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All Journal Indonesian Journal of Pharmaceutical Science and Technology Journal of Pharmaceutical Sciences and Community
Tasia Amelia
School Of Pharmacy, Bandung Institute Of Technology, Jalan Ganesha 10, Bandung 40132, Indonesia
Computer Science & IT Medicine & Pharmacology

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Antiviral Activity and Toxicity Prediction of Compounds Contained in Figs (Ficus carica L.) by In Silico Method Sophi Damayanti; Khonsa Khonsa; Tasia Amelia
Indonesian Journal of Pharmaceutical Science and Technology Vol 8, No 1 (2021)
Publisher : Indonesian Journal of Pharmaceutical Science and Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24198/ijpst.v8i1.29868

Abstract

Viral infection is a global health problem that can cause endemic to pandemic. Compounds delivered from plants has been developed as an alternative antiviral agent. One of the plants that can be used as antiviral therapy is Ficus carica L. (figs). The aim of this research is to predict the inhibitory activity and toxicity of compounds contained in figs as an antiviral for HIV-1 using in silico method. Compounds were docked to the HIV-1 Reverse Transcriptase protein (PDB ID: 3LAL). Threedimentional structures were modeled using GaussView and optimized using Gaussian 09W. Optimized compounds were docked to the target protein using AutoDock Tools and the interaction to protein binding side were analyzed in comparison with the standard compounds. The standard compound used for the analysis nevirapine, efavirenz, and doravirine. The compound toxicity was analyzed using ECOSAR and Toxtree. Based on the results, the compounds that has similar interaction to the standard compounds were campesterol which has 4 similar hydrophobic interactions. Based on the classification of Cramer Rules for toxicity test, campesterol are classified in class 3 (high toxicity) and according to the Benigni/Bossa Rulebase classification, campesterol are negative for genotoxic and nongenotoxic carcinogenicity.Keywords: Antivirus, HIV, figs, molecular docking, toxicity
Employing Ensemble Protein-Ligand Interaction Fingerprints to Mimic Induced-Fit Theory in Structure-Based Virtual Screening Targeting Dipeptidyl Peptidase IV Enade Perdana Istyastono; Bonifacius Ivan Wiranata; Florentinus D.O. Riswanto; Fransiska Kurniawan; Tasia Amelia; Nunung Yuniarti; Eko Adi Prasetyanto
Journal of Pharmaceutical Sciences and Community Vol. 23 No. 1 (2026)
Publisher : Sanata Dharma University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24071/jpsc.v23i1.1070

Abstract

We have successfully employed PyPLIF HIPPOS in retrospective Structure-Based Virtual Screening (SBVS) campaigns targeting some G-protein coupled receptors (GPCRs), which could pinpoint the molecular determinants of the protein-ligand bindings and increase the quality of the SBVS protocols. We were then tempted to append with molecular dynamics simulations using YASARA-Structure to mimic the induced-fit theory in the construction of SBVS protocols targeting dipeptidyl peptidase IV (DPP4). The protocol was retrospectively validated by employing the DPP4 ligands and decoys provided by the Directory of Useful Decoys: Enhanced (DUDE). The best SBVS protocol from this research has the balanced accuracy (BA) value of 0.836, which could be used further in prospective screening campaigns. 
Co-Authors Bonifacius Ivan Wiranata Eko Adi Prasetyanto Enade Perdana Istyastono Florentinus D.O. Riswanto Fransiska Kurniawan Khonsa Khonsa Nunung Yuniarti Sophi Damayanti