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Structure-Based Function Prediction of Uncharacterized Protein Using Binding Sites Comparison

Overview of attention for article published in PLoS Computational Biology, November 2013
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Title
Structure-Based Function Prediction of Uncharacterized Protein Using Binding Sites Comparison
Published in
PLoS Computational Biology, November 2013
DOI 10.1371/journal.pcbi.1003341
Pubmed ID
Authors

Janez Konc, Milan Hodošček, Mitja Ogrizek, Joanna Trykowska Konc, Dušanka Janežič

Abstract

A challenge in structural genomics is prediction of the function of uncharacterized proteins. When proteins cannot be related to other proteins of known activity, identification of function based on sequence or structural homology is impossible and in such cases it would be useful to assess structurally conserved binding sites in connection with the protein's function. In this paper, we propose the function of a protein of unknown activity, the Tm1631 protein from Thermotoga maritima, by comparing its predicted binding site to a library containing thousands of candidate structures. The comparison revealed numerous similarities with nucleotide binding sites including specifically, a DNA-binding site of endonuclease IV. We constructed a model of this Tm1631 protein with a DNA-ligand from the newly found similar binding site using ProBiS, and validated this model by molecular dynamics. The interactions predicted by the Tm1631-DNA model corresponded to those known to be important in endonuclease IV-DNA complex model and the corresponding binding free energies, calculated from these models were in close agreement. We thus propose that Tm1631 is a DNA binding enzyme with endonuclease activity that recognizes DNA lesions in which at least two consecutive nucleotides are unpaired. Our approach is general, and can be applied to any protein of unknown function. It might also be useful to guide experimental determination of function of uncharacterized proteins.

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Geographical breakdown

Country Count As %
France 1 1%
Germany 1 1%
Argentina 1 1%
Unknown 77 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 16 20%
Student > Ph. D. Student 15 19%
Student > Master 12 15%
Student > Bachelor 7 9%
Professor > Associate Professor 6 8%
Other 14 18%
Unknown 10 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 32 40%
Biochemistry, Genetics and Molecular Biology 15 19%
Computer Science 9 11%
Engineering 4 5%
Neuroscience 2 3%
Other 6 8%
Unknown 12 15%