor binding domains (RBDs) tightly bind the essential free fatty acid (FFA) linoleic acid (LA) in three composite binding pockets. The pocket also appears to be present in the highly pathogenic coronaviruses SARS-CoV and MERS-CoV. LA binding stabilizes a locked S conformation giving rise to reduced ACE2 interaction in vitro. In human cells, LA supplementation synergizes with the COVID-19 drug remdesivir, suppressing SARS-CoV-2 replication. Our structure directly links LA and S, setting the stage for intervention strategies targeting LA binding by SARS-CoV-2.

We present an atomic-level description of the reaction chemistry of an enzyme-catalyzed reaction dominated by proton tunneling. By solving structures of reaction intermediates at near-atomic resolution, we have identified the reaction pathway for tryptamine oxidation by aromatic amine dehydrogenase. Combining experiment and computer simulation, we show proton transfer occurs predominantly to oxygen O2 of Asp(128)beta in a reaction dominated by tunneling over approximately 0.6 angstroms. The role of long-range coupled motions in promoting tunneling is controversial. We show that, in this enzyme system, tunneling is promoted by a short-range motion modulating proton-acceptor distance and no long-range coupled motion is required.

Computational enzymology is a rapidly maturing field that is increasingly integral to understanding mechanisms of enzyme-catalyzed reactions and their practical applications. Combined quantum mechanics/molecular mechanics (QM/MM) methods are important in this field. By treating the reacting species with a quantum mechanical method (i.e., a method that calculates the electronic structure of the active site) and including the enzyme environment with simpler molecular mechanical methods, enzyme reactions can be modeled. Here, we review QM/MM methods and their application to enzyme-catalyzed reactions to investigate fundamental and practical problems in enzymology. A range of QM/MM methods is available, from cheaper and more approximate methods, which can be used for molecular dynamics simulations, to highly accurate electronic structure methods. We discuss how modeling of reactions using such methods can provide detailed insight into enzyme mechanisms and illustrate this by reviewing some recent applications. We outline some practical considerations for such simulations. Further, we highlight applications that show how QM/MM methods can contribute to the practical development and application of enzymology, e.g., in the interpretation and prediction of the effects of mutagenesis and in drug and catalyst design.

The accurate prediction of enzyme kinetics from first principles is one of the central goals of theoretical biochemistry. Currently there is considerable debate [1][2][3] about the applicability of transition state theory (TST) to compute rate constants of enzyme-catalyzed reactions. Classical TST is known to be insufficient in some cases, but corrections for dynamical recrossing and quantum mechanical tunneling can be included. [1,2,4] Many effects that go beyond the framework of TST have been proposed, particularly focusing on the possible role of protein dynamics and conformational effects on the enzyme activity. Unfortunately, the overall importance of these effects for the effective reaction rate is difficult (if not impossible) to determine experimentally. However, if one could compute the quasi-thermodynamical free energy of activation with chemical accuracy (i.e. 1 kcal mol À1 ), comparison with the effective measured free energy of activation would directly show the importance of other effects.Combined quantum mechanical/molecular mechanical (QM/MM) methods have become an important tool for computational modeling of enzyme-catalyzed reactions. Only the substrate(s) and relevant residues in the active site are treated quantum mechanically, the rest of the protein is described at the empirical MM level. This lowers the computational expense, making it possible to treat large enzyme systems and the surrounding solvent, and to sample phase space. Nevertheless, the number of QM atoms is still relatively large, and until now only low levels of QM theory, such as semiempirical methods or density functional theory (DFT), have been feasible. Semiempirical methods, though applicable to large systems, are generally not accurate enough because computed free energies of activation may have an error of ten or more kcal mol À1 . DFT (especially with the B3LYP functional [5] ) offers improved accuracy but still lacks key physical interactions (e.g., dispersion). Often, DFT underestimates barrier heights by several kcal mol À1 , which cannot be systematically improved. Thus, when theoretical barriers do not agree with those from experiment, it is not clear whether the discrepancy arises from deficiencies in the electronic structure theory and the sampling, in the experimental observations, or in the underlying theoretical framework of QM/MM and TST.Consequently, there is a need for high-level electronic structure calculations for reliable predictions of enzyme reactivity. The ab initio electron correlation methods MP2 (Møller-Plesset second-order perturbation theory), CCSD (coupled-cluster theory with single and double excitations), and CCSD(T) (CCSD with a perturbative treatment of triple excitations) provide a well-established hierarchy that converge reliably to give high accuracy, and rate constants of gasphase reactions involving only a few atoms can be predicted with error bars comparable to those found experimentally. [6,7] However, the computational expense of these methods increases very rapidly with the number o...

