Hybrid drug-screening strategy identifies potential SARS-CoV-2 cell-entry inhibitors targeting human transmembrane serine protease

Abstract

The spread of coronavirus infectious disease (COVID-19) is associated with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which has risked public health more than any other infectious disease. Researchers around the globe use multiple approaches to identify an effective approved drug (drug repurposing) that treats viral infections. Most of the drug repurposing approaches target spike protein or main protease. Here we use transmembrane serine protease 2 (TMPRSS2) as a target that can prevent the virus entry into the cell by interacting with the surface receptors. By hypothesizing that the TMPRSS2 binders may help prevent the virus entry into the cell, we performed a systematic drug screening over the current approved drug database. Furthermore, we screened the Enamine REAL fragments dataset against the TMPRSS2 and presented nine potential drug-like compounds that give us clues about which kinds of groups the pocket prefers to bind, aiding future structure-based drug design for COVID-19. Also, we employ molecular dynamics simulations, binding free energy calculations, and well-tempered metadynamics to validate the obtained candidate drug and fragment list. Our results suggested three potential FDA-approved drugs against human TMPRSS2 as a target. These findings may pave the way for more drugs to be exposed to TMPRSS2, and testing the efficacy of these drugs with biochemical experiments will help improve COVID-19 treatment. Supplementary information The online version contains supplementary material available at 10.1007/s11224-022-01960-w.

Structural analysis of human G‐protein‐coupled receptor 17 ligand binding sites

“…The apo structure of the GPR17 model (group 1), GPR17 complexed with UDP at ligand binding site 1 (group 2), GPR17 complexed with UDP at ligand binding site 2 (group 3), and GPR17 complexed with UDP at both sites (group 4) were the four groups selected for the molecular dynamics simulations in the lipid environment. In our previous work, we have shown that the combination of molecular docking and simulations and free energy calculations can be an efficient modern strategy to screen drug candidates targeting Coronavirus target proteins 49–52 . By restricting the backbone of the helices, the 3D molecule was locally reduced in vacuo, providing a preliminary optimization of the rough shape generated from homology modeling 16 .…”

“…In our previous work, we have shown that the combination of molecular docking and simulations and free energy calculations can be an efficient modern strategy to screen drug candidates targeting Coronavirus target proteins. [49][50][51][52] By restricting the backbone of the helices, the 3D molecule was locally reduced in vacuo, providing a preliminary optimization of the rough shape generated from homology modeling. 16 The membrane environment was next modeled using the DPPC in the liquid-crystalline phase developed by Tieleman and Berendsen.…”

Identifying Potent Fat Mass and Obesity-Associated Protein Inhibitors Using Deep Learning-Based Hybrid Procedures

“…Researchers have proposed a mixed approach for drug discovery called hybrid drug virtual screening [51]. For example, the deep learning architecture encompassing the end-to-end network structure exhibits exceptional speed and accuracy in recognizing patterns [52,53].…”

DeepBindGCN: Integrating Molecular Vector Representation with Graph Convolutional Neural Networks for Protein–Ligand Interaction Prediction

“…Moreover, combining the protein–ligand binding prediction model with the affinity prediction model can be more powerful in identifying strong affinity candidates. As aforementioned, hybrid screening has been used to virtualize potential drugs for given targets [ 26 ]. However, we still lack a model that can screen over a database size of 100,000~1,000,000 accurately and efficiently with the ability to distinguish between the spatial and physical–chemical features of protein–ligand binding.…”