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Intrinsically Disordered Proteins Studied by NMR Spectroscopy

Overview of attention for book
Intrinsically Disordered Proteins Studied by NMR Spectroscopy
Springer International Publishing

Table of Contents

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    Book Overview
  2. Altmetric Badge
    Chapter 1 Back to the Future: Nuclear Magnetic Resonance and Bioinformatics Studies on Intrinsically Disordered Proteins.
  3. Altmetric Badge
    Chapter 2 Structure and Dynamics of Intrinsically Disordered Proteins.
  4. Altmetric Badge
    Chapter 3 NMR Methods for the Study of Instrinsically Disordered Proteins Structure, Dynamics, and Interactions: General Overview and Practical Guidelines
  5. Altmetric Badge
    Chapter 4 Ensemble Calculation for Intrinsically Disordered Proteins Using NMR Parameters
  6. Altmetric Badge
    Chapter 5 NMR Spectroscopic Studies of the Conformational Ensembles of Intrinsically Disordered Proteins.
  7. Altmetric Badge
    Chapter 6 Recombinant Intrinsically Disordered Proteins for NMR: Tips and Tricks.
  8. Altmetric Badge
    Chapter 7 Biophysical Methods to Investigate Intrinsically Disordered Proteins: Avoiding an "Elephant and Blind Men" Situation.
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    Chapter 8 Application of SAXS for the Structural Characterization of IDPs
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    Chapter 9 Bioinformatics Approaches for Predicting Disordered Protein Motifs
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    Chapter 10 Towards Understanding Protein Disorder In-Cell
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    Chapter 11 The Protein Ensemble Database
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    Chapter 12 Order and Disorder in the Replicative Complex of Paramyxoviruses.
  14. Altmetric Badge
    Chapter 13 Druggability of Intrinsically Disordered Proteins.
  15. Altmetric Badge
    Chapter 14 Beta Amyloid Hallmarks: From Intrinsically Disordered Proteins to Alzheimer's Disease.
Attention for Chapter 9: Bioinformatics Approaches for Predicting Disordered Protein Motifs
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (51st percentile)
  • Good Attention Score compared to outputs of the same age and source (70th percentile)

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Chapter title
Bioinformatics Approaches for Predicting Disordered Protein Motifs
Chapter number 9
Book title
Intrinsically Disordered Proteins Studied by NMR Spectroscopy
Published in
Advances in experimental medicine and biology, January 2015
DOI 10.1007/978-3-319-20164-1_9
Pubmed ID
Book ISBNs
978-3-31-920163-4, 978-3-31-920164-1
Authors

Bhowmick, Pallab, Guharoy, Mainak, Tompa, Peter, Pallab Bhowmick, Mainak Guharoy, Peter Tompa

Abstract

Short, linear motifs (SLiMs) in proteins are functional microdomains consisting of contiguous residue segments along the protein sequence, typically not more than 10 consecutive amino acids in length with less than 5 defined positions. Many positions are 'degenerate' thus offering flexibility in terms of the amino acid types allowed at those positions. Their short length and degenerate nature confers evolutionary plasticity meaning that SLiMs often evolve convergently. Further, SLiMs have a propensity to occur within intrinsically unstructured protein segments and this confers versatile functionality to unstructured regions of the proteome. SLiMs mediate multiple types of protein interactions based on domain-peptide recognition and guide functions including posttranslational modifications, subcellular localization of proteins, and ligand binding. SLiMs thus behave as modular interaction units that confer versatility to protein function and SLiM-mediated interactions are increasingly being recognized as therapeutic targets. In this chapter we start with a brief description about the properties of SLiMs and their interactions and then move on to discuss algorithms and tools including several web-based methods that enable the discovery of novel SLiMs (de novo motif discovery) as well as the prediction of novel occurrences of known SLiMs. Both individual amino acid sequences as well as sets of protein sequences can be scanned using these methods to obtain statistically overrepresented sequence patterns. Lists of putatively functional SLiMs are then assembled based on parameters such as evolutionary sequence conservation, disorder scores, structural data, gene ontology terms and other contextual information that helps to assess the functional credibility or significance of these motifs. These bioinformatics methods should certainly guide experiments aimed at motif discovery.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 25 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 24%
Researcher 3 12%
Lecturer > Senior Lecturer 2 8%
Student > Master 2 8%
Student > Bachelor 1 4%
Other 2 8%
Unknown 9 36%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 11 44%
Agricultural and Biological Sciences 3 12%
Computer Science 1 4%
Physics and Astronomy 1 4%
Unknown 9 36%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 26 September 2015.
All research outputs
#13,213,964
of 22,829,083 outputs
Outputs from Advances in experimental medicine and biology
#1,822
of 4,952 outputs
Outputs of similar age
#168,891
of 353,128 outputs
Outputs of similar age from Advances in experimental medicine and biology
#76
of 272 outputs
Altmetric has tracked 22,829,083 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,952 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.0. This one has gotten more attention than average, scoring higher than 62% 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 353,128 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 51% of its contemporaries.
We're also able to compare this research output to 272 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 70% of its contemporaries.