Hedgehog (Hh) plays crucial roles in tissue-patterning and activates signaling in Patched (Ptc)-expressing cells. Paracrine signaling requires release and transport over many cell diameters away by a process that requires interaction with heparan sulfate proteoglycans (HSPGs). Here, we examine the organization of functional, fluorescently tagged variants in living cells by using optical imaging, FRET microscopy, and mutational studies guided by bioinformatics prediction. We find that cell-surface Hh forms suboptical oligomers, further concentrated in visible clusters colocalized with HSPGs. Mutation of a conserved Lys in a predicted Hh-protomer interaction interface results in an autocrine signaling-competent Hh isoform--incapable of forming dense nanoscale oligomers, interacting with HSPGs, or paracrine signaling. Thus, Hh exhibits a hierarchical organization from the nanoscale to visible clusters with distinct functions.

Structure prediction and three-dimensional modeling of disulfide-rich systems are challenging due to the limited number of such folds in the structural databank. We exploit the stereochemical compatibility of substructures in known protein structures to accommodate disulfide bonds in predicting the structures of disulfide-rich polypeptides directly from disulfide connectivity pattern and amino acid sequence in the absence of structural homologs and any other structural information. This knowledge-based approach is illustrated using structure prediction of 40 nonredundant bioactive disulfide-rich polypeptides such as toxins, growth factors, and endothelins available in the structural databank. The polypeptide conformation could be predicted in 35 out of 40 nonredundant entries (87%). Nonhomologous templates could be identified and models could be obtained within 2 A deviation from the query in 29 peptides (72%). This procedure can be accessed from the World Wide Web (http://www.ncbs.res.in/ approximately faculty/mini/dsdbase/dsdbase.html).