/pdf/benchmarking-the-ability-of-novel-compounds-to-inhibit-sars-1nqepgq0.pdf

Benchmarking the ability of novel compounds to inhibit SARS-CoV-2 main protease using steered molecular dynamics simulations

https://doi.org/10.1016/j.compbiomed.2022.105738

Investigating the structure-activity relationship of marine polycyclic batzelladine alkaloids as promising inhibitors for SARS-CoV-2 main protease (Mpro)

In silico and in vitro analysis of PPAR - α / γ dual agonists: Comparative evaluation of potential phytochemicals with anti-obesity drug orlistat

The protein is an ideal food matrix binding volatile compounds. However, the synergistic effects between the protein rheological behavior and protein-ligand interactions on the small molecule ligand retention are not fully reported. This study clarified how aldehydes bind to myosin during the constant thermal treatment. The results presented that the myosin unfolded at 25–64 °C and were cross-linked to generate polymers at 75–79 °C. The α-helix fractions of myosin turned into β-turn and random coil fractions, regulating the rheological behavior and exposing more binding sites with aldehydes. The binding affinity of myosin with aldehydes was confirmed. Notably, the fluorescence quenching behaviors of myosin were increased (p < 0.05) with the increase of pentanal concentration and heating temperature. Synergistic effects between the rheological behavior of myosin and the myosin-aldehyde molecular interaction might predominantly contribute to the aldehyde retention. The hydrophobic interaction, amino acid residues, free sulfhydryl groups, and hydrogen bond might be the key (non)covalent linkages for myosin to bind aldehydes. Thr125, Pro128, Trp131, and Val187 might be key sites between myosin and pentanal, among which the chemical forces were the conventional hydrogen bond, alkyl, pi-alkyl, and alkyl hydrophobic among them, respectively. 

Molecular docking simulation combining with multi-spectroscopy techniques clarify how small molecule ligands bind to biomacromolecule: Myosin and aldehydes as a case study

Drug design is a time-consuming and cumbersome process due to the vast search space of drug-like molecules and the difficulty of investigating atomic and electronic interactions. The present paper proposes a computational drug design workflow that combines artificial intelligence (AI) methods, i.e., an evolutionary algorithm and artificial neural network model, and molecular dynamics (MD) simulations to design and evaluate potential drug candidates. For the purpose of illustration, the proposed workflow was applied to design drug candidates against the main protease of severe acute respiratory syndrome coronavirus 2. From the ∼140,000 molecules designed using AI methods, MD analysis identified two molecules as potential drug candidates.

/pdf/design-of-sars-cov-2-main-protease-inhibitors-using-1smhaf1p.pdf

Shayan Shadravan

Papers

De novo design of novel protease inhibitor candidates in the treatment of SARS-CoV-2 using deep learning, docking, and molecular dynamic simulations.