IN SILICO STUDY: MOLECULAR DOCKING STUDY OF LEAVES SOURSOP (Annona muricata L.) ACTIVE COMPOUNDS AS ANTIHYPERLIPIDEMIA
Riska Prasetiawati (a), Rubi Rahman Fauji (a), Benny Permana (b), Novriyanti Lubis (a*)

a, Department of Analytical Pharmaceutical Chemistry Faculty of Mathematics and Natural Sciences, University, Garut 44151, Indonesia

b, School of Pharmacy, Bandung Institute of Technology, Jl. Ganesha 10, Bandung 40132


Abstract

Hyperlipidemia is a condition where is the level of fat increase in the blood circulation. This condition can cause the narrowing of the arteries called atherosclerosis. Soursop leaves (Annona muricata L.) are traditionally used as anticholesterol, antibacterial, anti-inflammatory and antioxidant. There are 63 known compounds contained in soursop leaves. The purpose of this study was to search candidate compounds from soursop leaves that have activities as antihyperlipidemic through interactions with HMG CoA reductase and PPAR&#945- receptor carried out in silico by molecular docking method, include prediction of absorption, distribution, and toxicity. Molecular docking results showed chlorogenic acid had a good affinity for the HMG CoA reductase enzyme, the value of \triangleG -8.55 kcal/mol and KI value of 543.74 \muM, while the comparison compound produced \triangleG -7.33 kcal/mol and KI value of 4.24 \muM. Toward PPAR&#945- receptor, compound with the best affinity was solamin, with the value of \triangleG -8.14 kcal/mol and KI value of 1.09 \muM, while the comparison compound produced avalue of \triangleG -7.17 kcal/mol and Ki value 5.51 \muM. Solamin fulfills Lipinski^s rule of five while chlorogenic acid didn^t fulfills this rule. Both compounds had good absorption and distribution profiles and are not carcinogenic but mutagenic chlorogenic acid. In conclusion, both compounds could be used as potential candidates for hyperlipidemia therapy

Keywords: HMG CoA reductase enzyme, molecular docking, PPAR&#945- receptors, soursop leaves (Annona muricata L.)

Topic: Theoretical and Computational Chemistry

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