深度学习辅助的体液免疫蛋白复合物亲和力分类

Bahar Dadfar, Safoura Vaez, Cristian Haret, Meike Koenig, Tahereh Mohammadi Hafshejani, Matthias Franzreb, Joerg Lahann
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引用次数: 0

摘要

免疫球蛋白是生物学和生物技术的重要组成部分。随着综合深度学习方法的出现,出现了快速、准确的蛋白质-蛋白质相互作用分类方法的巨大机遇。在此,我们将广泛使用的图像分类算法重新用于对一系列不同的免疫球蛋白 G(IgG)复合物进行物种特异性分型。将各种免疫球蛋白与 B 细胞超抗原(SAg)(重组葡萄球菌蛋白 A)混合后的液滴沉积在疏水性聚合物基底上,并使用偏振光显微镜对所产生的蛋白染色进行成像。一项基于 23 745 张图像的综合研究发现,预训练卷积神经网络(CNN)InceptionV3 不仅能成功地对来自四个不同物种的 IgG 进行分类,还能预测它们与蛋白 A 的结合亲和力:对 36 对结合对进行平均,观察到以下结果:1) 整体准确率为 81.4%;2) 人类 IgG 的预测准确率最高,它是与蛋白 A 结合亲和力最高的抗体;3) 不同 IgG/Protein A 比率的分类准确率通常与通过圆二色光谱测定的蛋白质-蛋白质-复合物的结合强度相关。此外,使用不同超抗原(SAg,蛋白质 G)的一组新图像对使用 IgG/Protein A 染色图像预训练的 CNN 进行了挑战。尽管使用了未知的 SAg,但 CNN 仍能正确地对人类 IgG 和蛋白质 G 的图像进行分类,在各种摩尔结合率下的准确率高达 94%。这些发现值得注意,因为它们证明了经过适当预训练的 CNN 可用于预测超出原始训练集范围的蛋白质-蛋白质相互作用。在深度学习方法的帮助下,混合蛋白质溶液的简单染色可以准确预测蛋白质-蛋白质相互作用的强度,这与蛋白质工程、自聚集或蛋白质在复杂介质中的稳定性息息相关。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Deep-Learning-Assisted Affinity Classification for Humoral Immunoprotein Complexes

Deep-Learning-Assisted Affinity Classification for Humoral Immunoprotein Complexes
Immunoglobulins are important building blocks in biology and biotechnology. With the emergence of comprehensive deep-learning approaches, there are enormous opportunities for fast and accurate methods of classification of protein–protein interactions to arise. Herein, widely accessible image classification algorithms for species-specific typification of a range of different immunoglobulin G (IgG) complexes are repurposed. Droplets of various immunoglobulins mixed with a B-cell superantigen (SAg) (recombinant staphylococcal Protein A) are deposited onto hydrophobic polymer substrates and the resulting protein stains are imaged using polarized light microscopy. A comprehensive study based on 23 745 images finds that the pretrained convolutional neural network (CNN) InceptionV3 not only successfully categorizes IgGs from four different species but also predicts their binding affinity to Protein A: averaged over 36 binding pairs, the following are observed: 1) an overall accuracy of 81.4%, 2) the highest prediction accuracy for human IgG, the antibody with the highest binding affinity for Protein A, and 3) that the classification accuracy regarding the various IgG/Protein A ratios generally correlates with the binding strength of the protein–protein–complex as determined via circular dichroism spectroscopy. In addition, the CNN pretrained with IgG/Protein A stain images has been challenged with a new set of images using a different superantigen (SAg, Protein G). Despite the use of the unknown SAg, the CNN correctly classifies the images of human IgG and Protein G as indicated by a 94% accuracy over the various molar binding ratios. These findings are noteworthy because they demonstrate that appropriately pretrained CNNs can be used for the prediction of protein–protein interactions beyond the scope of the original training set. Aided by deep-learning methods, simple stains of mixed protein solutions may serve as accurate predictors of the strength of protein–protein interactions with relevance to protein engineering, self-aggregation, or protein stability in complex media.
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