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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 11: How to Predict Disorder in a Protein of Interest
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Citations

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Chapter title
How to Predict Disorder in a Protein of Interest
Chapter number 11
Book title
Prediction of Protein Secondary Structure
Published in
Methods in molecular biology, January 2017
DOI 10.1007/978-1-4939-6406-2_11
Pubmed ID
27787825
Book ISBNs
978-1-4939-6404-8, 978-1-4939-6406-2
Authors

Vladimir N. Uversky

Abstract

Currently available computational tools, which are many, provide a researcher with the multitude of options for prediction of intrinsic disorder in a protein of interest and for finding at least some of its disorder-based functions. This chapter provides a highly subjective guideline on how not to be lost in the "dark forest" of available tools for the analysis of intrinsic disorder. By no means it gives a unique pathway through this forest, but simply presents some of the tools the author uses in his everyday research.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 14 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 14 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 3 21%
Student > Master 2 14%
Student > Ph. D. Student 2 14%
Lecturer > Senior Lecturer 1 7%
Other 1 7%
Other 2 14%
Unknown 3 21%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 5 36%
Engineering 2 14%
Chemical Engineering 1 7%
Computer Science 1 7%
Agricultural and Biological Sciences 1 7%
Other 2 14%
Unknown 2 14%

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