Protein dynamics have controversially been proposed to be at the heart of enzyme catalysis, but identification and analysis of dynamical effects in enzyme-catalyzed reactions have proved very challenging. Here, we tackle this question by comparing an enzyme with its heavy ( 15 N, 13 C, 2 H substituted) counterpart, providing a subtle probe of dynamics. The crucial hydride transfer step of the reaction (the chemical step) occurs more slowly in the heavy enzyme. A combination of experimental results, quantum mechanics/molecular mechanics simulations, and theoretical analyses identify the origins of the observed differences in reactivity. The generally slightly slower reaction in the heavy enzyme reflects differences in environmental coupling to the hydride transfer step. Importantly, the barrier and contribution of quantum tunneling are not affected, indicating no significant role for "promoting motions" in driving tunneling or modulating the barrier. The chemical step is slower in the heavy enzyme because protein motions coupled to the reaction coordinate are slower. The fact that the heavy enzyme is only slightly less active than its light counterpart shows that protein dynamics have a small, but measurable, effect on the chemical reaction rate.kinetics | computational chemistry | biological chemistry | biophysics | quantum biology

One of the critical variables that determine the rate of any reaction is temperature. For biological systems, the effects of temperature are convoluted with myriad (and often opposing) contributions from enzyme catalysis, protein stability, and temperature-dependent regulation, for example. We have coined the phrase "macromolecular rate theory (MMRT)" to describe the temperature dependence of enzyme-catalyzed rates independent of stability or regulatory processes. Central to MMRT is the observation that enzyme-catalyzed reactions occur with significant values of ΔCp(‡) that are in general negative. That is, the heat capacity (Cp) for the enzyme-substrate complex is generally larger than the Cp for the enzyme-transition state complex. Consistent with a classical description of enzyme catalysis, a negative value for ΔCp(‡) is the result of the enzyme binding relatively weakly to the substrate and very tightly to the transition state. This observation of negative ΔCp(‡) has important implications for the temperature dependence of enzyme-catalyzed rates. Here, we lay out the fundamentals of MMRT. We present a number of hypotheses that arise directly from MMRT including a theoretical justification for the large size of enzymes and the basis for their optimum temperatures. We rationalize the behavior of psychrophilic enzymes and describe a "psychrophilic trap" which places limits on the evolution of enzymes in low temperature environments. One of the defining characteristics of biology is catalysis of chemical reactions by enzymes, and enzymes drive much of metabolism. Therefore, we also expect to see characteristics of MMRT at the level of cells, whole organisms, and even ecosystems.

The role of protein dynamics in enzyme catalysis is a matter of intense current debate. Enzyme-catalysed reactions that involve significant quantum tunnelling can give rise to experimental kinetic isotope effects with complex temperature dependences, and it has been suggested that standard statistical rate theories, such as transition-state theory, are inadequate for their explanation. Here we introduce aspects of transition-state theory relevant to the study of enzyme reactivity, taking cues from chemical kinetics and dynamics studies of small molecules in the gas phase and in solution--where breakdowns of statistical theories have received significant attention and their origins are relatively better understood. We discuss recent theoretical approaches to understanding enzyme activity and then show how experimental observations for a number of enzymes may be reproduced using a transition-state-theory framework with physically reasonable parameters. Essential to this simple model is the inclusion of multiple conformations with different reactivity.

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