Prediction of Protein Secondary Structure

Chapter 1 Where the Name “GOR” Originates: A Story

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

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

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

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

Chapter 6 SPIDER2: A Package to Predict Secondary Structure, Accessible Surface Area, and Main-Chain Torsional Angles by Deep Neural Networks

Chapter 7 Backbone Dihedral Angle Prediction

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

Chapter 9 Assessing Predicted Contacts for Building Protein Three-Dimensional Models

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

Chapter 11 How to Predict Disorder in a Protein of Interest

Chapter 12 Intrinsic Disorder and Semi-disorder Prediction by SPINE-D

Chapter 13 Prediction of Protein Secondary Structure

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

Chapter 15 Prediction of Protein Secondary Structure

Chapter 16 Computational Approaches for Predicting Binding Partners, Interface Residues, and Binding Affinity of Protein–Protein Complexes

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

Chapter 18 Prediction of Protein Phosphorylation Sites by Integrating Secondary Structure Information and Other One-Dimensional Structural Properties

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

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

Chapter 21 Erratum to: One-Dimensional Structural Properties of Proteins in the Coarse-Grained CABS Model