A Machine-Learning Protocol for Ultraviolet Protein-Backbone Absorption Spectroscopy under Environmental Fluctuations
A Machine-Learning Protocol for Ultraviolet Protein-Backbone Absorption Spectroscopy under Environmental Fluctuations
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DOI：
10.1021/acs.jpcb.1c03296
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发表时间：
2021-06-04
影响因子：
3.3
通讯作者：
Jiang，Jun
中科院分区：
文献类型：
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作者：
Zhang，Jinxiao;Ye，Sheng;Jiang，Jun
紫外（UV）吸收光谱通常用于表征蛋白质的整体结构。然而，紫外光谱的理论解释受到跨越巨大构象空间的大量昂贵的激发态从头计算的阻碍。我们提出了一种用于蛋白质远紫外（FUV）光谱的机器学习（ML）协议，它可以以与密度泛函理论（DFT）计算相当的精度预测蛋白质的远紫外（FUV）光谱，但计算成本降低了3-4个数量级。它进一步显示了出色的预测能力和可转移性，可用于探测结构突变和蛋白质折叠途径。
Ultraviolet (UV) absorption spectra are commonly used for characterizing the global structure of proteins. However, the theoretical interpretation of UV spectra is hindered by the large number of required expensive ab initio calculations of excited states spanning a huge conformation space. We present a machine-learning (ML) protocol for far-UV (FUV) spectra of proteins, which can predict FUV spectra of proteins with comparable accuracy to density functional theory (DFT) calculations but with 3–4 orders of magnitude reduced computational cost. It further shows excellent predictive power and transferability that can be used to probe structural mutations and protein folding pathways.