Motifier: An IgOme Profiler Based on Peptide Motifs Using Machine Learning

Haim Ashkenazy, Oren Avram, Arie Ryvkin, Anna Roitburd-Berman, Yael Weiss-Ottolenghi, Smadar Hada-Neeman, Jonathan M. Gershoni, Tal Pupko

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Article number167071
JournalJournal of Molecular Biology
Issue number15
StatePublished - 23 Jul 2021


  • deep-panning
  • next-generation phage display
  • phage display
  • random peptide libraries


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