Motifier: an IgOme profiler based on peptide-motifs using machine learning

Antibodies provide a comprehensive record of the encounters with threats and insults to the immune system. The ability to examine the repertoire of antibodies in serum and discover those that best represent “discriminating features” characteristic of various clinical situations, is potentially very useful. Recently, phage display technologies combined with Next-Generation Sequencing (NGS) produced a powerful experimental methodology, coined “Deep-Panning”, in which the spectrum of serum antibodies is probed. In order to extract meaningful biological insights from the tens of millions of affinity-selected peptides generated by Deep-Panning, advanced bioinformatics algorithms are a must. In this study, we describe Motifier, a computational pipeline comprised of a set of algorithms that systematically generates discriminatory peptide motifs based on the affinity-selected peptides identified by Deep-Panning. These motifs are shown to effectively characterize antibody binding activities and through the implementation of machine-learning protocols are shown to accurately classify complex antibody mixtures representing various biological conditions.

抗体可全面记录免疫系统曾遭遇的各类威胁与损伤事件。检测血清中的抗体谱系，并挖掘出最能体现各类临床场景特征"鉴别特征"的抗体，这一能力具备极高的应用潜力。近年来，噬菌体展示技术（phage display technologies）结合下一代测序（Next-Generation Sequencing, NGS）催生了一种强大的实验方法，被命名为"深度淘选（Deep-Panning）"，该方法可对血清抗体谱进行探测分析。若要从深度淘选所产生的数千万个亲和筛选肽段中提取有价值的生物学见解，先进的生物信息学算法必不可少。本研究介绍了Motifier——一套由多种算法组成的计算流程，可基于深度淘选鉴定出的亲和筛选肽段，系统性生成具有鉴别性的肽基序。实验结果表明，此类肽基序可有效表征抗体结合活性，且通过机器学习协议的实施，能够精准分类代表不同生物学状态的复杂抗体混合物。