Sirtuin 1 (SIRT1) is a class III family of protein histone deacetylases involved in NAD+-dependent deacetylation reactions. It has been suggested that SIRT1 activators may have a protective role against type 2 diabetes, the aging process, and inflammation. This study aimed to explore and identify medicinal plant compounds from Indonesian Herbal Database (HerbalDB) that might potentially become a candidate for SIRT1 activators through a combination of in silico and in vitro methods. Two pharmacophore models were developed using co-crystalized ligands that allosterically bind with SIRT1 similar to the putative ligands used by SIRT1 activators. Then, these were used for the virtual screening of HerbalDB. The identified compounds were subjected to molecular docking and 50 ns molecular dynamics simulation. Molecular dynamics simulation was analyzed using MM-GB(PB)SA methods. The compounds identified by these methods were tested in an in vitro study using a SIRT-Glo™ luminescence assay. Virtual screening using structure-based pharmacophores predicted that mulberrin as the best candidate SIRT1 activator. Virtual screening using ligand-based pharmacophores predicted that gartanin, quinidine, and quinine to be the best candidates as SIRT1 activators. The molecular docking studies showed the important residues involved were Ile223 and Ile227 at the allosteric region. The MM-GB(PB)SA calculations confirmed that mulberrin, gartanin, quinidine, quinine showed activity at allosteric region and their EC50 in vitro values are 2.10; 1.79; 1.71; 1.14 μM, respectively. Based on in silico and in vitro study results, mulberin, gartanin, quinidine, and quinine had good activity as SIRT1 activators.

Background: Ionizing radiation, such as gamma irradiation, serves as a useful approach to inhibit spore germination and to control pathogens in postharvest seeds. Recently, its application on phytochemical sources and its influence on antioxidant activity of various phytochemical compounds has become an interesting topic to be explored. Objective: The objectives of this study were to determine the effect of gamma irradiation as sterilization method on the resveratrol content and its antioxidant, HMG-CoA reductase inhibitory and dipeptidyl peptidase-4 (DPP-4) inhibitory activities of Melinjo (Gnetum gnemon) seeds. Methods: In this research, melinjo seeds were irradiated by 0.0; 2.5; 5.0; 7.5; and 10.0 kGy with gamma irradiation and then extracted with ethanol. The extracts were tested for resveratrol content with HPLC, antioxidant activities by DPPH assay, HMG-CoA inhibitory activity using HMG-CoA reductase assay kit and DPP-4 inhibitory activity using DPP-4 Inhibitor Screening Assay Kit. Gamma irradiation has effect on resveratrol content, antioxidant activity, HMG-CoA reductase inhibition and DPP-4 inhibitory activity. Results: From the research, the highest value of resveratrol content is 0.18±0.004 mg/g seeds powder found in 5.0 kGy gamma irradiation treatment with IC 50 94.64±0.236 µg/mL, while the highest HMG-CoA reductase inhibition is shown in 2.5 kGy irradiation dose. Melinjo seeds irradiated by 2.5 kGy gamma irradiation also shown a significant increase of DPP-4 inhibition activity. Conclusion: This study suggests that 2.5-5 kGy radiation is the effective gamma irradiation dose to improve the quality of melinjo seeds.

Objective: The importance of SIRT1 activator’s role in antidiabetic and anti-aging therapies is widely demonstrated. Drug discovery and developmentare time consuming. Drug design can be performed in silico using molecular dynamic approaches to accelerate and facilitate identification of the bestcompound candidates and their physicochemical characteristics and hit-to-lead selection.Methods: In silico study of SIRT1 activator for complexes using of Protein Data Bank (PDB) IDs 4ZZI, 4ZZJ 4ZZH, and 5BTR and 4TO ligand. Ligand–receptor interactions and bond energies were determined using molecular docking with the AutoDock4Zn program. Then, the complex with thebest bond energy was identified using a simulation of the molecular dynamics (50 ns) using the Amber program, and values for root mean squaredeviation, root mean square fluctuation, and bond energy were determined using the Molecular Mechanic–Poisson Boltzmann (Generalized Born)surface area (MM-PB[GB]SA) calculation.Results: Interaction analysis between activator ligand (4TO) and the SIRT1 receptor (PDB IDs 4ZZJ and 5BTR) revealed the ligand’s selectivity forhydrophobic interaction at Leu206, Ile223, Ile227, and hydrophilic interaction at Asn226, Glu230. Hydrogen bond interactions between Glu230 and Arg234(allosteric region) with Arg446, Val459, His473, and Asp475 (catalytic region) brought them close to the bounding substrate area. Bond energy valuesobtained using the MM-GB(PB)SA calculation showed 4TO interaction with 4ZZJ (MMGBSA ΔG, −31.4729–−26.6756; MMPBSA ΔG, −32.6292–−28.486].The bond energy value of the 4TO interaction with 5BTR showed MMGBSA ΔG = −40.6255–−30.0653 and MMPBSA ΔG = −34.6713–−25.9951.Conclusions: These findings provide important information on the target interaction of the bonds to the more selective SIRT1 activator useful fordrug discovery and development.

