Prediction of Protein Secondary Structure

1. Front Matter

   Pages i-xi

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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 Tóth, László 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

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.

• 1D structural properties of proteins
• Protein backbone structure
• Model building
• One-dimensional functional properties
• Backbone torsion angles
• 3D structure

• 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

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