Machine learning reveals hidden stability code in protein native fluorescence.
Machine learning reveals hidden stability code in protein native fluorescence.
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DOI：
10.1016/j.csbj.2021.04.047
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发表时间：
2021
影响因子：
6
通讯作者：
Dalby PA
中科院分区：
文献类型：
--
作者：
Zhang H;Yang Y;Zhang C;Farid SS;Dalby PA
蛋白质的构象稳定性通常通过光谱测量解折叠熔化温度来获得。然而，在天然条件下的光谱被认为包含太少的分辨率探测蛋白质的稳定性。在这里，我们已经建立并训练了一个神经网络模型，以仅在天然条件下的固有荧光发射的温度依赖性作为输入，然后预测在解折叠过渡和变性状态下的光谱。应用于治疗性抗体片段表明，从预测的光谱获得的热转变与实验测量的热转变高度相关。至关重要的是，这项工作揭示了温度依赖性的本地荧光光谱包含了高度的先前隐藏的信息有关本地合奏功能的稳定性。这可能导致仅在非变性温度下基于光谱测量快速筛选治疗性蛋白质变体和制剂。
Conformational stability of a protein is usually obtained by spectroscopically measuring the unfolding melting temperature. However, optical spectra under native conditions are considered to contain too little resolution to probe protein stability. Here, we have built and trained a neural network model to take the temperature-dependence of intrinsic fluorescence emission under native-only conditions as inputs, and then predict the spectra at the unfolding transition and denatured state. Application to a therapeutic antibody fragment demonstrates that thermal transitions obtained from the predicted spectra correlate highly with those measured experimentally. Crucially, this work reveals that the temperature-dependence of native fluorescence spectra contains a high-degree of previously hidden information relating native ensemble features to stability. This could lead to rapid screening of therapeutic protein variants and formulations based on spectroscopic measurements under non-denaturing temperatures only.
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影响因子：
4.6
作者：
Kunz P;Zinner K;Mücke N;Bartoschik T;Muyldermans S;Hoheisel JD
通讯作者：
Hoheisel JD
影响因子：
2.7
作者：
Hebditch, Max;Warwicker, Jim
通讯作者：
Warwicker, Jim
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3.3
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影响因子：
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作者：
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通讯作者：
Dalby, Paul A.
影响因子：
2.9
作者：
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通讯作者：
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