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Protein Function Prediction

Methods and Protocols

  • Book
  • © 2025
  • Latest edition

Overview

Editors:
  1. Lukasz Kurgan

    1. Computer Science, Virginia Commonwealth University, RICHMOND, USA

  2. Daisuke Kihara

    1. Biological Sciences, Purdue University, West Lafayette, USA

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

Part of the book series: Methods in Molecular Biology (MIMB, volume 2947)

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About this book

This fully updated volume explores a wide array of new and state-of-the-art tools and resources for protein function prediction. Beginning with in-depth overviews of essential underlying computational techniques, such as machine learning, multi-task learning, protein language models, and deep learning, the book continues by covering specific tools for protein function prediction, ranging from gene ontology-term predictions to the predictions of binding sites, protein localization and solubility, signal peptides, intrinsic disorder, and intrinsically disordered binding regions, as well as presenting databases that address protein moonlighting and protein binding. Written for the highly successful Methods in Molecular Biology series, chapters include introductions to their respective topics, step-by-step instructions on how to use software and web resources, use cases, and tips on troubleshooting and avoiding known pitfalls.

 

Authoritative and up-to-date, Protein Function Prediction: Methods and Protocols, Second Edition helps readers to understand and appreciate this vibrant and growing research area and guides in the quest to identify and use the best computational methods and resources for their projects.

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Table of contents (20 protocols)

  1. Front Matter

    Pages i-xiv
    Download chapter PDF

  2. Overview and Surveys

    1. Front Matter

      Pages 1-1
      Download chapter PDF

    2. Computational prediction of protein functional annotations

      • Maxat Kulmanov, Robert Hoehndorf
      Pages 3-28

    3. Machine Learning for Protein Function Prediction

      • Yi-Heng Zhu, Zi Liu, Yu Ding, Zhiwei Ji, Dong-Jun Yu
      Pages 29-48

    4. Graph Neural Network-Based Approaches for Protein Function Prediction

      • Meenal Chaudhari, Soufia Bahmani, Pawel Pratyush, Steven Garrett, Neel J. Thapa, Dukka B. KC
      Pages 49-74

    5. Multitask Learning-Based Approaches for Protein Function Prediction

      • Soufia Bahmani, Meenal Chaudhari, Callen Carrier, Steven Garrett, Pawel Pratyush, Dukka B. KC
      Pages 75-88

    6. A Survey of Deep Learning Methods and Tools for Protein Binding Site Prediction

      • Alina Rohulia, Yanli Wang, Jianlin Cheng
      Pages 89-108

    7. A Survey of Current Status in AI-Based Topology Prediction of Transmembrane Proteins

      • Divyangana Bathla, Richa Mishra, Shandar Ahmad
      Pages 109-135

  3. Tools

    1. Front Matter

      Pages 137-137
      Download chapter PDF

    2. NetGO 3.0: A Recent Protein Function Prediction Tool Based on Protein Language Model

      • Shaojun Wang, Hancheng Liu, Ronghui You, Yunjia Liu, Yi Xiong, Shanfeng Zhu
      Pages 139-150

    3. Predicting Protein Functions with Function-Aware Domain Embeddings Using Domain-PFP

      • Nabil Ibtehaz, Daisuke Kihara
      Pages 151-160

    4. Integrating Gene Ontology Relationships for Protein Function Prediction Using PFresGO

      • Tong Pan, Geoffrey I. Webb, Seiya Imoto, Jiangning Song
      Pages 161-169

    5. Annotating genomes with DeepGO protein function prediction tools

      • Rund Tawfiq, Kexin Niu, Maxat Kulmanov, Robert Hoehndorf
      Pages 171-189

    6. An Online Server for Geometry-Aware Protein Function Annotations Through Predicted Structure

      • Jialin Zou, Qianmu Yuan, Yuedong Yang
      Pages 191-208

    7. Exploring Binding Sites on Proteins for Function Prediction Using the PoSSuM Databases

      • Kentaro Tomii, Kazuyoshi Ikeda
      Pages 209-222

    8. Comprehensive Prediction of Protein Localization and Signal Peptides Using MULocDeep

      • Lei Jiang, Weinan Zhang, Shuai Zeng, Yuexu Jiang, Dong Xu
      Pages 223-239

    9. A Benchmarking Platform for Assessing Protein Language Models on Function-Related Prediction Tasks

      • Elif Çevrim, Melih Gökay Yiğit, Erva Ulusoy, Ardan Yılmaz, Tunca Doğan
      Pages 241-268

    10. Prediction of Intrinsic Disorder Functions with DEPICTER2

      • Sushmita Basu, Lukasz Kurgan
      Pages 269-284

    11. Prediction of Disordered Linear Interacting Peptides with CLIP

      • Jiahui Liang, Zhenling Peng, Lukasz Kurgan
      Pages 285-299

    12. Prediction of Intrinsically Disordered Lipid Binding Residues with DisoLipPred

      • Bi Zhao, Lukasz Kurgan
      Pages 301-312
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Editors and Affiliations

  • Computer Science, Virginia Commonwealth University, RICHMOND, USA

    Lukasz Kurgan

  • Biological Sciences, Purdue University, West Lafayette, USA

    Daisuke Kihara

Accessibility Information

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This ebook is designed with accessibility in mind, aiming to meet the ePub Accessibility 1.0 AA and WCAG 2.2 Level AA standards. It features a navigable table of contents, structured headings, and alternative text for images, ensuring smooth, intuitive navigation and comprehension. The text is reflowable and resizable, with sufficient contrast. We recognize the importance of accessibility, and we welcome queries about accessibility for any of our products. If you have a question or an access need, please get in touch with us at accessibilitysupport@springernature.com.

Bibliographic Information

  • Book Title: Protein Function Prediction

  • Book Subtitle: Methods and Protocols

  • Editors: Lukasz Kurgan, Daisuke Kihara

  • Series Title: Methods in Molecular Biology

  • DOI: https://doi.org/10.1007/978-1-0716-4662-5

  • 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 2025

  • Hardcover ISBN: 978-1-0716-4661-8Published: 30 July 2025

  • Softcover ISBN: 978-1-0716-4664-9Due: 13 August 2026

  • eBook ISBN: 978-1-0716-4662-5Published: 29 July 2025

  • Series ISSN: 1064-3745

  • Series E-ISSN: 1940-6029

  • Edition Number: 2

  • Number of Pages: XIV, 360

  • Number of Illustrations: 78 b/w illustrations

  • Topics: Chemistry/Food Science, general, Bioinformatics

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