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Prediction of Protein Secondary Structure

Overview of attention for book
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
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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
  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 8: One-Dimensional Structural Properties of Proteins in the Coarse-Grained CABS Model
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Chapter title
One-Dimensional Structural Properties of Proteins in the Coarse-Grained CABS Model
Chapter number 8
Book title
Prediction of Protein Secondary Structure
Published in
Methods in molecular biology, January 2017
DOI 10.1007/978-1-4939-6406-2_8
Pubmed ID
Book ISBNs
978-1-4939-6404-8, 978-1-4939-6406-2
Authors

Sebastian Kmiecik, Andrzej Kolinski

Abstract

Despite the significant increase in computational power, molecular modeling of protein structure using classical all-atom approaches remains inefficient, at least for most of the protein targets in the focus of biomedical research. Perhaps the most successful strategy to overcome the inefficiency problem is multiscale modeling to merge all-atom and coarse-grained models. This chapter describes a well-established CABS coarse-grained protein model. The CABS (C-Alpha, C-Beta, and Side chains) model assumes a 2-4 united-atom representation of amino acids, knowledge-based force field (derived from the statistical regularities seen in known protein sequences and structures) and efficient Monte Carlo sampling schemes (MC dynamics, MC replica-exchange, and combinations). A particular emphasis is given to the unique design of the CABS force-field, which is largely defined using one-dimensional structural properties of proteins, including protein secondary structure. This chapter also presents CABS-based modeling methods, including multiscale tools for de novo structure prediction, modeling of protein dynamics and prediction of protein-peptide complexes. CABS-based tools are freely available at http://biocomp.chem.uw.edu.pl/tools.

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X Demographics

The data shown below were collected from the profiles of 3 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 19 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Japan 1 5%
France 1 5%
Unknown 17 89%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 32%
Student > Ph. D. Student 4 21%
Student > Master 2 11%
Professor 1 5%
Other 1 5%
Other 0 0%
Unknown 5 26%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 4 21%
Agricultural and Biological Sciences 3 16%
Chemistry 3 16%
Computer Science 2 11%
Physics and Astronomy 1 5%
Other 0 0%
Unknown 6 32%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 01 December 2015.
All research outputs
#14,073,810
of 22,990,068 outputs
Outputs from Methods in molecular biology
#3,960
of 13,150 outputs
Outputs of similar age
#223,356
of 421,126 outputs
Outputs of similar age from Methods in molecular biology
#346
of 1,074 outputs
Altmetric has tracked 22,990,068 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,150 research outputs from this source. They receive a mean Attention Score of 3.4. This one has gotten more attention than average, scoring higher than 68% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 421,126 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1,074 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 66% of its contemporaries.