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"ABSTRACTInhibitor sodium glucose co-transporter 2 (SGLT2) telah dievaluasi dalam uji klinis sebagai dasar strategi penanganan hiperglikemia pada diabetes. Namun, karena tergolong kelas obat baru antidiabetik oral, inhibitor SGLT-2 masih jarang ditemukan di Indonesia dan harga beli yang masih tinggi untuk dijangkau. Studi ini bertujuan untuk menemukan senyawa kandidat yang berpotensi mempunyai aktivitas sebagai inhibitor SGLT2 dari pangkalan data herbal Indonesia melalui pendekatan penapisan virtual berbasis farmakofor. Model farmakofor inhibitor SGLT2 dibuat dari 10 training set ligan inhibitor SGLT2 menggunakan LigandScout 4.1.5. Sepuluh model farmakofor yang terbentuk kemudian divalidasi menggunakan metode test set dan decoy set. Farmakofor model-1 merupakan model farmakofor terbaik dengan nilai 0,9080, EF1 =56,5 , EF5 = 56,5 dan AUC100 = 0,87 yang akan dijadikan model untuk penapisan virtual. Model-1 terdiri dari 1 interaksi hidrofobik, 1 cincin aromatis, 4 donor ikatan hidrogen dan 5 akseptor ikatan hidrogen. Penapisan virtual menghasilkan tiga senyawa kandidat Hits yang memiliki pharmacophore fit score terbaik terhadap model 1, yakni vitexin = 113,62; cucumerin A = 112,62; dan cucumerin B = 113,51. Sehingga dapat disimpulkan bahwa Vitexin, Cucumerin A, dan Cucumerin B berpotensi sebagai kandidat senyawa yang mempunyai aktivitas sebagai inhibitor SGLT2.

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ABSTRACTSodium glucose co transporter 2 SGLT2 inhibitor has been evaluated in clinical trials as the basic strategy of hyperglycemia handling in diabetes. However, because of SGLT 2 inhibitor was the new class of oral antidiabetic, it still rare in Indonesia and because of the high price. This study was intended to find compounds that capable of having activity as SGLT2 inhibitors from Indonesian herbal database through a pharmacophore based virtual screening approach. The SGLT2 inhibitor pharmacophore models was made from 10 training sets of SGLT2 ligand inhibitors using the LigandScout 4.1.5. Ten pharmacophore models which has been made were validated using test set and decoy set methods. The model 1 pharmacophore was the best model, with values of 0.9080, EF1 56.5 , EF5 56.5 and AUC100 0.87 which will serve as models for virtual screening. Model 1 consists of 1 hydrophobic interaction, 1 aromatic ring, 4 hydrogen bond donors and 5 hydrogen bond acceptors. Virtual screening showed three compounds Hits which have best pharmacophore fit scores according to model 1, they were Vitexin 113.62 Cucumerin A 112,62 and Cucumerin B 113,51. These results conclude that Vitexin, Cucumerin A, and Cucumerin B potentially have an activity as a SGLT2 inhibitor."

"Kanker merupakan penyebab kematian utama di seluruh dunia. Salah satu faktor terjadinya kanker adalah modifikasi epigenetik yang abnormal (hipermetilasi). Hipermetilasi yang terjadi pada gen diyakini bahwa yang berperan besar dalam proses karsinogenesis adalah enzim DNA metiltransferase (DNMT). Penelitian-penelitian yang dilakukan saat ini untuk menemukan senyawa inhibitor DNMT dari bahan alam. Salah satu metode yang mendukung untuk analisis ini adalah metode in silico. Dalam penelitian ini, diteliti beberapa senyawa pilihan dari basis data herbal Indonesia hasil penapisan virtual terhadap aktivitasnya sebagai inhibitor DNMT. Hasil penambatan molekuler senyawa Cassiamin C, Procyanidin B2, Ent-epicatechin-4alpha-8-ent-epicatechin, Epicatechin-4beta-8-epicatechin-3-O-gallate, Neorhusflavanone, 3-O-galloylepigallocatechin-4beta-6-epicatechin-3-O-gallate, Withanolide, 3-O-galloylepigallocatechin-4beta-6-epigallocatechin-3-O-gallate, Cyanidin-3-6''-caffeylsophoroside-5-glucoside, Epifriedelinol, Gallo-catechin-4alpha-8-epicatechin, Scutellarein-7-glucosyl-1-4-rhamnoside, Epigallo-catechin-3-gallate (EGCG) (kontrol positif), dan sinefungin (kokristal) didapatkan nilai ΔG secara berturut-turut, -9.34, -10.95, -7.95, -11.01, -8.78, -8.87, -11.49, -7.98, -5.92, -8.92, -9.17, -8.76, -9.70, dan -9.11 kkal/mol. Senyawa cassiamin C, procyanidin B2, epicatechin-4beta-8-epicatechin-3-O-gallate, withanolide, dan gallocatechin-4alpha-8-epicatechin memiliki ΔG lebih rendah dari senyawa sinefungin (kokristal) dan EGCG (kontrol positif). Sehingga, tahap selanjutnya akan dilakukan simulasi dinamika molekuler terhadap tujuh ligan tersebut. Hasil simulasi dinamika molekuler menunjukkan aktivitas terbaik secara keseluruhan yaitu pada senyawa procyanidin B2, epicatechin-4beta-8-epicatechin-3-O-gallate, dan gallocatechin-4alpha-8-epicatechin. Residu asam amino yang penting bagi aktivitas inhibitor DNMT1 adalah Phe1145, Glu1168, Met1169, Cys1191, Glu1266, Ala1579, dan Val1580.

