In-silico and in-vitro screening of Asiatic acid and Asiaticoside A against Cathepsin S enzyme

BACKGROUND

Atherosclerosis is a form of cardiovascular disease that affects the endothelium of the blood vessel. Series of events are involved in the pathophysiology of this disease which includes the breaking down of the connective tissue elastin and collagen responsible for the tensile strength of the arterial wall by proteolytic enzyme. One of these enzymes called Cathepsin S (CatS) is upregulated in the progression of the disease and its inhibition has been proposed to be a promising pharmacological target to improve the prognosis of the disease condition. Asiatic acid and asiaticoside A are both pentacyclic triterpenoids isolated from Centella asiatica. Their use in treating various cardiovascular diseases has been reported.

METHODS

In this study through in silico and in vitro methods, the pharmacokinetic properties, residue interaction, and inhibitory activities of these compounds were checked against the CatS enzyme. The SwissADME online package and the ToxTree 3.01 version of the offline software were used to determine the physicochemical properties of the compounds.

RESULT

Asiatic acid reported no violation of the Lipinski rule while asiaticoside A violated the rule with regards to its molecular structure and size. The molecular docking was done using Molecular Operating Environment (MOE) and the S-score of - 7.25988, - 7.08466, and - 4.147913 Kcal/mol were recorded for LY300328, asiaticoside A, and asiatic acid respectively. Asiaticoside A has a docking score value (- 7.08466Kcal/mol) close to the co-crystallize compound. Apart from the close docking score, the amino acid residue glycine69 and asparagine163 both interact with the co-crystallized compound and asiaticoside A. The in vitro result clearly shows the inhibitory effect of asiaticoside and asiatic acid. Asiaticoside A has an inhibitory value of about 40% and asiatic acid has an inhibitory value of about 20%.

CONCLUSION

This clearly shows that asiaticoside will be a better drug candidate than asiatic acid in inhibiting the CatS enzyme for the purpose of improving the outcome of atherosclerosis. However, certain modifications need to be made to the structural make-up of asiaticoside A to improve its pharmacokinetics properties.

Predicting binding poses and affinity ranking in D3R Grand Challenge using PL-PatchSurfer2.0

Computational prediction of protein-ligand interactions is a useful approach that aids the drug discovery process. Two major tasks of computational approaches are to predict the docking pose of a compound in a known binding pocket and to rank compounds in a library according to their predicted binding affinities. There are many computational tools developed in the past decades both in academia and industry. To objectively assess the performance of existing tools, the community has held a blind assessment of computational predictions, the Drug Design Data Resource Grand Challenge. This round, Grand Challenge 4 (GC4), focused on two targets, protein beta-secretase 1 (BACE-1) and cathepsin S (CatS). We participated in GC4 in both BACE-1 and CatS challenges using our molecular surface-based virtual screening method, PL-PatchSurfer2.0. A unique feature of PL-PatchSurfer2.0 is that it uses the three-dimensional Zernike descriptor, a mathematical moment-based shape descriptor, to quantify local shape complementarity between a ligand and a receptor, which properly incorporates molecular flexibility and provides stable affinity assessment for a bound ligand-receptor complex. Since PL-PatchSurfer2.0 does not explicitly build a bound pose of a ligand, we used an external docking program, such as AutoDock Vina, to provide an ensemble of poses, which were then evaluated by PL-PatchSurfer2.0. Here, we provide an overview of our method and report the performance in GC4.