Characterizing the ligand-binding affinity toward SARS-CoV-2 Mpro via physics- and knowledge-based approaches

MedChemExpress compounds prevent neuraminidase N1 via physics- and knowledge-based methods

Influenza A viruses spread out worldwide, causing several global concerns. Hence, discovering neuraminidase inhibitors to prevent the influenza A virus is of great interest. In this work, a machine learning model was employed to evaluate the ligand-binding affinity of ca. 10 000 compounds from the MedChemExpress (MCE) database for inhibiting neuraminidase. Atomistic simulations, including molecular docking and molecular dynamics simulations, then confirmed the ligand-binding affinity. Furthermore, we clarified the physical insights into the binding process of ligands to neuraminidase. It was found that five compounds, including micronomicin, didesmethyl cariprazine, argatroban, Kgp-IN-1, and AY 9944, are able to inhibit neuraminidase N1 of the influenza A virus. Ten residues, including Glu119, Asp151, Arg152, Trp179, Gln228, Glu277, Glu278, Arg293, Asn295, and Tyr402, may be very important in controlling the ligand-binding process to N1.

Machine learning combines atomistic simulations to predict SARS-CoV-2 Mpro inhibitors from natural compounds

To date, the COVID-19 pandemic has still been infectious around the world, continuously causing social and economic damage on a global scale. One of the most important therapeutic targets for the treatment of COVID-19 is the main protease (Mpro) of SARS-CoV-2. In this study, we combined machine-learning (ML) model with atomistic simulations to computationally search for highly promising SARS-CoV-2 Mpro inhibitors from the representative natural compounds of the National Cancer Institute (NCI) Database. First, the trained ML model was used to scan the library quickly and reliably for possible Mpro inhibitors. The ML output was then confirmed using atomistic simulations integrating molecular docking and molecular dynamic simulations with the linear interaction energy scheme. The results turned out to show that there was evidently good agreement between ML and atomistic simulations. Ten substances were proposed to be able to inhibit SARS-CoV-2 Mpro. Seven of them have high-nanomolar affinity and are very potential inhibitors. The strategy has been proven to be reliable and appropriate for fast prediction of SARS-CoV-2 Mpro inhibitors, benefiting for new emerging SARS-CoV-2 variants in the future accordingly.

Natural compounds inhibit Monkeypox virus methyltransferase VP39 in silico studies

VP39, an essential 2'-O-RNA methyltransferase enzyme discovered in Monkeypox virus (MPXV), plays a vital role in viral RNA replication and transcription. Inhibition of the enzyme may prevent viral replication. In this context, using a combination of molecular docking and molecular dynamics (MDs) simulations, the inhibitory ability of NCI Diversity Set VII natural compounds to VP39 protein was investigated. It should be noted that the computed binding free energy of ligand via molecular docking and linear interaction energy (LIE) approaches are in good agreement with the corresponding experiments with coefficients of R = 0.72 and 0.75, respectively. NSC 319990, NSC 196515 and NSC 376254 compounds were demonstrated that can inhibit MPVX methyltransferase VP39 protein with the similar affinity compared to available inhibitor sinefungin. Moreover, nine residues involving Gln39, Gly68, Gly72, Asp95, Arg97, Val116, Asp138, Arg140 and Asn156 may be argued that they play an important role in binding process of inhibitors to VP39.Communicated by Ramaswamy H. Sarma.

QMrebind: incorporating quantum mechanical force field reparameterization at the ligand binding site for improved drug-target kinetics through milestoning simulations

Understanding the interaction of ligands with biomolecules is an integral component of drug discovery and development. Challenges for computing thermodynamic and kinetic quantities for pharmaceutically relevant receptor-ligand complexes include the size and flexibility of the ligands, large-scale conformational rearrangements of the receptor, accurate force field parameters, simulation efficiency, and sufficient sampling associated with rare events. Our recently developed multiscale milestoning simulation approach, SEEKR2 (Simulation Enabled Estimation of Kinetic Rates v.2), has demonstrated success in predicting unbinding (koff) kinetics by employing molecular dynamics (MD) simulations in regions closer to the binding site. The MD region is further subdivided into smaller Voronoi tessellations to improve the simulation efficiency and parallelization. To date, all MD simulations are run using general molecular mechanics (MM) force fields. The accuracy of calculations can be further improved by incorporating quantum mechanical (QM) methods into generating system-specific force fields through reparameterizing ligand partial charges in the bound state. The force field reparameterization process modifies the potential energy landscape of the bimolecular complex, enabling a more accurate representation of the intermolecular interactions and polarization effects at the bound state. We present QMrebind (Quantum Mechanical force field reparameterization at the receptor-ligand binding site), an ORCA-based software that facilitates reparameterizing the potential energy function within the phase space representing the bound state in a receptor-ligand complex. With SEEKR2 koff estimates and experimentally determined kinetic rates, we compare and interpret the receptor-ligand unbinding kinetics obtained using the newly reparameterized force fields for model host-guest systems and HSP90-inhibitor complexes. This method provides an opportunity to achieve higher accuracy in predicting receptor-ligand koff rate constants.

Upgrading nirmatrelvir to inhibit SARS-CoV-2 Mpro via DeepFrag and free energy calculations

The first oral drug for the treatment of COVID-19, Paxlovid, has been authorized; however, nirmatrelvir, a major component of the drug, is reported to be associated with some side effects. Moreover, the appearance of many novel variants raises concerns about drug resistance, and designing new potent inhibitors to prevent viral replication is thus urgent. In this context, using a hybrid approach combining machine learning (ML) and free energy simulations, 6 compounds obtained by modifying nirmatrelvir were proposed to bind strongly to SARS-CoV-2 Mpro. The structural modification of nirmatrelvir significantly enhances the electrostatic interaction free energy between the protein and ligand and slightly decreases the vdW term. However, the vdW term is the most important factor in controlling the ligand-binding affinity. In addition, the modified nirmatrelvir might be less toxic to the human body than the original inhibitor.