UJI BIOINFORMATIC MODELING SENYAWA AKTIF BIJI MELINJO (Gnetum gnemon L.) PADA PROTEIN KANKER SERVIKS DAN SARS COV-2

Introduction: Cervical cancer cases in Indonesia are ranked second with the results ofdata in 2020 of 36,633 cases. During the COVID-19 pandemic, cancer patients aresusceptible to being infected by COVID-19. Study data from China, February 2020, therewere 107 cancer patients infected with COVID-19. It should get more attention becauseweakened immune form cancer patients. The purpose of this study was to determine thepotential of active compounds from melinjo seeds in inhibiting cervical cancer proteins andSARS CoV-2. Methods: The method used is bioinformatics test (STITCH -STRING) and moleculardocking test using Autodock Vina. The results will be seen from the lowest docking score.Result: The results of the bioinformatics test were obtained in the form of target proteinsfor cervical cancer regulators, namely MYC and TP53 (Resveratrol), FN1 and MAPK1(Gnetin C), SERPINE1 and VEGFA (Oleic Acid). The results from the analysis of thebinding affinity a target protein obtained the best binding affinity for cancer proteins, namelyGnetin C with FN1 with a docking score of -10.8 kcal/mol. The best affinity for SARS CoV2 protein after Remdesivir was for Resveratrol and Nsp3 with a docking score of -7.4kcal/mol. Conclusion: The results showed that the active compounds Resveratrol and Gnetin C hadgood potential in inhibiting cervical cancer protein and SARS CoV-2 with a lower dockingscore compared to the comparison drug Keywords: Gnetum gnemon L, bioinformatic modeling, cervical cancer, SARS CoV-2Pendahuluan: Kasus kanker serviks di Indonesia menempati peringkat kedua padapenyakit kanker dengan data tahun 2020 sebesar 36.633 kasus. Pada kondisi pandemiCOVID-19, pasien kanker rentan terinfeksi. Data studi dari Cina, 11 Februari 2020 terdapat107 pasien kanker yang terinfeksi COVID-19 per 72.314 kasus. Hal ini menjadi perhatiankhusus karena lemahnya imunitas pasien kanker yang disebabkan oleh efek sampingkemoterapi.Tujuan penelitian ini untuk mengetahui potensi senyawa aktif biji melinjo dalammenghambat protein kanker serviks dan SARS CoV-2. Metode: Metode penelitian yang digunakan yaitu bioinformatika dan molecular docking.Uji bioinformatika terhadap senyawa uji dilakukan menggunakan STITCH dan STRING.Lalu, uji molecular docking menggunakan Autodock Vina dengan melihat docking scoreterendah. Docking protein target kanker serviks menggunakan pembanding Paclitaxel.Sedangkan pada SARS CoV-2 menggunakan pembanding Favipiravir dan Remdesivir. Hasil: Diperoleh hasil uji bioinformatika berupa protein target kanker serviks yaitu MYCdan TP53 (Resveratrol), FN1 dan MAPK1 (Gnetin C), SERPINE1 dan VEGFA (AsamOleat). Hasil analisis afinitas ikatan protein target tersebut menggunakan moleculardocking didapatkan hasil afinitas ikatan terbaik pada protein kanker yaitu senyawa GnetinC dengan protein FN1 dengan docking score sebesar -10,8 kkal/mol. Sedangkan padaprotein SARS CoV-2 afinitas terbaik setelah obat Remdesivir ialah pada senyawaResveratrol dan Nsp3 dengan docking score sebesar -7,4 kkal/mol. Kesimpulan: Dapat disimpulkan hasil penelitian menunjukkan bahwa senyawa aktifResveratrol dan Gnetin C memiliki potensi yang baik dalam menghambat protein kankerserviks dan SARS CoV-2 dengan docking score yang lebih rendah dibandingkan denganobat pembandingnya. Kata kunci: Gnetum gnemon L., Bioinformatic modelling, kanker serviks, SARS CoV-

Analysis of Protein Interaction Networks for the Detection of Candidate Hepatitis B and C Biomarkers

Hepatitis B virus (HBV) and hepatitis C virus (HCV) infection are the major causes of chronic liver disease, cirrhosis and hepatocellular carcinoma (HCC). The resolution or chronicity of acute infection is dependent on a complex interplay between virus and innate/adaptive immunity. The mechanisms that lead a significant proportion of patients to more severe liver disease are not clearly defined and involve virus induced host gene/protein alterations. The utilization of protein interaction networks (PINs) is expected to identify novel aspects of the disease concerning the patients’ immune response to virus as well as the main pathways that are involved in the development of fibrosis and HCC. In this study, we designed several PINs for HBV and HCV and employed topological, modular, and functional analysis techniques in order to determine significant network nodes that correspond to prominent candidate biomarkers. The networks were built using data from various interaction databases. When the overall PINs of HBV and HCV were compared, 48 nodes were found in common. The implementation of a statistical ranking procedure indicated that three of them are of higher importance