整合深度学习以实现翻译后修改,对Hsp90和药物结合的交叉沟通
Jennifer A Heritz1, Katherine A Meluni1, Sarah J Backe2
1Department of Urology, SUNY Upstate Medical University, Syracuse, New York, USA; Department of Biochemistry and Molecular Biology, SUNY Upstate Medical University, Syracuse, New York, USA; Upstate Cancer Center, SUNY Upstate Medical University, Syracuse, New York, USA.
在人体细胞中删除HDAC3和HDAC8增强了与ATP和Ganetespib结合的热冲击蛋白-90 (Hsp90). 这揭示了一个常见的翻译后修改 (PTM) 签名,并突出了AI.
科学领域:
- 生物化学和分子生物学
- 蛋白质组学是指蛋白质组学.
- 癌症生物学 癌症生物学
背景情况:
- 翻译后修改 (PTMs) 增加了蛋白质组的复杂性,并调节了细胞蛋白质稳定.
- 热冲击蛋白-90 (Hsp90) 是一个关键的分子伴侣,参与蛋白质稳定和癌症信号传递,使其成为治疗点.
- PTM交叉对蛋白质的功能多样性有着显著的贡献.
研究的目的:
- 研究基因组脱乙酶3 (HDAC3) 和基因组脱乙酶8 (HDAC8) 删除对Hsp90 PTM和药物结合的影响.
- 用实验和计算方法解读Hsp90上的PTM交叉声.
- 评估深度学习人工智能模型在预测PTM交叉通话方面的有效性.
主要方法:
- 使用了人类细胞系,删除了HDAC3和HDAC8.
- 通过质谱学分析了Hsp90与ATP的结合以及抑制剂Ganetespib.
- 采用深度学习人工智能 (AI) 预测模型进行PTM分析.
主要成果:
- 删除HDAC3和HDAC8增加了Hsp90与ATP和Ganetespib的结合.
- 来自淘汰赛细胞的Hsp90在与Ganetespib.结合时显示出类似的酸化和乙化PTM.
- 从两个淘汰赛细胞类型的Hsp90中观察到一种共同的蛋白质网络特征.
- 人工智能模型的预测与质谱数据对齐,用于破译PTM交叉通话.
结论:
- HDAC3和HDAC8在调节Hsp90 PTM和药物相互作用方面发挥着作用.
- 深度学习的人工智能模型提供了一种有效和快速的方法来破译像Hsp90.0.这样的复杂蛋白质上的PTM交叉声.
- 了解Hsp90 PTM交叉声可以为癌症治疗策略提供信息.
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