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Document Figure 3 Stereo view of IFN-β crystal structure [2]. Page 444 The structure of IFN-τ was also examined by CD [10]. Analysis of the IFN-τ spectra predicts that the secondary structural elements derived from CD spectra indicate approximately 70% α-helix. The remainder of the molecule is either predicted to be random or a combination of β sheet and turn. Since it is known that algorithms that predict secondary structures from CD spectra are most accurate at identifying α helices, we are confident that IFN-τ is mainly α helical. The CD spectra for the synthetic peptides of IFN-τ were also obtained. The peptides IFN-τ(1–37), IFN-τ(62–92), IFN-τ(119–150), and IFN-τ(139–172) all show the presence of α helix, while IFN-τ(34–64) and IFN-τ(90–122) are mainly random. The presence of an α helix in the peptides supports the CD analysis of the intact protein and also roughly indicates the location of helical segments. The secondary structure of IFN-τ, including the location of the α helices and loop region, was then predicted using a neural network-based computer program called PHD that relies on sequence alignments of all proteins related to the target sequence [25,26]. When this prediction is correlated with the CD data, peptides that possess considerable α helicity are predicted to contain entire helical segments, and conversely, peptides with little helicity are predicted to be within loop regions. A model of the 3-D structure of IFN-τ was constructed using a distance geometry-based homology modeling method with mouse IFN-β acting as a template. The distance constraints were generated between residues within IFN-τ that are homologous to residues of IFN-β. Dihedral-angle restraints of α helices were generated from the secondary-structure prediction of IFN-τ. No constraints were applied to the 13-residue carboxy tail of IFN-τ, which is absent in IFN-β, since it is likely to be flexible in a manner similar to other proteins such as IFN-γ. Additional distance constraints were added from putative disul- http://legacy.netlibrary.com/nlreader/nlReader.dll?bookid=12640&filename=Page_444.html (1 of 2) [4/9/2004 12:11:24 AM]
Document Figure 4 Stereo view of IFN-τ model. Highlighted sequences are from 1–37, 62–92, and 139–172. Page 445 fide bridges between residues 1 and 99 and residues 29 and 139. Several structures were generated using distance geometry routines, and the energy was minimized and averaged to yield a final model [16]. A similar model was built by Senda et al. (unpublished results) using a homology modeling method. This model was also built using the x-ray coordinates of IFN-β and shows a similar topology to the IFN-β three-dimensional structure (Figure 4). The most striking feature of both models is that those discontinuous regions, previously determined to be functionally important, are localized to one side of the molecule and found to be spatially contiguous (Figure 4). This observation is consistent with multiple binding sites on IFN-τ interacting simultaneously with the Type I IFN receptor and emphasizes the importance of structural modeling in the understanding and interpretation of functional data. III. Type II IFN A. Functional Sites on the IFN-γ Molecule The production of IFN-γ-neutralizing antibodies specific for an N-terminal peptide of human IFN-γ provided the first evidence that the N-terminus of IFN-γ contained an important functional site [27]. A similar approach was used to produce N-terminus-specific neutralizing antisera against murine IFN-γ [28]. Subsequent studies using IFN-γ synthetic peptides to map the epitope specificity of monoclonal antibodies to murine IFN-γ showed that N-terminal specific monoclonal antibodies neutralize IFN-γ antiviral activity [29]. In receptor- http://legacy.netlibrary.com/nlreader/nlReader.dll?bookid=12640&filename=Page_445.html [4/9/2004 12:11:30 AM]
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