Prediction of Protein Secondary Structure

<p>Where the Name “GOR” Originates: A Story.-&nbsp;The GOR Method of Protein Secondary Structure Prediction and Its Application as a Protein Aggregation Prediction Tool.-&nbsp;Consensus Prediction of Charged Single Alpha-Helices with CSAHserver.-&nbsp;Predicting Protein Secondary Structure Using Consensus Data Mining (CDM) Based on Empirical Statistics and Evolutionary Information.-&nbsp;Accurate Prediction of One-Dimensional Protein Structure Features Using SPINE-X.-&nbsp;SPIDER2: A Package to Predict Secondary Structure, Accessible Surface Area, and Main-Chain Torsional Angles by Deep Neural Networks.-&nbsp;Backbone Dihedral Angle Prediction.-&nbsp;One-Dimensional Structural Properties of Proteins in the Coarse-Grained CABS Model.-&nbsp;Assessing Predicted Contacts for Building Protein Three-Dimensional Models.-&nbsp;Fast and Accurate Accessible Surface Area Prediction Without a Sequence Profile.-&nbsp;How to Predict Disorder in a Protein of Interest.-&nbsp;Intrinsic Disorder and Semi-Disorder Prediction by SPINE-D.-&nbsp;Predicting Real-Valued Protein Residue Fluctuation Using FlexPred.-&nbsp;Prediction of Disordered RNA, DNA, and Protein Binding Regions Using DisoRDPbind.-&nbsp;Sequence-Based Prediction of RNA-Binding Residues in Proteins.-&nbsp;Computational Approaches for Predicting Binding Partners, Interface Residues, and Binding Affinity of Protein-Protein Complexes.-&nbsp;In Silico Prediction of Linear B-Cell Epitopes on Proteins.-&nbsp;Prediction of Protein Phosphorylation Sites by Integrating Secondary Structure Information and Other One-Dimensional Structural Properties.-&nbsp;Predicting Post-Translational Modifications from Local Sequence Fragments Using Machine Learning Algorithms: Overview and Best Practices.-&nbsp;CX, DPX, and PCW: Web Servers for the Visualization of Interior and Protruding Regions of Protein Structures in 3D and 1D.</p>