通过将蛋白质语言模型表示与结构上下文集成,挖掘氨酸翻译后修改站点.
Mengqi Luo1, Xiaohong Zhu2, Chen Bai2
1Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China.
概括
我们开发了一个深度学习框架,通过整合蛋白序列和结构数据来识别氨酸翻译后修饰 (PTM) 位点. 这种计算工具有助于理解蛋白质的调节和功能.
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
- 结构生物学 结构生物学
背景情况:
- 氨酸 (Lys/K) 残留物对于后翻译性修饰 (PTMs) 是至关重要的,因为它们具有多功能的e-氨基群,调节各种细胞功能.
- 在计算上识别修改后的 lysine 站点需要集成序列和结构数据的模型,最大限度地减少对域特定特征工程的需求.
研究的目的:
- 提出一个统一的深度学习框架,用于识别 lysine PTM 站点.
- 通过共享的建模策略,在多种 lysine PTM 类型中实现一致的应用.
- 将蛋白质语言模型中的序列表示与原子级3D结构特征集成.
主要方法:
- 开发了一个深度学习框架,整合了蛋白质语言模型衍生的序列表示和原子级的3D结构特征.
- 应用了一个共享的建模策略,在各种 lysine PTM 类型中进行一致的预测.
- 利用全原子分子动力学模拟来评估预测PTM位点在人类C型学菌素域家族12成员A (hCLEC12A) 上的功能相关性.
主要成果:
- 深度学习框架成功地在hCLEC12A.上确定了潜在的PTM站点.
- 分子动力学模拟显示,预测的氨酸残留物影响hCLEC12A-抗体50C1复合物的稳定性和结合.
- 该模型证明了对多种 lysine PTM 类型的一致适用性.
结论:
- 介绍了一种集成计算框架,用于高效的lysine PTM站点挖掘和功能分析.
- 该框架有效地结合了PTM预测的序列和结构信息.
- 预测的氨酸修饰在蛋白质复合物的稳定性和结合相互作用中起着重要作用.
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