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. (C) 2021 Elsevier Ltd. All rights reserved.

Automated summary

This study describes Motifier, a computational pipeline that systematically generates discriminatory peptide motifs based on affinity-selected peptides identified by Deep-Panning. These motifs effectively characterize antibody binding activities and accurately classify complex antibody mixtures through machine-learning protocols.