Molecular Modeling Studies of Natural Inhibitors of Androgen Signaling in Prostate Cancer.

IF 2.4 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Cancer Informatics Pub Date : 2022-08-13 eCollection Date: 2022-01-01 DOI:10.1177/11769351221118556
Samuel O Olubode, Mutolib O Bankole, Precious A Akinnusi, Olayinka S Adanlawo, Kehinde I Ojubola, Daniel O Nwankwo, Onome E Edjebah, Ayomide O Adebesin, Abigail O Ayodele
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引用次数: 7

Abstract

Prostate cancer is the second most common disease in men and the sixth leading cause of death from cancer globally, with 20 million men expected to be affected by 2024 thus considered as chronic illness which requires immediate attention. As an androgen-dependent illness that relies on the androgen receptor for development and progression, inhibition of the androgen receptor can lead to a therapeutic solution, hence serving as a vital therapeutic target. This study focused on the computational analysis of the inhibitory potentials of Vitis vinifera, a reported plant with anti-cancer properties, against androgen receptor employing molecular docking, ADMET studies, Binding energy study, pharmacophore modeling, and molecular dynamics simulation approaches. After the investigation, it was determined that 5 compounds: cis-piceid, cis-astrigin, gallocatechin, phlorizin, and trans-polydatin, might be possible androgen receptor inhibitors since they had higher docking scores and ADMET qualities than compared standards, with cis-piceid being the best-predicted inhibitor.

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Abstract Image

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前列腺癌中雄激素信号天然抑制剂的分子模拟研究。
前列腺癌是男性中第二大常见疾病,也是全球癌症死亡的第六大原因,预计到2024年将有2000万男性受到影响,因此被视为需要立即关注的慢性病。作为一种雄激素依赖性疾病,雄激素受体的发展和进展依赖于雄激素受体,抑制雄激素受体可以获得治疗方案,因此是一个重要的治疗靶点。本研究采用分子对接、ADMET研究、结合能研究、药效基团建模和分子动力学模拟等方法,对抗癌植物葡萄(Vitis vinifera)对雄激素受体的抑制电位进行了计算分析。经研究,确定5种化合物:顺式piceid、顺式astrigin、没食子儿茶素、phlorizin和trans-polydatin可能是雄激素受体抑制剂,因为它们的对接评分和ADMET质量高于比较标准,顺式piceid是预测最好的抑制剂。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Cancer Informatics
Cancer Informatics Medicine-Oncology
CiteScore
3.00
自引率
5.00%
发文量
30
审稿时长
8 weeks
期刊介绍: The field of cancer research relies on advances in many other disciplines, including omics technology, mass spectrometry, radio imaging, computer science, and biostatistics. Cancer Informatics provides open access to peer-reviewed high-quality manuscripts reporting bioinformatics analysis of molecular genetics and/or clinical data pertaining to cancer, emphasizing the use of machine learning, artificial intelligence, statistical algorithms, advanced imaging techniques, data visualization, and high-throughput technologies. As the leading journal dedicated exclusively to the report of the use of computational methods in cancer research and practice, Cancer Informatics leverages methodological improvements in systems biology, genomics, proteomics, metabolomics, and molecular biochemistry into the fields of cancer detection, treatment, classification, risk-prediction, prevention, outcome, and modeling.
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