Maximizing Depth of PTM Coverage: Generating Robust MS Datasets for Computational Prediction Modeling
- PMID: 35696073
- DOI: 10.1007/978-1-0716-2317-6_1
Maximizing Depth of PTM Coverage: Generating Robust MS Datasets for Computational Prediction Modeling
Abstract
Post-translational modifications (PTMs) regulate complex biological processes through the modulation of protein activity, stability, and localization. Insights into the specific modification type and localization within a protein sequence can help ascertain functional significance. Computational models are increasingly demonstrated to offer a low-cost, high-throughput method for comprehensive PTM predictions. Algorithms are optimized using existing experimental PTM data, thus accurate prediction performance relies on the creation of robust datasets. Herein, advancements in mass spectrometry-based proteomics technologies to maximize PTM coverage are reviewed. Further, requisite experimental validation approaches for PTM predictions are explored to ensure that follow-up mechanistic studies are focused on accurate modification sites.
Keywords: Bioinformatics; Bottom-up proteomics; Database searching; Liquid chromatography–tandem mass spectrometry; PTM enrichment; Post-translational modifications.
© 2022. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.
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