In general, computational simulations of enzymatic catalysis processes are thermodynamic and structural surveys to complement experimental studies, requiring high level computational methods to match accurate energy values. In the present work, we propose the usage of reactivity descriptors, theoretical quantities calculated from the electronic structure, to characterize enzymatic catalysis outlining its reaction profile using low-level computational methods, such as semiempirical Hamiltonians. We simulate three enzymatic reactions paths, one containing two reaction coordinates and without prior computational study performed, and calculate the reactivity descriptors for all obtained structures. We observed that the active site local hardness does not change substantially, even more so for the amino-acid residues that are said to stabilize the reaction structures. This corroborates with the theory that activation energy lowering is caused by the electrostatic environment of the active sites. Also, for the quantities describing the atom electrophilicity and nucleophilicity, we observed abrupt changes along the reaction coordinates, which also shows the enzyme participation as a reactant in the catalyzed reaction. We expect that such electronic structure analysis allows the expedient proposition and/or prediction of new mechanisms, providing chemical characterization of the enzyme active sites, thus hastening the process of transforming the resolved protein three-dimensional structures in catalytic information.

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The main-protease (M pro ) catalyzes a crucial step for the SARS-CoV-2 life cycle. The recent SARS-CoV-2 presents the main protease (M CoV2 pro ) with 12 mutations compared to SARS-CoV (M CoV1 pro ). Recent studies point out that these subtle differences lead to mobility variances at the active site loops with functional implications. We use metadynamics simulations and a sort of computational analysis to probe the dynamic, pharmacophoric and catalytic environment differences between the monomers of both enzymes. So, we verify how much intrinsic distinctions are preserved in the functional dimer of M CoV2 pro , as well as its implications for ligand accessibility and optimized drug screening. We find a significantly higher accessibility to open binding conformers in the M CoV2 pro monomer compared to M CoV1 pro . A higher hydration propensity for the M CoV2 pro S2 loop with the A46S substitution seems to exercise a key role. Quantum calculations suggest that the wider conformations for M CoV2 pro are less catalytically active in the monomer. However, the statistics for contacts involving the N-finger suggest higher maintenance of this activity at the dimer. Docking analyses suggest that the ability to vary the active site width can be important to improve the access of the ligand to the active site in different ways. So, we carry out a multiconformational virtual screening with different ligand bases. The results point to the importance of taking into account the protein conformational multiplicity for new promissors anti M CoV2 pro ligands. We hope these results will be useful in prospecting, repurposing and/or designing new anti SARS-CoV-2 drugs. Communicated by Ramaswamy H. Sarma

Obtaining reactivity information from the molecular electronic structure of a chemical system is a computationally intensive process. As a way of probing reactivity information around that, there exist electron density response variables, such as the Fukui functions (FFs), which are well‐established descriptors that summarize the local susceptibility to react. These properties only require few single‐point quantum chemical calculations, but even then, the intrinsic high cost and unfavorable computational complexity with respect to the number of atoms in the system makes this approach available only to small fragments and systems. In this study, we explore the computation of FFs, showing that semiempirical quantum chemical methods can be used to obtain the reactivity information equivalent to that of a Density Functional Theory (DFT) functional, for the eight entire polypeptide chains. The combination of semiempirical methods with the frozen orbital approximation allows for the obtention of these reactivity descriptors for biological systems with reasonable accuracy and speed, unlocking the utilization of these methods for such systems. These results for the frozen orbital approximation can be additionally improved when other molecular orbitals from the frontier band are employed in the computation. We also show the potential of this computational protocol in the ligand–protein complexes of HIV‐1 protease, predicting which of those ligands are active inhibitors.

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