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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.

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1. Where the Name "GOR" Originates: A Story

Jean Garnier

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

Maksim Kouza, Eshel Faraggi, Andrzej Kolinski, and Andrzej Kloczkowski

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

Dániel Dudola, Gábor Tóth, László Nyitray, and Zoltán Gáspári

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

Gaurav Kandoi, Sumudu P. Leelananda, Robert L. Jernigan, and Taner Z. Sen

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

Eshel Faraggi and Andrzej Kloczkowski

6. 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, Jihua Wang, Abdul Sattar, and Yaoqi Zhou

7. Backbone Dihedral Angle Prediction

Olav Zimmermann

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

Sebastian Kmiecik and Andrzej Kolinski

Badri Adhikari, Debswapna Bhattacharya, Renzhi Cao, and Jianlin Cheng

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

Eshel Faraggi, Maksim Kouza, Yaoqi Zhou, and Andrzej Kloczkowski

11. How to Predict Disorder in a Protein of Interest

Vladimir N. Uversky

12. Intrinsic Disorder and Semi-Disorder Prediction by SPINE-D

Tuo Zhang, Eshel Faraggi, Zhixiu Li, and Yaoqi Zhou

13. Predicting Real-Valued Protein Residue Fluctuation Using FlexPred

Lenna Peterson, Michal Jamroz, Andrzej Kolinski, and Daisuke Kihara

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

Zhenling Peng, Chen Wang, Vladimir N. Uversky, and Lukasz Kurgan

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

Rasna R. Walia, Yasser EL-Manzalawy, Vasant G. Honavar, and Drena Dobbs

16. Computational Approaches for Predicting Binding Partners, Interface Residues, and Binding Affinity of Protein-Protein Complexes

K. Yugandhar and M. Michael Gromiha

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

Yasser EL-Manzalawy, Drena Dobbs, and Vasant G. Honavar

18. Prediction of Protein Phosphorylation Sites by Integrating Secondary Structure Information and Other One-Dimensional Structural Properties
Yongchao Dou, Bo Yao, and Chi Zhang

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

Marcin Tatjewski, Marcin Kierczak, and Dariusz Plewczynski

20. CX, DPX, and PCW: Web Servers for the Visualization of Interior and Protruding Regions of Protein Structures in 3D and 1D

Balázs Ligeti, Roberto Vera, János Juhász, and Sándor Pongor

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