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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 9: Assessing Predicted Contacts for Building Protein Three-Dimensional Models
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Chapter title
Assessing Predicted Contacts for Building Protein Three-Dimensional Models
Chapter number 9
Book title
Prediction of Protein Secondary Structure
Published in
Methods in molecular biology, January 2017
DOI 10.1007/978-1-4939-6406-2_9
Pubmed ID
Book ISBNs
978-1-4939-6404-8, 978-1-4939-6406-2
Authors

Badri Adhikari, Debswapna Bhattacharya, Renzhi Cao, Jianlin Cheng

Abstract

Recent successes of contact-guided protein structure prediction methods have revived interest in solving the long-standing problem of ab initio protein structure prediction. With homology modeling failing for many protein sequences that do not have templates, contact-guided structure prediction has shown promise, and consequently, contact prediction has gained a lot of interest recently. Although a few dozen contact prediction tools are already currently available as web servers and downloadables, not enough research has been done towards using existing measures like precision and recall to evaluate these contacts with the goal of building three-dimensional models. Moreover, when we do not have a native structure for a set of predicted contacts, the only analysis we can perform is a simple contact map visualization of the predicted contacts. A wider and more rigorous assessment of the predicted contacts is needed, in order to build tertiary structure models. This chapter discusses instructions and protocols for using tools and applying techniques in order to assess predicted contacts for building three-dimensional models.

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 %
Germany 1 10%
Unknown 9 90%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 20%
Researcher 2 20%
Unspecified 1 10%
Professor 1 10%
Librarian 1 10%
Other 2 20%
Unknown 1 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 4 40%
Computer Science 3 30%
Biochemistry, Genetics and Molecular Biology 1 10%
Medicine and Dentistry 1 10%
Unknown 1 10%