Dynamic protein structures are crucial for deciphering their diverse biological functions. Two-dimensional infrared (2DIR) spectroscopy stands as an ideal tool for tracing rapid conformational evolutions in proteins. However, linking spectral characteristics to dynamic structures poses a formidable challenge. Here, we present a pretrained machine learning model based on 2DIR spectra analysis. This model has learned signal features from approximately 204,300 spectra to establish a “spectrum-structure” correlation, thereby tracing the dynamic conformations of proteins. It excels in accurately predicting the dynamic content changes of various secondary structures and demonstrates universal transferability on real folding trajectories spanning timescales from microseconds to milliseconds. Beyond exceptional predictive performance, the model offers attention-based spectral explanations of dynamic conformational changes. Our 2DIR-based pretrained model is anticipated to provide unique insights into the dynamic structural information of proteins in their native environments.

中文翻译：

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使用预训练的机器学习模型通过二维红外光谱揭示动态蛋白质结构


动态蛋白质结构对于破译其多样化的生物学功能至关重要。二维红外 (2DIR) 光谱是追踪蛋白质快速构象演化的理想工具。然而，将光谱特征与动态结构联系起来是一个巨大的挑战。在这里，我们提出了一种基于 2DIR 光谱分析的预训练机器学习模型。该模型从大约 204,300 个光谱中学习了信号特征，建立了“光谱-结构”相关性，从而追踪蛋白质的动态构象。它擅长准确预测各种二级结构的动态内容变化，并展示了跨微秒到毫秒时间尺度的真实折叠轨迹的通用可转移性。除了出色的预测性能之外，该模型还提供了基于注意力的动态构象变化的光谱解释。我们基于 2DIR 的预训练模型预计将为蛋白质在其天然环境中的动态结构信息提供独特的见解。