Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark.
Methods Mol Biol. 2024;2758:179-195. doi: 10.1007/978-1-0716-3646-6_9.
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.
肽类治疗正在兴起。肽组学领域的进展使研究人员能够从各种生物体和组织类型中获取有关肽存在、表达和功能的重要信息。用于高通量肽定量的质谱技术的发展为鉴定和发现众多已知和新型肽铺平了道路。尽管已经取得了很多成就，但科学家们在缩小大规模质谱生成的肽组学数据集的搜索空间并关注功能相关肽子集方面仍然面临困难。此外，目前尚无直接的方法来分析比较不同生物样本中生物活性肽的分布，而当试图描述组织或液体特异性肽组时，这可能会揭示出很多有用的信息。在本章中，我们展示了如何从广泛的肽组学数据集中识别、排序和比较预测的生物活性肽和生物活性分布。为了辅助这项任务，我们利用 MultiPep（一种用于分类肽生物活性的多标签深度学习方法）来识别生物活性肽。预测的生物活性与 UniProt 数据库中的蛋白质信息协同结合，有助于在充满可能性的治疗性肽和相关肽先导物的丛林中导航。
