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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
  2. Altmetric Badge
    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 14: Prediction of Disordered RNA, DNA, and Protein Binding Regions Using DisoRDPbind
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Chapter title
Prediction of Disordered RNA, DNA, and Protein Binding Regions Using DisoRDPbind
Chapter number 14
Book title
Prediction of Protein Secondary Structure
Published in
Methods in molecular biology, January 2017
DOI 10.1007/978-1-4939-6406-2_14
Pubmed ID
Book ISBNs
978-1-4939-6404-8, 978-1-4939-6406-2
Authors

Zhenling Peng, Chen Wang, Vladimir N. Uversky, Lukasz Kurgan

Abstract

Intrinsically disordered proteins and regions (IDPs and IDRs) are involved in a wide range of cellular functions and they often facilitate interactions with RNAs, DNAs, and proteins. Although many computational methods can predict IDPs and IDRs in protein sequences, only a few methods predict their functions and these functions primarily concern protein binding. We describe how to use the first computational method DisoRDPbind for high-throughput prediction of multiple functions of disordered regions. Our method predicts the RNA-, DNA-, and protein-binding residues located in IDRs in the input protein sequences. DisoRDPbind provides accurate predictions and is sufficiently fast to make predictions for full genomes. Our method is implemented as a user-friendly webserver that is freely available at http://biomine.ece.ualberta.ca/DisoRDPbind/ . We overview our predictor, discuss how to run the webserver, and show how to interpret the corresponding results. We also demonstrate the utility of our method based on two case studies, human BRCA1 protein that binds various proteins and DNA, and yeast 60S ribosomal protein L4 that interacts with proteins and RNA.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 30 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 30 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 30%
Student > Master 3 10%
Researcher 2 7%
Professor 2 7%
Student > Doctoral Student 1 3%
Other 4 13%
Unknown 9 30%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 10 33%
Agricultural and Biological Sciences 4 13%
Computer Science 2 7%
Medicine and Dentistry 2 7%
Unspecified 1 3%
Other 1 3%
Unknown 10 33%