将机器学习与基于结构的蛋白质设计结合起来,以预测和设计蛋白质的翻译后修改
Moritz Ertelt1,2, Vikram Khipple Mulligan3, Jack B Maguire4
1Institute for Drug Discovery, Leipzig University Medical Faculty, Leipzig, Germany.
PLoS computational biology
|March 14, 2024
概括
这项研究引入了一种使用人工神经网络和Rosetta的新计算方法,用于预测和设计蛋白质后翻译修饰 (PTM). 这种方法可以精确地控制PTM,以提高蛋白质工程和治疗开发.
科学领域:
- 生物化学和分子生物学
- 计算生物学和生物信息学
- 蛋白质工程是指蛋白质工程.
背景情况:
- 翻译后修改 (PTMs) 显著影响蛋白质的功能,稳定性和相互作用,为蛋白质工程提供了巨大的潜力.
- 已知有400多种PTM,为实现蛋白质设计的多样化提供了挑战和机遇,超出了基因编码的氨基酸.
研究的目的:
- 开发用于预测和设计特定蛋白质翻译后修饰 (PTM) 的计算工具.
- 将人工神经网络 (ANN) 预测与罗塞塔蛋白质建模套件集成为基于结构的PTM设计.
- 为了实现治疗应用的蛋白质中新型PTM的合理修改和引入.
主要方法:
- 训练有素的ANN可以预测18种常见的PTM,包括糖化,化,甲基化和脱化.
- 将ANN模型集成到罗塞塔计算蛋白质建模套件中.
- 开发了一个设计协议,以控制特定地点特定PTM的概率.
主要成果:
- 成功地将ANN预测与Rosetta结合起来,用于基于结构的PTM设计.
- 证明了修改现有PTM并引入新型PTM的能力.
- 验证了应用的潜力,例如表位膜掩盖和增强蛋白质稳定性.
结论:
- 开发的计算方法为在罗塞塔蛋白质工程框架内合理设计PTM提供了新的工具.
- 这种方法可以通过精确控制PTM来调节它们的特性,从而显著推进蛋白疗法设计.
- 这项工作扩大了蛋白质工程工具箱,为各种生物应用提供了量身定制的修改.
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