Mechanism of Inhibition of Cholesteryl Ester Transfer Protein by Small Molecule Inhibitors

Cholesteryl ester transfer protein (CETP) facilitates the bidirectional exchange of cholesteryl esters and triglycerides between high-density lipoproteins and low- or very low-density lipoproteins. Recent studies have shown that the impairment of lipid exchange processes of CETP can be an effective strategy for the treatment of cardiovascular diseases (CVDs). Understanding the molecular mechanism of CETP inhibition has, therefore, attracted tremendous attention in recent past. In this study, we explored the detailed mechanism of CETP inhibition by a series of recently reported small molecule inhibitors that are currently under preclinical testing. Our results from molecular dynamics simulations and protein–ligand docking studies suggest that the hydrophobic interactions between the CETP core tunnel residues and inhibitor moieties play a pivotal role, and physical occlusion of the CETP tunnel by these small molecules is the primary mechanism of CETP inhibition. Interestingly, bound inhibitors were found to increase the plasticity of CETP, which was explained by principal component analysis that showed a larger space of sampling of CETP C-domain due to inhibitor binding. The atomic-level details presented here could help accelerate the structure-based drug-discovery processes targeting CETP for CVD therapeutics

Whole proteome mapping of compound-protein interactions

Off-target binding is one of the primary causes of toxic side effects of drugs in clinical development, resulting in failures of clinical trials. While off-target drug binding is a known phenomenon, experimental identification of the undesired protein binders can be prohibitively expensive due to the large pool of possible biological targets. Here, we propose a new strategy combining chemical similarity principle and deep learning to enable proteome-wide mapping of compound-protein interactions. We have developed a pipeline to identify the targets of bioactive molecules by matching them with chemically similar annotated “bait” compounds and ranking them with deep learning. We have constructed a user-friendly web server for drug-target identification based on chemical similarity (DRIFT) to perform searches across annotated bioactive compound datasets, thus enabling high-throughput, multi-ligand target identification, as well as chemical fragmentation of target-binding moieties

Catestatin Gly364Ser Variant Alters Systemic Blood Pressure and the Risk for Hypertension in Human Populations via Endothelial Nitric Oxide Pathway

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