The In Silico Fischer Lock-and-Key Model: The Combined Use of Molecular Descriptors and Docking Poses for the Repurposing of Old Drugs

Clustering Protein Binding Pockets and Identifying Potential Drug Interactions: A Novel Ligand-Based Featurization Method

Protein-ligand interactions are essential to drug discovery and drug development efforts. Desirable on-target or multitarget interactions are the first step in finding an effective therapeutic, while undesirable off-target interactions are the first step in assessing safety. In this work, we introduce a novel ligand-based featurization and mapping of human protein pockets to identify closely related protein targets and to project novel drugs into a hybrid protein-ligand feature space to identify their likely protein interactions. Using structure-based template matches from PDB, protein pockets are featured by the ligands that bind to their best co-complex template matches. The simplicity and interpretability of this approach provide a granular characterization of the human proteome at the protein-pocket level instead of the traditional protein-level characterization by family, function, or pathway. We demonstrate the power of this featurization method by clustering a subset of the human proteome and evaluating the predicted cluster associations of over 7000 compounds.

Network pharmacology and experimental verification based research into the effect and mechanism of Aucklandiae Radix-Amomi Fructus against gastric cancer

To investigate the mechanism of the Aucklandiae Radix–Amomi Fructus (AR–AF) herb pair in treating gastric cancer (GC) by using network pharmacology and experimental verification. Using the traditional Chinese medicine system pharmacology database and analysis platform (TCMSP), the major active components and their corresponding targets were estimated and screened out. Using Cytoscape 3.7.2 software, a visual network was established using the active components of AR–AF and the targets of GC. Based on STRING online database, the protein interaction network of vital targets was built and analyzed. With the Database for Annotation, Visualization, and Integrated Discovery (DAVID) server, the gene ontology (GO) biological processes and the Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathways of the target enrichment were performed. AutoDock Vina was used to perform molecular docking and calculate the binding affinity. The mRNA and protein expression levels of the hub targets were analyzed by the Oncomine, GEPIA, HPA databases and TIMER online tool, and the predicted targets were verified by qRT–PCR in vitro. Eremanthin, cynaropicrin, and aceteugenol were identified as vital active compounds, and AKT1, MAPK3, IL6, MAPK1, as well as EGFR were considered as the major targets. These targets exerted therapeutic effects on GC by regulating the cAMP signaling pathway, and PI3K-Akt signaling pathway. Molecular docking revealed that these active compounds and targets showed good binding interactions. The validation in different databases showed that most of the results were consistent with this paper. The experimental results confirmed that eremanthin could inhibit the proliferation of AGS by reducing the mRNA expression of hub targets. As predicted by network pharmacology and validated by the experimental results, AR–AF exerts antitumor effects through multiple components, targets, and pathways, thereby providing novel ideas and clues for the development of preparations and the treatment of GC.

Exploring the SARS-CoV-2 Proteome in the Search of Potential Inhibitors via Structure-Based Pharmacophore Modeling/Docking Approach

To date, SARS-CoV-2 infectious disease, named COVID-19 by the World Health Organization (WHO) in February 2020, has caused millions of infections and hundreds of thousands of deaths. Despite the scientific community efforts, there are currently no approved therapies for treating this coronavirus infection. The process of new drug development is expensive and time-consuming, so that drug repurposing may be the ideal solution to fight the pandemic. In this paper, we selected the proteins encoded by SARS-CoV-2 and using homology modeling we identified the high-quality model of proteins. A structure-based pharmacophore modeling study was performed to identify the pharmacophore features for each target. The pharmacophore models were then used to perform a virtual screening against the DrugBank library (investigational, approved and experimental drugs). Potential inhibitors were identified for each target using XP docking and induced fit docking. MM-GBSA was also performed to better prioritize potential inhibitors. This study will provide new important comprehension of the crucial binding hot spots usable for further studies on COVID-19. Our results can be used to guide supervised virtual screening of large commercially available libraries.

Estimation of the ligand-binding free energy of checkpoint kinase 1 via non-equilibrium MD simulations

Checkpoint kinase 1 (CHK1) is a serine/threonine-protein kinase that is involved in cell cycle regulation in eukaryotes. Inhibition of CHK1 is thus considered as a promising approach in cancer therapy. In this study, the fast pulling of ligand (FPL) process was applied to predict the relative binding affinities of CHK1 inhibitors using non-equilibrium molecular dynamics (MD) simulations. The work of external harmonic forces to pull the ligand out of the binding cavity strongly correlated with the experimental binding affinity of CHK1 inhibitors with the correlation coefficient of R = -0.88 and an overall root mean square error (RMSE) of 0.99 kcal/mol. The data indicate that the FPL method is highly accurate in predicting the relative binding free energies of CHK1 inhibitors with an affordable CPU time. A new set of molecules were designed based on the molecular modeling of interactions between the known inhibitor and CHK1 as inhibitory candidates. Molecular docking and FPL results exhibited that the binding affinities of developed ligands were similar to the known inhibitor in interaction with the catalytic site of CHK1, producing very potential CHK1 inhibitors of that the inhibitory activities should be further evaluated in vitro.