5 research outputs found

    Relative and regional stabilities of the hamster, mouse, rabbit, and bovine prion proteins toward urea unfolding assessed by nuclear magnetic resonance and circular dichroism spectroscopies

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    The residue-specific urea-induced unfolding patterns of recombinant prion proteins from different species (bovine, rabbit, mouse, and Syrian hamster) were monitored using high-resolution 1H nuclear magnetic resonance (NMR) spectroscopy. Protein constructs of different lengths, and with and without a His tag attached at the N-terminus, were studied. The various species showed different overall sensitivities toward urea denaturation with stabilities in the following order: hamster 64 mouse < rabbit < bovine protein. This order is in agreement with recent circular dichroism (CD) spectroscopic measurements for several species [Khan, M. Q. (2010) Proc. Natl. Acad. Sci. U.S.A.107, 19808-19813] and for the bovine protein presented herein. The [urea] 1/2 values determined by CD spectroscopy parallel those of the most stable residues observed by NMR spectroscopy. Neither the longer constructs containing an additional hydrophobic region nor the His tag influenced the stability of the structured domain of the constructs studied. The effect of the S174N mutation in rabbit PrP C was also investigated. The rank order of the regional stabilities within each protein remained the same for all species. In particular, the residues in the \u3b2-sheet region in all four species were more sensitive to urea-induced unfolding than residues in the \u3b12 and \u3b13 helical regions. These observations indicate that the regional specific unfolding pattern is the same for the four mammalian prion proteins studied but militate against the idea that PrP Sc formation is linked with the global stability of PrP C. \ua9 2011 American Chemical Society.Peer reviewed: YesNRC publication: Ye

    The Inhibition of Polysialyltranseferase ST8SiaIV Through Heparin Binding to Polysialyltransferase Domain (PSTD)

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    Identifying Cancer Targets Based on Machine Learning Methods via Chou’s 5-steps Rule and General Pseudo Components

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