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Deep Learning–Based Advances In Protein Posttranslational Modification Site and Protein Cleavage Prediction

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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

Posttranslational modification (PTM ) is a ubiquitous phenomenon in both eukaryotes and prokaryotes which gives rise to enormous proteomic diversity. PTM mostly comes in two flavors: covalent modification to polypeptide chain and proteolytic cleavage. Understanding and characterization of PTM is a fundamental step toward understanding the underpinning of biology. Recent advances in experimental approaches, mainly mass-spectrometry-based approaches, have immensely helped in obtaining and characterizing PTMs. However, experimental approaches are not enough to understand and characterize more than 450 different types of PTMs and complementary computational approaches are becoming popular. Recently, due to the various advancements in the field of Deep Learning (DL), along with the explosion of applications of DL to various fields, the field of computational prediction of PTM has also witnessed the development of a plethora of deep learning (DL)-based approaches. In this book chapter, we first review some recent DL-based approaches in the field of PTM site prediction. In addition, we also review the recent advances in the not-so-studied PTM , that is, proteolytic cleavage predictions. We describe advances in PTM prediction by highlighting the Deep learning architecture, feature encoding, novelty of the approaches, and availability of the tools/approaches. Finally, we provide an outlook and possible future research directions for DL-based approaches for PTM prediction.

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Author notes

  1. Subash C. Pakhrin and Suresh Pokharel contributed equally with all other contributors.

Authors and Affiliations

  1. School of Computing, College of Engineering, Wichita State University, Wichita, KS, USA

    Subash C. Pakhrin

  2. Department of Computer Science, College of Computing, Michigan Technological University, Houghton, MI, USA

    Suresh Pokharel & Dukka B. KC

  3. Faculty of Information Science and Electrical Engineering, Kyushu University, Fukuoka, Japan

    Hiroto Saigo

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  1. Subash C. Pakhrin
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  2. Suresh Pokharel
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  3. Hiroto Saigo
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  4. Dukka B. KC
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Corresponding author

Correspondence to Dukka B. KC .

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  1. Michigan Technological University, Houghton, MI, USA

    Dukka B. KC

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Pakhrin, S.C., Pokharel, S., Saigo, H., KC, D.B. (2022). Deep Learning–Based Advances In Protein Posttranslational Modification Site and Protein Cleavage Prediction. 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_15

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  • DOI: https://doi.org/10.1007/978-1-0716-2317-6_15

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