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Computational Methods for Predicting Post-Translational Modification Sites

  • Book
  • © 2022

Overview

Editors:
  1. Dukka B. KC

    1. Michigan Technological University, Houghton, USA

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  • Includes cutting-edge methods and protocols
  • Provides step-by-step detail essential for reproducible results
  • Contains key notes and implementation advice from the experts

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

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About this book

This volume describes computational approaches to predict multitudes of PTM sites. Chapters describe in depth approaches on algorithms, state-of-the-art Deep Learning based approaches, hand-crafted features, physico-chemical based features, issues related to obtaining negative training, sequence-based features, and structure-based features. Written in the format of the highly successful Methods in Molecular Biology series, each chapter includes an introduction to the topic, lists necessary materials and reagents, includes tips on troubleshooting and known pitfalls, and step-by-step, readily reproducible protocols.

 Authoritative and cutting-edge, Authoritative and cutting-edge,  Computational Methods for Predicting Post-Translational Modification Sites aims to be a useful guide for researchers who are interested in the field of PTM site prediction. 

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Table of contents (15 protocols)

  1. Front Matter

    Pages i-xvii
    Download chapter PDF

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

    • Anthony A. Iannetta, Leslie M. Hicks
    Pages 1-41

  3. PLDMS: Phosphopeptide Library Dephosphorylation Followed by Mass Spectrometry Analysis to Determine the Specificity of Phosphatases for Dephosphorylation Site Sequences

    • Thomas Kokot, Bernhard Hoermann, Dominic Helm, Jeremy E. Chojnacki, Mikhail M. Savitski, Maja Köhn
    Pages 43-64

  4. FEPS: A Tool for Feature Extraction from Protein Sequence

    • Hamid Ismail, Clarence White, Hussam AL-Barakati, Robert H. Newman, Dukka B. KC
    Pages 65-104

  5. A Pretrained ELECTRA Model for Kinase-Specific Phosphorylation Site Prediction

    • Lei Jiang, Duolin Wang, Dong Xu
    Pages 105-124

  6. iProtGly-SS: A Tool to Accurately Predict Protein Glycation Site Using Structural-Based Features

    • Iman Dehzangi, Alok Sharma, Swakkhar Shatabda
    Pages 125-134

  7. Functions of Glycosylation and Related Web Resources for Its Prediction

    • Kiyoko F. Aoki-Kinoshita
    Pages 135-144

  8. Analysis of Posttranslational Modifications in Arabidopsis Proteins and Metabolic Pathways Using the FAT-PTM Database

    • Madison N. Blea, Ian S. Wallace
    Pages 145-154

  9. Bioinformatic Analyses of Peroxiredoxins and RF-Prx: A Random Forest-Based Predictor and Classifier for Prxs

    • Hussam AL-Barakati, Robert H. Newman, Dukka B. KC, Leslie B. Poole
    Pages 155-176

  10. Computational Prediction of N- and O-Linked Glycosylation Sites for Human and Mouse Proteins

    • Ghazaleh Taherzadeh, Matthew Campbell, Yaoqi Zhou
    Pages 177-186

  11. iPTMnet RESTful API for Post-translational Modification Network Analysis

    • Sachin Gavali, Karen E. Ross, Julie Cowart, Chuming Chen, Cathy H. Wu
    Pages 187-204

  12. Systematic Characterization of Lysine Post-translational Modification Sites Using MUscADEL

    • Zhen Chen, Xuhan Liu, Fuyi Li, Chen Li, Tatiana Marquez-Lago, André Leier et al.
    Pages 205-219

  13. Enhancing the Discovery of Functional Post-Translational Modification Sites with Machine Learning Models – Development, Validation, and Interpretation

    • Nolan English, Matthew Torres
    Pages 221-260

  14. Exploration of Protein Posttranslational Modification Landscape and Cross Talk with CrossTalkMapper

    • Arthur Grimaud, Frederik Haugaard Holck, Louise Marie Buur, Rebecca Kirsch, Veit Schwämmle
    Pages 261-273

  15. PTM-X: Prediction of Post-Translational Modification Crosstalk Within and Across Proteins

    • Yuxuan Li, Yuanhua Huang, Tingting Li
    Pages 275-283

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

    • Subash C. Pakhrin, Suresh Pokharel, Hiroto Saigo, Dukka B. KC
    Pages 285-322

  17. Back Matter

    Pages 323-326
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Editors and Affiliations

  • Michigan Technological University, Houghton, USA

    Dukka B. KC

Bibliographic Information

  • Book Title: Computational Methods for Predicting Post-Translational Modification Sites

  • Editors: Dukka B. KC

  • Series Title: Methods in Molecular Biology

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

  • Publisher: Humana New York, NY

  • eBook Packages: Springer Protocols

  • Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2022

  • Hardcover ISBN: 978-1-0716-2316-9Published: 14 June 2022

  • Softcover ISBN: 978-1-0716-2319-0Published: 14 June 2023

  • eBook ISBN: 978-1-0716-2317-6Published: 13 June 2022

  • Series ISSN: 1064-3745

  • Series E-ISSN: 1940-6029

  • Edition Number: 1

  • Number of Pages: XVII, 326

  • Number of Illustrations: 6 b/w illustrations, 60 illustrations in colour

  • Topics: Bioinformatics

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