Integrating a Multi-label Deep Learning Approach with Protein Information to Compare Bioactive Peptides in Brain and Plasma

Research output: Chapter in Book/Report/Conference proceedingBook chapterResearchpeer-review

Peptide therapeutics is gaining momentum. Advances in the field of peptidomics have enabled researchers to harvest vital information from various organisms and tissue types concerning peptide existence, expression and function. The development of mass spectrometry techniques for high-throughput peptide quantitation has paved the way for the identification and discovery of numerous known and novel peptides. Though much has been achieved, scientists are still facing difficulties when it comes to reducing the search space of the large mass spectrometry-generated peptidomics datasets and focusing on the subset of functionally relevant peptides. Moreover, there is currently no straightforward way to analytically compare the distributions of bioactive peptides in distinct biological samples, which may reveal much useful information when seeking to characterize tissue- or fluid-specific peptidomes. In this chapter, we demonstrate how to identify, rank, and compare predicted bioactive peptides and bioactivity distributions from extensive peptidomics datasets. To aid this task, we utilize MultiPep, a multi-label deep learning approach designed for classifying peptide bioactivities, to identify bioactive peptides. The predicted bioactivities are synergistically combined with protein information from the UniProt database, which assist in navigating through the jungle of putative therapeutic peptides and relevant peptide leads.

Original languageEnglish
Title of host publicationPeptidomics : Methods and Protocols
EditorsMichael Schrader, Lloyd D. Fricker
Number of pages17
PublisherHumana Press
Publication date2024
ISBN (Print)978-1-0716-3648-0
ISBN (Electronic)978-1-0716-3646-6
Publication statusPublished - 2024
SeriesMethods in Molecular Biology

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024.

    Research areas

  • Deep learning, Neuropeptide, Peptide bioactivity, Peptide hormone, Peptide therapeutics, Peptidomics

ID: 389551340