JanusDDG:一个基于物理的神经网络,通过双前线的注意力来实现基于序列的蛋白质稳定
Guido Barducci1, Ivan Rossi2, Francesco Codicé2
1AI and Computational Biomedicine Unit, Department of Medical Sciences, University of Turin, Turin, Italy. guido.barducci@unito.it.
Communications biology
|February 3, 2026
概括
JanusDDG是一个新的基于物理学的模型,它可以准确地预测蛋白质稳定性的变化. 这通过将热力学与深度学习相结合,推进了蛋白质设计和疾病突变影响评估.
科学领域:
- 计算生物学 计算生物学
- 蛋白质工程是指蛋白质的工程.
- 生物物理学的生物物理.
背景情况:
- 预测突变导致的蛋白质稳定性变化对于蛋白质设计和了解疾病机制至关重要.
- 蛋白质语言模型 (PLM) 已经改进了计算预测,但很难遵守热力学定律.
- 基于序列的模型在平衡准确性与基本热力学原理方面面临挑战.
研究的目的:
- 开发一种新的基于物理学的神经网络,用于预测蛋白质稳定性的变化.
- 使用序列数据准确预测单个和多个残留物突变的稳定性变化.
- 确保预测满足热力学定律,同时保持高准确度.
主要方法:
- 开发了JanusDDG,一个基于物理的神经网络,集成了PLM嵌入和交叉注意力变压器.
- 采用了基于物理的范式来限制模型的热力学原理 (反对称性,过渡性).
- 利用交叉交叉的注意力机制来分析野生类型和突变序列的嵌入.
主要成果:
- JanusDDG在预测蛋白质稳定性从序列变化方面取得了最先进的性能.
- 该模型在单个和多个残留物突变中都具有很高的准确性.
- JanusDDG的性能与仅使用序列信息的基于结构的方法相匹配或超越.
结论:
- JanusDDG提供了一种强大的基于序列的方法,用于预测蛋白质稳定性的突变影响.
- 基于物理学的设计确保了预测中的热力学一致性.
- 这种方法促进了合理的蛋白质设计和疾病相关突变的评估.
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