Statistical Learning from Single-Molecule Experiments: Support Vector Machines and Expectation–Maximization Approaches to Understanding Protein Unfolding Data
Statistical Learning from Single-Molecule Experiments: Support Vector Machines and Expectation–Maximization Approaches to Understanding Protein Unfolding Data
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单分子实验的统计学习：支持向量机和期望——理解蛋白质展开数据的最大化方法
DOI：
10.1021/acs.jpcb.1c02334
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Barsegov, Valeri
中科院分区：
文献类型：
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作者：
Maksudov, Farkhad;Jones, Lee K.;Barsegov, Valeri
单分子力谱已成为探索涉及蛋白质的动态过程的有力工具；然而，对实验数据进行有意义的解释仍然具有挑战性。由于低信噪比，实验力扩展谱包含非特异性相互作用、尖端或底物脱离和蛋白质脱附引起的力信号。复杂蛋白质结构的解开导致了不同类型的展开转变。在这里，我们测试了支持向量机（SVM）和期望最大化（EM）方法在动态力实验统计学习中的性能。当分子建模（或其他研究）的输出用作训练集时，支持向量机和EM可以应用于理解展开力数据。最大边际或最大似然分类器可以将实验测试观测值分离为不同类型的展开转换，然后利用EM优化来解决展开力的统计：权重、平均力和标准差。我们设计了一种基于em的方法，可以直接应用于实验数据，不需要对数据进行分类，也不需要将数据分为训练观测和测试观测。这种方法即使在样本量较小和展开过渡具有重叠力范围的情况下也能很好地发挥作用。
Single-molecule force spectroscopy has become a powerful tool for the exploration of dynamic processes that involve proteins; yet, meaningful interpretation of the experimental data remains challenging. Owing to low signal-to-noise ratio, experimental force-extension spectra contain force signals due to nonspecific interactions, tip or substrate detachment, and protein desorption. Unravelling of complex protein structures results in the unfolding transitions of different types. Here, we test the performance of Support Vector Machines (SVM) and Expectation Maximization (EM) approaches in statistical learning from dynamic force experiments. When the output from molecular modelingin silico(or other studies) is used as a training set, SVM and EM can be applied to understand the unfolding force data. The maximal margin or maximum likelihood classifier can be used to separate experimental test observations into the unfolding transitions of different types, and EM optimization can then be utilized to resolve the statistics of unfolding forces: weights, average forces, and standard deviations. We designed an EM-based approach, which can be directly applied to the experimental data without data classification and division into training and test observations. This approach performs well even when the sample size is small and when the unfolding transitions are characterized by overlapping force ranges.