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Prediction of Protein Secondary Structure

  • Book
  • © 2017

Overview

Editors:
  1. Yaoqi Zhou

    1. Institute for Glycomics and School of Information and Communication Technology, Griffith University, Southport, Australia

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  2. Andrzej Kloczkowski

    1. Battelle Center for Mathematical Medicine, Nationwide Children’s Hospital, Columbus, USA

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  3. Eshel Faraggi

    1. Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, USA

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  4. Yuedong Yang

    1. Institute for Glycomics and School of Information and Communication Technology, Griffith University, Southport, Australia

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    You can also search for this editor in PubMed   Google Scholar

  • Includes cutting-edge techniques for the study of protein 1D properties and protein secondary structure
  • Provides step-by-step detail essential for reproducible results
  • Contains key notes and implementation advice from the experts
  • Includes supplementary material: sn.pub/extras

Part of the book series: Methods in Molecular Biology (MIMB, volume 1484)

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Table of contents (21 protocols)

  1. Front Matter

    Pages i-xi
    Download chapter PDF

  2. Where the Name “GOR” Originates: A Story

    • Jean Garnier
    Pages 1-5

  3. The GOR Method of Protein Secondary Structure Prediction and Its Application as a Protein Aggregation Prediction Tool

    • Maksim Kouza, Eshel Faraggi, Andrzej Kolinski, Andrzej Kloczkowski
    Pages 7-24

  4. Consensus Prediction of Charged Single Alpha-Helices with CSAHserver

    • Dániel Dudola, Gábor TĂłth, LászlĂł Nyitray, Zoltán Gáspári
    Pages 25-34

  5. Predicting Protein Secondary Structure Using Consensus Data Mining (CDM) Based on Empirical Statistics and Evolutionary Information

    • Gaurav Kandoi, Sumudu P. Leelananda, Robert L. Jernigan, Taner Z. Sen
    Pages 35-44

  6. Accurate Prediction of One-Dimensional Protein Structure Features Using SPINE-X

    • Eshel Faraggi, Andrzej Kloczkowski
    Pages 45-53

  7. SPIDER2: A Package to Predict Secondary Structure, Accessible Surface Area, and Main-Chain Torsional Angles by Deep Neural Networks

    • Yuedong Yang, Rhys Heffernan, Kuldip Paliwal, James Lyons, Abdollah Dehzangi, Alok Sharma et al.
    Pages 55-63

  8. Backbone Dihedral Angle Prediction

    • Olav Zimmermann
    Pages 65-82

  9. One-Dimensional Structural Properties of Proteins in the Coarse-Grained CABS Model

    • Sebastian Kmiecik, Andrzej Kolinski
    Pages 83-113

  10. Assessing Predicted Contacts for Building Protein Three-Dimensional Models

    • Badri Adhikari, Debswapna Bhattacharya, Renzhi Cao, Jianlin Cheng
    Pages 115-126

  11. Fast and Accurate Accessible Surface Area Prediction Without a Sequence Profile

    • Eshel Faraggi, Maksim Kouza, Yaoqi Zhou, Andrzej Kloczkowski
    Pages 127-136

  12. How to Predict Disorder in a Protein of Interest

    • Vladimir N. Uversky
    Pages 137-158

  13. Intrinsic Disorder and Semi-disorder Prediction by SPINE-D

    • Tuo Zhang, Eshel Faraggi, Zhixiu Li, Yaoqi Zhou
    Pages 159-174

  14. Predicting Real-Valued Protein Residue Fluctuation Using FlexPred

    • Lenna Peterson, Michal Jamroz, Andrzej Kolinski, Daisuke Kihara
    Pages 175-186

  15. Prediction of Disordered RNA, DNA, and Protein Binding Regions Using DisoRDPbind

    • Zhenling Peng, Chen Wang, Vladimir N. Uversky, Lukasz Kurgan
    Pages 187-203

  16. Sequence-Based Prediction of RNA-Binding Residues in Proteins

    • Rasna R. Walia, Yasser EL-Manzalawy, Vasant G. Honavar, Drena Dobbs
    Pages 205-235

  17. Computational Approaches for Predicting Binding Partners, Interface Residues, and Binding Affinity of Protein–Protein Complexes

    • K. Yugandhar, M. Michael Gromiha
    Pages 237-253

  18. In Silico Prediction of Linear B-Cell Epitopes on Proteins

    • Yasser EL-Manzalawy, Drena Dobbs, Vasant G. Honavar
    Pages 255-264

  19. Prediction of Protein Phosphorylation Sites by Integrating Secondary Structure Information and Other One-Dimensional Structural Properties

    • Yongchao Dou, Bo Yao, Chi Zhang
    Pages 265-274

  20. Predicting Post-Translational Modifications from Local Sequence Fragments Using Machine Learning Algorithms: Overview and Best Practices

    • Marcin Tatjewski, Marcin Kierczak, Dariusz Plewczynski
    Pages 275-300
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Keywords

About this book

This thorough volume explores predicting one-dimensional functional properties, functional sites in particular, from protein sequences, an area which is getting more and more attention. Beginning with secondary structure prediction based on sequence only, the book continues by exploring secondary structure prediction based on evolution information, prediction of solvent accessible surface areas and backbone torsion angles, model building, global structural properties, functional properties, as well as visualizing interior and protruding regions in proteins. Written for the highly successful Methods in Molecular Biology series, the chapters include the kind of detail and implementation advice to ensure success in the laboratory. 

Practical and authoritative, Prediction of Protein Secondary Structure serves as a vital guide to numerous state-of-the-art techniques that are useful for computational and experimental biologists.

Editors and Affiliations

  • Institute for Glycomics and School of Information and Communication Technology, Griffith University, Southport, Australia

    Yaoqi Zhou, Yuedong Yang

  • Battelle Center for Mathematical Medicine, Nationwide Children’s Hospital, Columbus, USA

    Andrzej Kloczkowski

  • Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, USA

    Eshel Faraggi

Bibliographic Information

  • Book Title: Prediction of Protein Secondary Structure

  • Editors: Yaoqi Zhou, Andrzej Kloczkowski, Eshel Faraggi, Yuedong Yang

  • Series Title: Methods in Molecular Biology

  • DOI: https://doi.org/10.1007/978-1-4939-6406-2

  • Publisher: Humana New York, NY

  • eBook Packages: Springer Protocols

  • Copyright Information: Springer Science+Business Media New York 2017

  • Hardcover ISBN: 978-1-4939-6404-8Published: 28 October 2016

  • Softcover ISBN: 978-1-4939-8189-2Published: 08 June 2018

  • eBook ISBN: 978-1-4939-6406-2Published: 27 October 2016

  • Series ISSN: 1064-3745

  • Series E-ISSN: 1940-6029

  • Edition Number: 1

  • Number of Pages: XI, 313

  • Number of Illustrations: 11 b/w illustrations, 56 illustrations in colour

  • Topics: Protein Science

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