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Prediction of Protein Secondary Structure

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Cover of 'Prediction of Protein Secondary Structure'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Where the Name “GOR” Originates: A Story
  3. Altmetric Badge
    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
  14. Altmetric Badge
    Chapter 13 Prediction of Protein Secondary Structure
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    Chapter 14 Prediction of Disordered RNA, DNA, and Protein Binding Regions Using DisoRDPbind
  16. Altmetric Badge
    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
  18. Altmetric Badge
    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
  20. Altmetric Badge
    Chapter 19 Predicting Post-Translational Modifications from Local Sequence Fragments Using Machine Learning Algorithms: Overview and Best Practices
  21. Altmetric Badge
    Chapter 20 CX, DPX, and PCW: Web Servers for the Visualization of Interior and Protruding Regions of Protein Structures in 3D and 1D
  22. Altmetric Badge
    Chapter 21 Erratum to: One-Dimensional Structural Properties of Proteins in the Coarse-Grained CABS Model
Attention for Chapter 13: Prediction of Protein Secondary Structure
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Chapter title
Prediction of Protein Secondary Structure
Chapter number 13
Book title
Prediction of Protein Secondary Structure
Published in
Methods in molecular biology, January 2017
DOI 10.1007/978-1-4939-6406-2_13
Pubmed ID
Book ISBNs
978-1-4939-6404-8, 978-1-4939-6406-2
Authors

Peterson, Lenna, Jamroz, Michal, Kolinski, Andrzej, Kihara, Daisuke, Lenna Peterson, Michal Jamroz, Andrzej Kolinski, Daisuke Kihara

Abstract

The conventional view of a protein structure as static provides only a limited picture. There is increasing evidence that protein dynamics are often vital to protein function including interaction with partners such as other proteins, nucleic acids, and small molecules. Considering flexibility is also important in applications such as computational protein docking and protein design. While residue flexibility is partially indicated by experimental measures such as the B-factor from X-ray crystallography and ensemble fluctuation from nuclear magnetic resonance (NMR) spectroscopy as well as computational molecular dynamics (MD) simulation, these techniques are resource-intensive. In this chapter, we describe the web server and stand-alone version of FlexPred, which rapidly predicts absolute per-residue fluctuation from a three-dimensional protein structure. On a set of 592 nonredundant structures, comparing the fluctuations predicted by FlexPred to the observed fluctuations in MD simulations showed an average correlation coefficient of 0.669 and an average root mean square error of 1.07 Å. FlexPred is available at http://kiharalab.org/flexPred/ .

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X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Poland 1 8%
Unknown 11 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 42%
Researcher 3 25%
Student > Bachelor 2 17%
Professor 1 8%
Unspecified 1 8%
Other 0 0%
Readers by discipline Count As %
Chemistry 3 25%
Agricultural and Biological Sciences 3 25%
Computer Science 2 17%
Unspecified 1 8%
Psychology 1 8%
Other 1 8%
Unknown 1 8%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 29 October 2016.
All research outputs
#15,692,595
of 23,318,744 outputs
Outputs from Methods in molecular biology
#5,494
of 13,323 outputs
Outputs of similar age
#259,035
of 422,873 outputs
Outputs of similar age from Methods in molecular biology
#471
of 1,076 outputs
Altmetric has tracked 23,318,744 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,323 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 44th percentile – i.e., 44% of its peers scored the same or lower than it.
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