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Prediction of Protein Secondary Structure

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Cover of 'Prediction of Protein Secondary Structure'

Table of Contents

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    Book Overview
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    Chapter 1 Where the Name “GOR” Originates: A Story
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    Chapter 2 The GOR Method of Protein Secondary Structure Prediction and Its Application as a Protein Aggregation Prediction Tool
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    Chapter 3 Consensus Prediction of Charged Single Alpha-Helices with CSAHserver
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    Chapter 4 Predicting Protein Secondary Structure Using Consensus Data Mining (CDM) Based on Empirical Statistics and Evolutionary Information
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    Chapter 5 Accurate Prediction of One-Dimensional Protein Structure Features Using SPINE-X
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    Chapter 6 SPIDER2: A Package to Predict Secondary Structure, Accessible Surface Area, and Main-Chain Torsional Angles by Deep Neural Networks
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    Chapter 7 Backbone Dihedral Angle Prediction
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    Chapter 8 One-Dimensional Structural Properties of Proteins in the Coarse-Grained CABS Model
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    Chapter 9 Assessing Predicted Contacts for Building Protein Three-Dimensional Models
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    Chapter 10 Fast and Accurate Accessible Surface Area Prediction Without a Sequence Profile
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    Chapter 11 How to Predict Disorder in a Protein of Interest
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    Chapter 12 Intrinsic Disorder and Semi-disorder Prediction by SPINE-D
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    Chapter 13 Prediction of Protein Secondary Structure
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    Chapter 14 Prediction of Disordered RNA, DNA, and Protein Binding Regions Using DisoRDPbind
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    Chapter 15 Prediction of Protein Secondary Structure
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    Chapter 16 Computational Approaches for Predicting Binding Partners, Interface Residues, and Binding Affinity of Protein–Protein Complexes
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    Chapter 17 In Silico Prediction of Linear B-Cell Epitopes on Proteins
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    Chapter 18 Prediction of Protein Phosphorylation Sites by Integrating Secondary Structure Information and Other One-Dimensional Structural Properties
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    Chapter 19 Predicting Post-Translational Modifications from Local Sequence Fragments Using Machine Learning Algorithms: Overview and Best Practices
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    Chapter 20 CX, DPX, and PCW: Web Servers for the Visualization of Interior and Protruding Regions of Protein Structures in 3D and 1D
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    Chapter 21 Erratum to: One-Dimensional Structural Properties of Proteins in the Coarse-Grained CABS Model
Attention for Chapter 4: Predicting Protein Secondary Structure Using Consensus Data Mining (CDM) Based on Empirical Statistics and Evolutionary Information
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Chapter title
Predicting Protein Secondary Structure Using Consensus Data Mining (CDM) Based on Empirical Statistics and Evolutionary Information
Chapter number 4
Book title
Prediction of Protein Secondary Structure
Published in
Methods in molecular biology, January 2017
DOI 10.1007/978-1-4939-6406-2_4
Pubmed ID
Book ISBNs
978-1-4939-6404-8, 978-1-4939-6406-2
Authors

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

Abstract

Predicting the secondary structure of a protein from its sequence still remains a challenging problem. The prediction accuracies remain around 80 %, and for very diverse methods. Using evolutionary information and machine learning algorithms in particular has had the most impact. In this chapter, we will first define secondary structures, then we will review the Consensus Data Mining (CDM) technique based on the robust GOR algorithm and Fragment Database Mining (FDM) approach. GOR V is an empirical method utilizing a sliding window approach to model the secondary structural elements of a protein by making use of generalized evolutionary information. FDM uses data mining from experimental structure fragments, and is able to successfully predict the secondary structure of a protein by combining experimentally determined structural fragments based on sequence similarities of the fragments. The CDM method combines predictions from GOR V and FDM in a hierarchical manner to produce consensus predictions for secondary structure. In other words, if sequence fragment are not available, then it uses GOR V to make the secondary structure prediction. The online server of CDM is available at http://gor.bb.iastate.edu/cdm/ .

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 10 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 10 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 50%
Researcher 1 10%
Student > Master 1 10%
Unknown 3 30%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 3 30%
Agricultural and Biological Sciences 2 20%
Unknown 5 50%