基于异质网络的化合物-蛋白相互作用预测揭示了潜在的抗肝瘤剂
Yong-Cui Wang1, Tian-Ze Li1, Ji-Jun Chen1
1State Key Laboratory of Phytochemistry and Plant Resources in West China, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming 650201, P.R. China.
我们开发了CIPHEN,这是一种新的计算工具,用于识别天然产品 (NP) 和它们的蛋白标,用于治疗肝细胞癌 (HCC). 这种方法有助于发现新的抗癌化合物,并了解它们的作用机制.
科学领域:
- 计算生物学是一种计算生物学.
- 药物发现 药物发现
- 自然产品研究自然产品研究
背景情况:
- 肝细胞癌 (HCC) 治疗面临现有的多酶和免疫检查点抑制剂的挑战,因为目标相似性和有限的生存改善.
- 自然产品 (NP) 提供了多样化的结构和活动,代表了药物发现的宝贵资源,但它们的目标和机制往往不清楚.
研究的目的:
- 提出CIPHEN (基于异质网络的化合物-蛋白相互作用预测),一种新的计算方法.
- 预测潜在的抗肝瘤天然产品及其相应的肝细胞癌 (HCC) 蛋白标.
主要方法:
- 开发CIPHEN,一种基于异质网络的计算模型,用于预测化合物-蛋白相互作用 (CPI).
- 使用规范CPI数据库和独立测试数据集对CIPHEN的评估.
- 实验验证,以确认天然产品与抗HCC标之间的预测关系.
主要成果:
- CIPHEN在预测已知和以前未报告的化合物-蛋白相互作用 (CPI) 方面表现出强大的能力.
- 该模型成功地确定了活性抗肝瘤类二聚体 (SDs) 与与HCC相关的标之间的关系.
- 实验结果证实了CIPHEN模型的预测.
结论:
- CIPHEN提供了一个有前途的计算策略,用于研究天然产品的作用模式.
- 这种方法可以加速肝细胞癌 (HCC) 的药物研发过程.
- CIPHEN促进了新型抗HCC天然产品及其分子标的发现.
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