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Cancer is the leading cause of death worldwide. Factors of cancer is an abnormal epigenetic modifications (hypermethylation). Hypermethylation that occur in genes believed that played a major role in process of carcinogenesis is DNA methyltransferase (DNMT) enzyme. Recent studies is conducted to find DNMT inhibitor compounds from natural materials. Method that support for this analysis is in silico studies. In this study, several selected compounds from herbal database Indonesia results of virtual screening will be studying for the activity as an inhibitor DNMT. Results molecular docking of Cassiamin C, Procyanidin B2, Epicatechin-4alphaent-8-ent-epicatechin, Epicatechin-4beta-8-epicatechin-3-O-gallate, Neorhusflavanone, 3-O-galloylepigallocatechin-4beta-6-epicatechin-3-O-gallate, Withanolide, 3-O-galloylepigallocatechin-4beta-6-epigallocatechin-3-O-gallate, Cyanidin-3-6''-caffeylsophoroside-5-glucoside, Epifriedelinol, Gallocatechin-4alpha-8-epicatechin, Scutellarein-7-glucosyl-1-4-rhamnoside, Epigallocatechin-3-gallate (EGCG) (positive control), and Sinefungin (co-crystal) compounds, ΔG values obtained -9.34, -10.95, -7.95, -11.01, -8.78, -8.87, -11.49, -7.98, -5.92, -8.92, -9.17, -8.76, -9.70, and -9.11 kcal/mol, respectively. Cassiamin C, Procyanidin B2, Epicatechin-4beta-8-epicatechin-3-O-gallate, Withanolide, and Gallocatechin-4alpha-8-epicatechin compounds had lower ΔG than Sinefungin (co-crystal) and EGCG (positive control) compounds. Therefore, molecular dynamic simulation of seven selected compounds will be performed. The results of molecular dynamic simulation shows the best overall activity is Procyanidin B2, Epicatechin-4beta-8-epicatechin-3-O-gallate, and Gallocatechin-4alpha-8-epi-catechin compounds. Amino acid residues which are important for the activity of DNMT1 inhibitor is Phe1145, Glu1168, Met1169, Cys1191, Glu1266, Ala1579, and Val1580."

"Histon Deasetilase 2 (HDAC2) merupakan salah satu enzim dari suatu superfamily 18 enzim zinc-dependent yang kini tengah banyak diteliti sebagai target terapeutik. HDAC2 merupakan bagian dari HDAC kelas 1 yang diketahui memiliki aktivitas kuat sebagai katalis, dan umumnya merupakan target bagi inhibitor HDAC (HDACi). HDAC2 berperan pada gejala komplikasi akhir diabetes seperti nefropati diabetes. Sehingga penelitian lebih lanjut terkait inhibitor enzim-enzim HDAC khususnya HDAC2 perlu dikembangkan.Dalam penelitian ini, diteliti senyawa-senyawa dari basis data herbal Indonesia terhadap aktivitasnya sebagai inhibitor HDAC2, dengan melakukan penapisan virtual, untuk mendapatkan senyawa-senyawa pilihan, kemudian dilakukan simulasi dinamika molekuler untuk mengetahui interaksi senyawa sebagai inhibitor enzim. Didapatkan sepuluh besar peringkat senyawa penapisan virtual, kemudian diambil lima diantaranya dengan kriteria terbaik untuk dilakukan simulasi dinamika molekuler yaitu senyawa Boesenbergin B, Pongachalcone I, 6,8-Diprenylgenistein, Marmin, Mangostin, dengan kristal ligan N-(2-aminophenyl)benzamide sebagai kontrol positif, dengan nilai ΔG secara berturut-turut -8.28; -9.15; -7.05; -9.07; -7.15 dan ΔG kontrol positif -10.27. Dari simulasi dinamika molekuler diketahui aktivitas inhibitor HDAC2 berinteraksi pada residu asam amino penting yaitu His145C, Tyr308C, Zn379C, Leu276C, Phe155C, Phe210C, Leu144C, Met35C.

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Histone Deacetylase 2 (HDAC2) is one of a superfamily of 18 zinc-dependent enzymes, which now being widely investigated as therapeutic targets. HDAC2 is part of class I HDAC enzymes which knownly active as a strong catalys, and generally is the target for HDAC inhibitors (HDACi). HDAC2 play a role in the symptoms of late complications of diabetes such as diabetic nephropathy. So further research related to HDAC inhibitors, particularly HDAC2 need to be developed.In this study, research will be performed using virtual screening method to obtain several herbal compounds against their activities as HDAC2 inhibitors from the Indonesian herbal database, molecular dynamics simulations are then conducted to understand the interaction of compounds as inhibitors of the enzyme. The results of virtual screening process managed to obtained ten compounds with the highest Pharmacophore fit score rating, and would selected five compounds with the best criteria for molecular dynamics simulations, which are Boesenbergin B, Pongachalcone I, 6,8-Diprenylgenistein, Marmin, Mangostin, and active crytal ligand N-(2-aminophenyl)benzamide as a positive control, with respectively ΔG values obtained -8.28; -9.15; -7.05; -9.07; -7.15 and -10.27 as the ΔG value of active crystal ligand. From moleculer dynamics simulation of the activity of HDAC2 inhibitors are known to interact in two important amino acid residue that is His145C, Tyr308C, Zn379C, Leu276C, Phe155C, Phe210C, Leu144C, Met35C."