Mochammad Aqilah Herdiansyah, Arif Nur Muhammad Ansori, Viol Dhea Kharisma, Mochamad Radika Tory Alifiansyah, Dhea Anggraini, Qiara Amelia Putri Priyono, Putri Antika Yusniasari, Amelia Julia Tria Fetty, Rahadian Zainul, Maksim Rebezov, Evgeniy Kolesnik, Nikolai Maksimiuk
Breast cancer is a chronic health problem that causes 690,000 deaths worldwide. The development of secondary metabolite compounds from natural preparations through an in silico approach is needed as a predictive tool to prevent breast cancer, one of them is cladosporol from Cladosporium spp. This study aims to utilize an in silico approach to predict the potential of cladosporol against alpha-estrogen receptors. The alpha-estrogen receptor with code 6CBZ was selected based on group function as pharmacophore in ligand-receptor interaction. The methods used in this study are by using an in silico approach with Molegro Virtual Docker (MVD) Ver 5.5 for the docking process and CABS-flex 2.0 for identifying the stability of the complexes. ADMET properties analysis was also performed to know the pharmacokinetics attributes of cladosporol. Based on research conducted, stated that cladosporol octanoate has the lowest rerank score with a-84.3593 value and the RMSD value is 1.195 Å so it’s valid for molecular docking. Exploration of cladosporol for anti-breast cancer from Cladosporium spp fungi can be a novelty for the development of future pharmaceutical research. Thus, the development of anti-cancer drugs for early prevention can be carried out to reduce the number of breast cancer cases worldwide. © 2024, Universitas Negeri Semarang. All rights reserved.
Department of Biology, Faculty of Science and Technology, Universitas Airlangga, Surabaya, Indonesia; Postgraduate School, Universitas Airlangga, Surabaya, Indonesia; Uttaranchal Institute of Pharmaceutical Sciences, Uttaranchal University, Dehradun, India; European Virus Bioinformatics Center, Jena, Germany; Division of Research and Development, Jalan Tengah, Surabaya, Indonesia; Faculty of Pharmacy, Universitas Airlangga, Surabaya, Indonesia; Department of Chemistry, Faculty of Science and Technology, Universitas Airlangga, Surabaya, Indonesia; Department of Chemistry, Faculty of Mathematics and Natural Sciences, Universitas Negeri Padang, Padang, Indonesia; Center for Advanced Material Processing, Artificial Intelligence and Biophysics Informatics (CAMPBIOTICS), Universitas Negeri Padang, Padang, Indonesia; Department of Scientific Research, V. M. Gorbatov Federal Research Center for Food Systems, Moscow, Russian Federation; Faculty of Biotechnology and Food Engineering, Ural State Agrarian University, Yekaterinburg, Russian Federation; Russian State Agrarian University – Moscow Agricultural Academy named after K.A. Timiryazev, Moscow, Russian Federation; Institute of Medical Education, Yaroslav-the-Wise Novgorod State University, Velikiy Novgorod, Russian Federation
