在细胞癌的癌症驱动因素的蛋白质蛋白相互作用的计算分析
Jimin Pei1,2,3, Jing Zhang1,2,3, Qian Cong1,2,3
1Eugene McDermott Center for Human Growth and Development, University of Texas Southwestern Medical Center, Dallas, TX, USA.
FEBS open bio
|November 15, 2023
概括
深度学习预测了癌驱动因素的蛋白质相互作用,揭示了突变对蛋白质结合的影响. 这些发现突显了细胞癌 (RCC) 发病过程中的关键突变,并提出了新的治疗点.
科学领域:
- 在瘤学瘤学.
- 结构生物学 结构生物学
- 计算生物学 计算生物学
背景情况:
- 细胞癌 (RCC) 是最常见的癌,发病率越来越高.
- RCC的发展与各种癌症驱动蛋白有关,这些蛋白质通常由瘤抑制基因编码.
- 这些驱动因素参与了关键的细胞过程,如蛋白质降解,染色质重塑和转录.
研究的目的:
- 使用深度学习预测RCC驱动蛋白的蛋白质与蛋白质相互作用 (PPI).
- 获得对RCC驱动复合体的结构洞察力,并确定潜在的药物点.
- 分析癌症体质突变对涉及RCC驱动器的PPI的影响.
主要方法:
- 利用包括AlphaFold在内的深度学习模型来预测蛋白质与蛋白质相互作用 (PPI).
- 预测涉及关键RCC驱动因素的高可信度蛋白质复合体 (例如,TCEB1,KMT2C/D,KDM6A,TSC1,TRRAP).
- 将RCC基因组测序数据的癌症体质误解突变映射到预测和实验PPI接口.
主要成果:
- 为多个RCC驱动器的复合体生成了高可靠性的结构预测.
- 确定了特定的相互作用接口,如NRF2-MAFK,作为潜在的药物设计目标.
- 观察到100多种癌症体质突变,影响了涉及VHL和TCEB的复合体的结合亲和力1.1.
结论:
- 预测的PPI提供了对RCC病原性有价值的结构性见解.
- 影响驱动蛋白复合体结合亲和力的突变在RCC中显著.
- 这些发现可能为细胞癌的新型向疗法铺平道路.
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