Motifier: An IgOme Profiler Based on Peptide Motifs Using Machine Learning
| Autor: | Haim Ashkenazy, Oren Avram, Arie Ryvkin, Jonathan M. Gershoni, Smadar Hada-Neeman, Anna Roitburd-Berman, Yael Weiss-Ottolenghi, Tal Pupko |
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| Rok vydání: | 2020 |
| Předmět: |
Phage display
Computer science Amino Acid Motifs Peptide Machine learning computer.software_genre Antibodies Article Set (abstract data type) Machine Learning 03 medical and health sciences 0302 clinical medicine Structural Biology Peptide Library Humans Molecular Biology 030304 developmental biology chemistry.chemical_classification 0303 health sciences business.industry Repertoire Computational Biology High-Throughput Nucleotide Sequencing Antigen binding Pipeline (software) chemistry Artificial intelligence business computer 030217 neurology & neurosurgery Algorithms |
| Zdroj: | J Mol Biol |
| ISSN: | 1089-8638 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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