Supramolecular modelling of an LCAT-rHDL assembly and cholesterol trans- esterification mechanism

LCAT is a liver-secreted protein that circulates in plasma reversibly bound to lipoproteins, where its main function is to catalyze cholesterol esterification, transferring an acyl chain from phosphatidylcholine to free cholesterol, thus playing a crucial role in HDL maturation and reverse cholesterol transfer. Loss of function mutations in the LCAT gene may lead to LCAT deficiencies, rare monogenic disorders of lipid metabolism with important clinical consequences, such as anemia and a severe renal failure, the latter being the first cause of morbidity and mortality in affected patients. No definite cure, at present, is available for LCAT deficiency. Recombinant enzyme replacement therapy (ERT) has been tested but, although effective, may be unsuitable for chronic treatments. Therefore, the viability of a SM-therapy to treat LCAT deficiencies would provide major advantages to patients compared to ERT, leading to an improved quality of life and lower social costs. Preliminary results show that small molecule (SM) allosteric activators may operate LCAT mutants in vitro, thus, the aim of this project is to rationalize the in-silico design of LCAT activators that could partially restore enzyme activity in carriers of defective LCAT. In order to identify selective and powerful LCAT activators candidates, an in-depth knowledge of LCAT enzymatic reaction mechanism is mandatory, although, since LCAT is active prevalently on the surface of HDLs, the role of Apo-AI–LCAT interactions cannot be underestimated. We took advantage of recently published crystallographic LCAT structures and assembled a model of a reconstituted HDL (rHDL); using molecular dynamics, stochastic conformational search, protein-protein docking and other structural bioinformatics tools, we are providing a general model of LCAT activation by Apo-AI and a study of the dynamics behaviour of LCAT subdomains critical to catalytic site accessibility and interaction with HDLs. The generated models will be useful to study LCAT mutants and understand how novel activators can restore their functionality.