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Sequence-specific backbone resonance assignments and microsecond timescale molecular dynamics simulation of human eosinophil-derived neurotoxin
Authors:Donald Gagné  Chitra Narayanan  Khushboo Bafna  Laurie-Anne Charest  Pratul K. Agarwal  Nicolas Doucet
Affiliation:1.INRS-Institut Armand-Frappier,Université du Québec,Laval,Canada;2.Graduate School of Genome Science and Technology,University of Tennessee,Knoxville,USA;3.Computational Biology Institute and Computer Science and Mathematics Division,Oak Ridge National Laboratory,Oak Ridge,USA;4.Department of Biochemistry, Cellular and Molecular Biology,University of Tennessee,Knoxville,USA;5.PROTEO, The Québec Network for Research on Protein Function, Engineering, and Applications,Université Laval,Quebec,Canada;6.GRASP, The Groupe de recherche Axé sur la Structure des Protéines,McGill University,Montreal,Canada;7.Structural Biology Initiative,CUNY Advanced Science Research Center,New York,USA
Abstract:Eight active canonical members of the pancreatic-like ribonuclease A (RNase A) superfamily have been identified in human. All structural homologs share similar RNA-degrading functions, while also cumulating other various biological activities in different tissues. The functional homologs eosinophil-derived neurotoxin (EDN, or RNase 2) and eosinophil cationic protein (ECP, or RNase 3) are known to be expressed and secreted by eosinophils in response to infection, and have thus been postulated to play an important role in host defense and inflammatory response. We recently initiated the biophysical and dynamical investigation of several vertebrate RNase homologs and observed that clustering residue dynamics appear to be linked with the phylogeny and biological specificity of several members. Here we report the 1H, 13C and 15N backbone resonance assignments of human EDN (RNase 2) and its molecular dynamics simulation on the microsecond timescale, providing means to pursue this comparative atomic-scale functional and dynamical analysis by NMR and computation over multiple time frames.
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