Maximizing Depth of PTM Coverage: Generating Robust MS Datasets for Computational Prediction Modeling

Anthony A Iannetta; Leslie M Hicks

doi:10.1007/978-1-0716-2317-6_1

Maximizing Depth of PTM Coverage: Generating Robust MS Datasets for Computational Prediction Modeling

Methods Mol Biol. 2022:2499:1-41. doi: 10.1007/978-1-0716-2317-6_1.

Authors

Anthony A Iannetta¹, Leslie M Hicks²

Affiliations

¹ Department of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
² Department of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. lmhicks@unc.edu.

PMID: 35696073
DOI: 10.1007/978-1-0716-2317-6_1

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.

Publication types

Review
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Computational Biology* / methods
Computer Simulation
Mass Spectrometry
Protein Processing, Post-Translational*
Proteomics / methods