A higher flexibility at the SARS-CoV-2 main protease active site compared to SARS-CoV and its potentialities for new inhibitor virtual screening targeting multi-conformers.

The main-protease (Mpro) catalyzes a crucial step for the SARS-CoV-2 life cycle. The recent SARS-CoV-2 presents the main protease (MCoV2pro) with 12 mutations compared to SARS-CoV (MCoV1pro). 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 MCoV2pro, as well as its implications for ligand accessibility and optimized drug screening. We find a significantly higher accessibility to open binding conformers in the MCoV2pro monomer compared to MCoV1pro. A higher hydration propensity for the MCoV2pro S2 loop with the A46S substitution seems to exercise a key role. Quantum calculations suggest that the wider conformations for MCoV2pro 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 MCoV2pro 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.

Thermochemical and Quantum Descriptor Calculations for Gaining Insight into Ricin Toxin A (RTA) Inhibitors.

In this work, we performed a study to assess the interactions between the ricin toxin A (RTA) subunit of ricin and some of its inhibitors using modern semiempirical quantum chemistry and ONIOM quantum mechanics/molecular mechanics (QM/MM) methods. Two approaches were followed (calculation of binding enthalpies, ΔH bind, and reactivity quantum chemical descriptors) and compared with the respective half-maximal inhibitory concentration (IC50) experimental data, to gain insight into RTA inhibitors and verify which quantum chemical method would better describe RTA-ligand interactions. The geometries for all RTA-ligand complexes were obtained after running classical molecular dynamics simulations in aqueous media. We found that single-point energy calculations of ΔH bind with the PM6-DH+, PM6-D3H4, and PM7 semiempirical methods and ONIOM QM/MM presented a good correlation with the IC50 data. We also observed, however, that the correlation decreased significantly when we calculated ΔH bind after full-atom geometry optimization with all semiempirical methods. Based on the results from reactivity descriptors calculations for the cases studied, we noted that both types of interactions, molecular overlap and electrostatic interactions, play significant roles in the overall affinity of these ligands for the RTA binding pocket.

PRIMoRDiA: A Software to Explore Reactivity and Electronic Structure in Large Biomolecules.

Plenty of enzymes with structural data do not have their mechanism of catalysis elucidated. Reactivity descriptors, theoretical quantities generated from resolved electronic structure, provide a way to predict and rationalize chemical processes of such systems. In this Application Note, we present PRIMoRDiA (PRIMoRDiA Macromolecular Reactivity Descriptors Access), a software built to calculate the reactivity descriptors of large biosystems by employing an efficient and accurate treatment of the large output files produced by quantum chemistry packages. Here, we show the general implementation details and the software main features. Calculated descriptors were applied for a set of enzymatic systems in order to show their relevance for biological studies and the software potential for use in large scale. Also, we test PRIMoRDiA to aid in the interaction depiction between the SARS-CoV-2 main protease and a potential inhibitor.

Theoretical characterization of the shikimate 5-dehydrogenase reaction from Mycobacterium tuberculosis by hybrid QC/MM simulations and quantum chemical descriptors.

In this study, we have investigated the enzyme shikimate 5-dehydrogenase from the causative agent of tuberculosis, Mycobacterium tuberculosis. We have employed a mixture of computational techniques, including molecular dynamics, hybrid quantum chemical/molecular mechanical potentials, relaxed surface scans, quantum chemical descriptors and free-energy simulations, to elucidate the enzyme's reaction pathway. Overall, we find a two-step mechanism, with a single transition state, that proceeds by an energetically uphill hydride transfer, followed by an energetically downhill proton transfer. Our mechanism and calculated free energy barrier for the reaction, 64.9 kJ mol- 1, are in good agreement with those predicted from experiment. An analysis of quantum chemical descriptors along the reaction pathway indicated a possibly important, yet currently unreported, role of the active site threonine residue, Thr65.

Elucidating Enzymatic Catalysis Using Fast Quantum Chemical Descriptors.

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.

Semiempirical methods do Fukui functions: Unlocking a modeling framework for biosystems.

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.