0% Complete
the 4th international edition and 13th Iranian Conference on Bioinformatics
international edition and Iranian Conference on Bioinformatics
صفحه اصلی
/
4th international edition and 13th Iranian Conference on Bioinformatics
Designing Anti-Cancer Peptides Using Bioinformatics to Inhibit Survivin, Disrupt Cell Division, and Trigger Apoptosis in Cancer Cells
نویسندگان :
Seyedeh Fatemeh Ahmadi
1
Hamidreza Samadikhah
2
Seyed Shahriar Arab
3
1- Department of Biology, Master’s Student in Genetics, Central Tehran Branch, Islamic Azad University, Tehran, Iran
2- Department of Biology, Faculty of Sciences, Central Tehran Branch, Islamic Azad University, Tehran, Iran
3- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA
کلمات کلیدی :
Survivin،Anti-cancer peptide،Molecular dynamics (MD) Simulations،Molecular docking
چکیده :
Abstract: Background: Cancer is a prevalent disease that, despite ongoing research and advancements in treatment and early diagnosis, continues to cause significant mortality worldwide. Research into the characteristics of cancer cells and their differences from normal cells, particularly through proteomic studies, plays a crucial role in cancer prevention, early diagnosis, and treatment. Recently, the Survivin protein has been studied as a key diagnostic marker in autoimmune diseases such as rheumatoid arthritis, as well as in various types of cancer (Wang and Greene, 2024, Zafari et al., 2019). Survivin is highly expressed in multiple human cancers and belongs to the inhibitors of apoptosis proteins (IAPs) family (Fang et al., 2024). As the smallest member of the inhibitors of apoptosis proteins (IAP) family, it plays a vital role in inhibiting apoptosis and regulating the cell cycle in cancer cells. Survivin is a key component of the chromosomal passenger complex (CPC) in the nucleus, playing a multifaceted role in the cell cycle by regulating mitosis and cytokinesis. In the cytoplasm, Survivin binds to an X-linked inhibitor of apoptosis protein (XIAP), leading to increased stability of XIAP, and interactively inhibits apoptosis by inhibiting caspase-9 activity (Singh et al., 2019). Another mechanism by which Survivin inhibits apoptosis is inactivation of the Smac/DIABLO factor (Fang et al., 2020). In this study, anti-cancer peptides were designed to target the functional region of Survivin, aiming to disrupt its interactions with other proteins, induce apoptosis, and arrest the cell cycle in cancer cells (Martínez-García et al., 2019). Methods: Anti-cancer peptides were designed based on the chromosomal passenger complex proteins (CPC). The anti-cancer peptides and their efficacy were evaluated using bioinformatics methods, including molecular docking (Alekseenko et al., 2020) and molecular dynamics (MD) simulations. Molecular dynamics simulations were performed for the complex of Survivin and the anti-cancer peptides using GROMACS software (version 2023) with the CHARMM36 force field (Ghavamipour et al., 2014, Kumar and Yaduvanshi, 2023), allowing atoms and molecules to interact over a 100 ns timeframe. Subsequently, GROMACS analyses, including root mean square deviation (RMSD) and radius of gyration (Rg), as well as GROMACS protein-ligand interaction energy (Hollingsworth and Dror, 2018) and gmx_MMPBSA (Valdés-Tresanco et al., 2021) analyses, were conducted to assess the stability and binding affinity of the systems. Results: Among the anti-cancer peptides analyzed for efficiency, the anti-cancer peptide (P2) exhibited the highest binding affinity compared to the native peptide, as determined by interaction energy analysis using GROMACS and gmx_MMPBSA software. Furthermore, root mean square deviation (RMSD) and radius of gyration (Rg) analyses demonstrated the stability of the systems throughout the molecular dynamics simulations. Conclusion: The anti-cancer peptide (P2), demonstrating a high binding affinity for Survivin, effectively reduces cell proliferation and induces apoptosis in cancer cells. These findings highlight P2 as a promising therapeutic candidate for treating human cancers. (Vadevoo et al., 2023).
لیست مقالات
لیست مقالات بایگانی شده
Discovering Moonlighting Proteins with AI and Explainability
Masoud Mahdavifar - Milad Besharatifard - Fateme Zaremirakabad
Designing Anti-Cancer Peptides Using Bioinformatics to Inhibit Survivin, Disrupt Cell Division, and Trigger Apoptosis in Cancer Cells
Seyedeh Fatemeh Ahmadi - Hamidreza Samadikhah - Seyed Shahriar Arab
Exploring the anti-inflammatory potential of the silymarin against IL-17A: in silico molecular docking
Tooba Abdizadeh
A Novel Approach to Antimicrobial Susceptibility Testing: Automated Disk Diffusion Analysis with Smartphone Cameras and Deep Learning Techniques
Mahdiar Mansouri - Mohammadreza Najafi Disfani - Danial Ghofrani - Arash Pournaji
Molecular Docking Study of Pyrazole Interaction with Bovine Serum Albumin (BSA): Insights from Drug-Protein Binding
Parisa Zallou - Yaghub Pazhang - Ebrahim Nemati-Kande
In-silico Drug Generation using Masked Language Modeling
Seyed Hassan Alavi - Zahra Ghorbanali - Fatemeh Zare-Mirakabad
Integrating Biological Networks and Deep Learning for Microbe-Disease Prediction
Sobhan Ahmadian Moghadam - Zahra Ghorbanali - Fatemeh Zare-Mirakabad - Hesam Montazeri
Revealing disease subtypes and heterogeneity in common variable immunodeficiency through transcriptomic analysis
Mohammad Reza Zabihi - Zahra Moradi - Nima Safari - Zahra Salehi - Kaveh Kavousi
Identification of potential RNA interference targets in bladder cancer through bioinformatics approaches
Sana Sajjadi - Mohammad ali Takhshid
Encoding the gRNA-DNA Pairs with Deep Transformers to Predict off-target Effects of CRISPR
Roghayyeh Alipanahi - Leila Safari - Alireza Khanteymoori
بیشتر
ثمین همایش، سامانه مدیریت کنفرانس ها و جشنواره ها - نگارش 44.5.0