Introduction: to obtain the optimum extraction conditions of total phenolic content (TPC), total flavonoid content (TFC), antioxidant activity (AA) and angiotensin converting enzyme (ACE) inhibitory activity from Peperomia pellucida (L.) Kunth. Method: TPC, TFC, AA and ACE inhibitory activity was measured by Folin-Ciocalteu method, AlCl3 method, DPPH and FRAP methods, and ACE kit- WST. MAE efficiency parameters used were ethanol concentration, a sample to solvent ratio, extraction time, and microwave power. Results: The optimum conditions of MAE for TPC (49.78 mg GAE/g extract) and TFC (37.18 mg QE/g extract) were 80% ethanol, sample-solvent ratio of 1:12, extraction time of 2 min, MAE power of 30% and 80% ethanol, ratio of 1:12, time of 2 min, and 70% power respectively. The optimum AA was obtained at 65% ethanol, ratio of 1:10, time of 3 min, 50% power (DPPH method), and 65% ethanol, the ratio of 1:12, time of 1 min, and 70% power (FRAP method) respectively. The optimum ACE inhibitory activity (54.73% at a concentration of 100 μg/mL) were obtained at 80% ethanol, ratio of 1:12, time of 2 min, and 70% power. Conclusion: The analysis of Pearson correlation indicated that there was no correlation between TPC with FRAP method; TPC with ACE inhibitory activity and TFC with DPPH method. Key words: ACE, Antioxidant Activity, Flavonoid Compounds, Microwave-Assisted Extraction, Peperomia pellucida (L.) Kunth, Phenolic Compounds.

Ligand-based virtual screening (LBVS) and structure-based virtual screening (SBVS), and their combinations, are frequently conducted in modern drug discovery campaigns. As a form of combination, an amalgamation of methods from ligand- and structure-based information, termed hybrid VS approaches, has been extensively investigated such as using interaction fingerprints (IFPs) in combination with machine learning (ML) models. This approach has the potential to prioritize active compounds in terms of protein-ligand binding and ligand structural characteristics, which is assumed to be difficult using either one of the approaches. Herein, we present an IFP, named the fragmented interaction fingerprint (FIFI), for hybrid VS approaches. FIFI is constructed from the extended connectivity fingerprint atom environments of a ligand proximal to the protein residues in the binding site. Each unique ligand substructure within each amino acid residue is encoded as a bit in FIFI while retaining sequence order. From the retrospective evaluation of activity prediction using a limited number and variety of active compounds for six biological targets, FIFI consistently showed higher prediction accuracy than that using previously proposed IFPs. For the same data sets, the screening performance of LBVS, SBVS sequential VS, parallel VS, and other hybrid VS approaches was investigated. Compared to these approaches, FIFI in combination with ML showed overall stable and high prediction accuracy, except for one target: the kappa opioid receptor, where the extended connectivity fingerprint combined with ML models showed better performance than other approaches by wide margins.

Objective: Cancer is a disease that can occur because of apoptosis failure. One of the causes of apoptosis failure is the presence of MDM2 inhibiting the activity of p53 so procaspase-3 could not be activated to caspase-3. Currently, the treatment of cancer has been applied widely, but more effective treatment always needed because cancer has been mutated. One of the treatments is the search for natural resources that come from the sea. The pursuit of the bioactive compound from marine organisms is not straightforward because it takes a long time and costly. Methods: Therefore, in silico method is used. This study conducted a docking of bioactive compounds from saponin and 2, 5-Piperazinedione as the MDM2 inhibitor and procaspase-3 activator by using Auto Dock and Vina. Results: The results showed that 18-Oxotryprostatin A and Intercedenside A were the best bioactive compounds to serve as MDM2 Inhibitor and 6-Methoxyspirotryprostatin B and Frondoside A as a procaspase-3 activator due to their low binding energy. Conclusion: Most recommended bioactive compounds are those who have low binding energy, which is 18-Oxotryprostatin A with the value of -8,6 kcal/mol and Intercedenside A with the value of -7,0 kcal/mol. Key words: 2,5-Piperazinedione, Activator procaspase-3, Inhibitor MDM2, Cancer, Molecular docking, Saponin.