Ripoll Daniel R, Chaudhury Sidhartha, Wallqvist Anders
DoD Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick, Maryland, United States of America.
Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc. (HJF), Bethesda, Maryland, United States of America.
PLoS Comput Biol. 2021 Mar 29;17(3):e1008864. doi: 10.1371/journal.pcbi.1008864. eCollection 2021 Mar.
High-throughput B-cell sequencing has opened up new avenues for investigating complex mechanisms underlying our adaptive immune response. These technological advances drive data generation and the need to mine and analyze the information contained in these large datasets, in particular the identification of therapeutic antibodies (Abs) or those associated with disease exposure and protection. Here, we describe our efforts to use artificial intelligence (AI)-based image-analyses for prospective classification of Abs based solely on sequence information. We hypothesized that Abs recognizing the same part of an antigen share a limited set of features at the binding interface, and that the binding site regions of these Abs share share common structure and physicochemical property patterns that can serve as a "fingerprint" to recognize uncharacterized Abs. We combined large-scale sequence-based protein-structure predictions to generate ensembles of 3-D Ab models, reduced the Ab binding interface to a 2-D image (fingerprint), used pre-trained convolutional neural networks to extract features, and trained deep neural networks (DNNs) to classify Abs. We evaluated this approach using Ab sequences derived from human HIV and Ebola viral infections to differentiate between two Abs, Abs belonging to specific B-cell family lineages, and Abs with different epitope preferences. In addition, we explored a different type of DNN method to detect one class of Abs from a larger pool of Abs. Testing on Ab sets that had been kept aside during model training, we achieved average prediction accuracies ranging from 71-96% depending on the complexity of the classification task. The high level of accuracies reached during these classification tests suggests that the DNN models were able to learn a series of structural patterns shared by Abs belonging to the same class. The developed methodology provides a means to apply AI-based image recognition techniques to analyze high-throughput B-cell sequencing datasets (repertoires) for Ab classification.
高通量B细胞测序为研究适应性免疫反应背后的复杂机制开辟了新途径。这些技术进步推动了数据生成，以及挖掘和分析这些大型数据集中所包含信息的需求，特别是治疗性抗体（Abs）或与疾病暴露和保护相关抗体的识别。在这里，我们描述了我们利用基于人工智能（AI）的图像分析，仅根据序列信息对抗体进行前瞻性分类的努力。我们假设识别抗原同一部分的抗体在结合界面共享一组有限的特征，并且这些抗体的结合位点区域共享共同的结构和物理化学性质模式，可作为识别未表征抗体的“指纹”。我们结合基于大规模序列的蛋白质结构预测，生成3D抗体模型的集合，将抗体结合界面简化为二维图像（指纹），使用预训练的卷积神经网络提取特征，并训练深度神经网络（DNN）对抗体进行分类。我们使用源自人类HIV和埃博拉病毒感染的抗体序列评估了这种方法，以区分两种抗体、属于特定B细胞家族谱系的抗体以及具有不同表位偏好的抗体。此外，我们探索了一种不同类型的DNN方法，以从更大的抗体库中检测一类抗体。在模型训练期间留出的抗体集上进行测试，根据分类任务的复杂性，我们实现了71%-96%的平均预测准确率。这些分类测试中达到的高准确率表明，DNN模型能够学习同一类抗体共享的一系列结构模式。所开发的方法提供了一种手段，可应用基于AI的图像识别技术来分析高通量B细胞测序数据集（库）以进行抗体分类。
