解突变对蛋白质稳定性和功能的影响与解的蛋白质语言模型
bioRxiv : the preprint server for biology
|February 12, 2026
概括
新的深度学习框架DETANGO将蛋白质功能与稳定效应分开. 它可以识别稳定但不活跃的变体和关键的功能残留物,从而推进蛋白质工程.
科学领域:
- 分子生物学分子生物学
- 计算生物学 计算生物学
- 生物物理学的生物物理.
背景情况:
- 进化约束塑造蛋白质序列,影响其稳定性和功能.
- 蛋白质语言模型 (pLMs) 预测突变效应,但混稳定性和功能.
- 解开这些影响对于理解突变机制和蛋白质工程至关重要.
研究的目的:
- 介绍DETANGO,这是一个深度学习框架,用于解构突变对蛋白质功能的影响.
- 通过将稳定性效应与pLM预测分开来估计功能可信度得分.
- 确定稳定但不活跃 (SBI) 变体和功能关键残留物.
主要方法:
- 开发了DETANGO,这是一个使用pLM预测和稳定性数据的深度学习框架.
- 对于单点突变的估计功能可信度得分.
- 基于各种功能上下文 (配体结合,催化,全ostery) 和蛋白质家族的基准DETANGO.
主要成果:
- DETANGO准确地识别了SBI变体和功能关键残留物.
- 该框架揭示了在同类蛋白质家族中共享和独特的功能模式.
- DETANGO有效地解开了对蛋白质稳定性和功能的进化压力.
结论:
- DETANGO为分析进化约束提供了一个生物基础的框架.
- 提升了对蛋白质功能和突变效应的机械学理解.
- 提供了合理的蛋白质工程和治疗开发的信息.
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