通过深度学习揭示了病原体的基本和与毒性相关的蛋白质相互作用
bioRxiv : the preprint server for biology
|April 22, 2024
概括
我们开发了一个深度学习管道来识别细菌蛋白相互作用,发现了数百个涉及必需基因和毒性因素的新复合体. 这有助于理解病原体生物学,并开发新的传染病治疗方法.
科学领域:
- 微生物学 微生物学
- 结构生物学 结构生物学
- 计算生物学 计算生物学
背景情况:
- 了解细菌蛋白与蛋白相互作用 (PPI) 对于破译病原性机制和开发传染病新疗法至关重要.
- 预测蛋白质复合体的结构可以提供关于它们在细菌病原体中的功能和相互作用的见解.
研究的目的:
- 开发和应用基于深度学习的管道,以系统地识别和结构性表征细菌PPI,以全蛋白质组的规模.
- 识别涉及至关重要的细胞过程和人类细菌病原体中毒性的新型蛋白质复合体.
主要方法:
- 采用了深度学习管道,整合了残留物-残留物共进化数据和蛋白质结构预测.
- 该管道用于分析19种人类细菌病原体中的7800万对蛋白质.
- 预测的PPI进一步分析了对基本基因和毒性因素的参与.
主要成果:
- 该管道确定了1923个自信地预测了涉及基本基因的PPI复合体和256个涉及毒性因子的PPI复合体.
- 许多预测的复合物都是新鲜的,实验验证证证了12个测试预测中的6个.
- 确定的相互作用跨越了关键途径,包括转录后修饰,酸中和外膜传输.
结论:
- 开发的深度学习管道对于大规模识别和细菌PPI的结构特征是有效的.
- 这些发现为细菌病原体的分子机制提供了宝贵的见解,并确定了药物开发的潜在目标.
- 这项工作有助于更深入地了解细菌生物学和设计新的抗感染策略.
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