ESMDynamic:从单个序列中快速准确地预测蛋白质动态接触图
Diego E Kleiman1, Jiangyan Feng2, Zhengyuan Xue1
1Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA.
bioRxiv : the preprint server for biology
|September 2, 2025
概括
通过ESMDynamic,一种新的深度学习模型,可以从序列中预测蛋白质的结构动态,其性能优于现有的方法. 它可以对蛋白质灵活性进行更快的基于序列的分析,用于工程和发现.
科学领域:
- 结构生物学
- 计算生物学
- 生物物理
背景情况:
- 了解蛋白质结构动态对于功能至关重要,但难以预测.
- 目前的深度学习模型通常预测静态的蛋白质结构,而忽视动态的行为.
研究的目的:
- 引入ESMDynamic,这是一个新的深度学习模型,用于直接从蛋白质序列预测动态残留物接触概率图.
- 为了使蛋白质结构可变性的基于序列的预测,而无需多次序列对齐.
主要方法:
- 在ESMDynamic的基础上建立了ESMFold架构.
- 该模型是从实验结构组合和分子动力学 (MD) 模拟中训练的接触波动.
- 在mdCATH和ATLAS数据集上与最先进的整体预测模型进行比较.
主要成果:
- 在预测短暂接触方面,ESMDynamic与现有的模型相匹配或超越.
- 与其他方法相比,实现了数量级更快的推断速度.
- 证明成功应用于传送器,设计蛋白质和病毒蛋白质模块,恢复验证的动态接触.
结论:
- ESMDynamic提供了一个快速的,可解释的,基于序列的方法来表征蛋白质结构动力学.
- 该模型可以从MD模拟中构建动力模型.
- 广泛的应用包括蛋白质工程,功能分析和模拟引导的发现.
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