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Structural Basis of High‐Affinity Nuclear Localization Signal Interactions with Importin‐α

Overview of attention for article published in Traffic, February 2012
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Title
Structural Basis of High‐Affinity Nuclear Localization Signal Interactions with Importin‐α
Published in
Traffic, February 2012
DOI 10.1111/j.1600-0854.2012.01329.x
Pubmed ID
Authors

Mary Marfori, Thierry G. Lonhienne, Jade K. Forwood, Bostjan Kobe

Abstract

Classical nuclear localization signals (cNLSs), comprising one (monopartite cNLSs) or two clusters of basic residues connected by a 10-12 residue linker (bipartite cNLSs), are recognized by the nuclear import factor importin-α. The cNLSs bind along a concave groove on importin-α; however, specificity determinants of cNLSs remain poorly understood. We present a structural and interaction analysis study of importin-α binding to both designed and naturally occurring high-affinity cNLS-like sequences; the peptide inhibitors Bimax1 and Bimax2, and cNLS peptides of cap-binding protein 80. Our data suggest that cNLSs and cNLS-like sequences can achieve high affinity through maximizing interactions at the importin-α minor site, and by taking advantage of multiple linker region interactions. Our study defines an extended set of binding cavities on the importin-α surface, and also expands on recent observations that longer linker sequences are allowed, and that long-range electrostatic complementarity can contribute to cNLS-binding affinity. Altogether, our study explains the molecular and structural basis of the results of a number of recent studies, including systematic mutagenesis and peptide library approaches, and provides an improved level of understanding on the specificity determinants of a cNLS. Our results have implications for identifying cNLSs in novel proteins.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 56 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 2%
Unknown 55 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 16 29%
Researcher 10 18%
Student > Doctoral Student 5 9%
Student > Master 5 9%
Student > Bachelor 4 7%
Other 6 11%
Unknown 10 18%
Readers by discipline Count As %
Agricultural and Biological Sciences 20 36%
Biochemistry, Genetics and Molecular Biology 19 34%
Computer Science 3 5%
Linguistics 1 2%
Physics and Astronomy 1 2%
Other 1 2%
Unknown 11 20%
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 17 January 2012.
All research outputs
#20,097,309
of 24,704,144 outputs
Outputs from Traffic
#1,070
of 1,258 outputs
Outputs of similar age
#207,001
of 257,103 outputs
Outputs of similar age from Traffic
#15
of 23 outputs
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