Deep Learning Enables Automatic Correction of Experimental HDX-MS Data with Applications in Protein Modeling
Deep Learning Enables Automatic Correction of Experimental HDX-MS Data with Applications in Protein Modeling
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DOI：
10.1021/jasms.3c00285
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发表时间：
2024-01-23
影响因子：
3.2
通讯作者：
Borysik，Antoni J.
中科院分区：
文献类型：
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作者：
Salmas，Ramin E.;Borysik，Antoni J.
在氢-氚交换质谱仪(HDX-MS)中，由于前向和后向交换，所观察到的与掺氢相关的质量漂移经常偏离初始信号。在典型的HDX-MS实验中，这些差异对数据解释的影响通常很小，因为研究的是相对质量变化，而不是绝对质量变化。然而，对于包括优化在内的更高级的数据处理，为了获得准确的结果，实验误差修正是必不可少的。这里展示了使用深度神经网络生成的模型进行HDX-MS数据自动校正的潜力。多层感知器(MLP)用于学习未校正的HDX-MS数据和质量位移经过反向和向前交换校正的数据之间的映射。该模型在不同水平上进行了严格的测试，包括多肽水平的质量变化、优化后的残基水平保护因子，以及使用HDX-MS引导的蛋白质建模正确识别天然蛋白质折叠的能力。人工智能在修正HDX-MS数据和提高所有级别的保真度方面显示出相当大的潜力。通过访问大数据，在线工具最终可能能够预测HDX-MS图谱中修正后的质量位移。这应该会提高需要报告实际大规模更改的工作流中的吞吐量，并允许对历史配置文件进行追溯更正，以促进使用这些数据进行新的发现。
Observed mass shifts associated with deuterium incorporation in hydrogen–deuterium exchange mass spectrometry (HDX-MS) frequently deviate from the initial signals due to back and forward exchange. In typical HDX-MS experiments, the impact of these disparities on data interpretation is generally low because relative and not absolute mass changes are investigated. However, for more advanced data processing including optimization, experimental error correction is imperative for accurate results. Here the potential for automatic HDX-MS data correction using models generated by deep neural networks is demonstrated. A multilayer perceptron (MLP) is used to learn a mapping between uncorrected HDX-MS data and data with mass shifts corrected for back and forward exchange. The model is rigorously tested at various levels including peptide level mass changes, residue level protection factors following optimization, and ability to correctly identify native protein folds using HDX-MS guided protein modeling. AI is shown to demonstrate considerable potential for amending HDX-MS data and improving fidelity across all levels. With access to big data, online tools may eventually be able to predict corrected mass shifts in HDX-MS profiles. This should improve throughput in workflows that require the reporting of real mass changes as well as allow retrospective correction of historic profiles to facilitate new discoveries with these data.