| Summary: | Computational chemistry and biology are helpful in understanding protein structure and the relationships between structure and biological activity. In particular, to develop a new drug, medicinal chemists and pharmacologists are interested in understanding and predict drug action at a molecular level, especially if the action of the drug is unknown or poorly understood. In these cases, the molecular modelling should reduce some of the work in the development of drug compounds. Here, we present two examples of homology modelling applied to pharmacology that obtained a great success. Phosphatidylcholine-sterol acyltransferase (LCAT) is a glycoprotein of 416 residues, synthesized by the liver and secreted in plasma. It catalyzes the transacylation of the sn−2 fatty acid of lecithin to the free 3−OH group of cholesterol, generating cholesterol esters and lysolecithin. LCAT shares the Ser/Asp−Glu/His triad with lipases, esterases and proteases, but the low level of overall sequence homology between LCAT and these enzymes makes standard modelling procedures unsuitable. For this reason, to build an LCAT model, we implemented a combined approach that included folding recognition, secondary structure prediction, and ‘chimeric’ homology modeling. In detail, the ab initio model was used as scaffold to merge the two best homology templates identified, Paucimonas lemoignei depolymerase for the N-terminus and Candida antarctica lipase A for the C-terminus. In this way, we built an accurate LCAT structure with a well-defined binding site. Then, we performed a high-throughput virtual screening exploration of LCAT pocket with a large database of chemical compounds. The best compounds identified during the HTS were tested in vitro and demonstrated the ability to modulate LCAT activity. G-protein coupled receptors (GPCRs) responding to signalling molecules are key transducers in cell-to-cell communication. Malfunctioning of GPCRs invariably leads to disease conditions; for this reason, they represent the target of more than 70% of ...
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