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

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Computational Methods for Predicting Post-Translational Modification Sites

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2499))

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.

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Acknowledgments

This work was supported by a National Science Foundation CAREER award (MCB-1552522) awarded to L.M.H. We thank Prof. Dukka KC, Amanda Smythers, and Patric Sadecki for critical reading of the manuscript.

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  1. Department of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Anthony A. Iannetta & Leslie M. Hicks

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  1. Anthony A. Iannetta
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  1. Michigan Technological University, Houghton, MI, USA

    Dukka B. KC

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Iannetta, A.A., Hicks, L.M. (2022). Maximizing Depth of PTM Coverage: Generating Robust MS Datasets for Computational Prediction Modeling. In: KC, D.B. (eds) Computational Methods for Predicting Post-Translational Modification Sites. Methods in Molecular Biology, vol 2499. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2317-6_1

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