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CorNet : assigning function to networks of co-evolving residues by automated literature mining

(2017) PLOS ONE. 12(5).
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Abstract
CorNet is a web-based tool for the analysis of co-evolving residue positions in protein superfamily sequence alignments. CorNet projects external information such as mutation data extracted from literature on interactively displayed groups of co-evolving residue positions to shed light on the functions associated with these groups and the residues in them. We used CorNet to analyse six enzyme super-families and found that groups of strongly co-evolving residues tend to consist of residues involved in a same function such as activity, specificity, co-factor binding, or enantioselectivity. This finding allows to assign a function to residues for which no data is available yet in the literature. A mutant library was designed to mutate residues observed in a group of co-evolving residues predicted to be involved in enantioselectivity, but for which no literature data is available yet. The resulting set of mutations indeed showed many instances of increased enantioselectivity.
Keywords
CORRELATED MUTATION ANALYSES, PROTEIN-STRUCTURE PREDICTION, OCEAN SAMPLING EXPEDITION, DIRECTED EVOLUTION, FAMILIES, ENANTIOSELECTIVITY, IDENTIFICATION, FLUORESCENS, SUPERFAMILY, ALIGNMENTS

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Chicago
van den Bergh, Tom, Giorgio Tamo, Alberto Nobili, Yifeng Tao, Tianwei Tan, Uwe T Bornscheuer, Remko KP Kuipers, et al. 2017. “CorNet : Assigning Function to Networks of Co-evolving Residues by Automated Literature Mining.” Plos One 12 (5).
APA
van den Bergh, T., Tamo, G., Nobili, A., Tao, Y., Tan, T., Bornscheuer, U. T., Kuipers, R. K., et al. (2017). CorNet : assigning function to networks of co-evolving residues by automated literature mining. PLOS ONE, 12(5).
Vancouver
1.
van den Bergh T, Tamo G, Nobili A, Tao Y, Tan T, Bornscheuer UT, et al. CorNet : assigning function to networks of co-evolving residues by automated literature mining. PLOS ONE. 2017;12(5).
MLA
van den Bergh, Tom, Giorgio Tamo, Alberto Nobili, et al. “CorNet : Assigning Function to Networks of Co-evolving Residues by Automated Literature Mining.” PLOS ONE 12.5 (2017): n. pag. Print.
@article{8545998,
  abstract     = {CorNet is a web-based tool for the analysis of co-evolving residue positions in protein superfamily sequence alignments. CorNet projects external information such as mutation data extracted from literature on interactively displayed groups of co-evolving residue positions to shed light on the functions associated with these groups and the residues in them. We used CorNet to analyse six enzyme super-families and found that groups of strongly co-evolving residues tend to consist of residues involved in a same function such as activity, specificity, co-factor binding, or enantioselectivity. This finding allows to assign a function to residues for which no data is available yet in the literature. A mutant library was designed to mutate residues observed in a group of co-evolving residues predicted to be involved in enantioselectivity, but for which no literature data is available yet. The resulting set of mutations indeed showed many instances of increased enantioselectivity.},
  articleno    = {e0176427},
  author       = {van den Bergh, Tom and Tamo, Giorgio and Nobili, Alberto and Tao, Yifeng and Tan, Tianwei and Bornscheuer, Uwe T and Kuipers, Remko KP and Vroling, Bas and de Jong, Ren{\'e} M and Subramanian, Kalyanasundaram and Schaap, Peter J and Desmet, Tom and Nidetzky, Bernd and Vriend, Gert and Joosten, Henk-Jan},
  issn         = {1932-6203},
  journal      = {PLOS ONE},
  keyword      = {CORRELATED MUTATION ANALYSES,PROTEIN-STRUCTURE PREDICTION,OCEAN SAMPLING EXPEDITION,DIRECTED EVOLUTION,FAMILIES,ENANTIOSELECTIVITY,IDENTIFICATION,FLUORESCENS,SUPERFAMILY,ALIGNMENTS},
  language     = {eng},
  number       = {5},
  pages        = {19},
  title        = {CorNet : assigning function to networks of co-evolving residues by automated literature mining},
  url          = {http://dx.doi.org/10.1371/journal.pone.0176427},
  volume       = {12},
  year         = {2017},
}

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