Abstract

Mutations in the aryl hydrocarbon receptor interacting protein-like 1 (AIPL1) cause the devastating blinding disease Leber’s congenital amaurosis (LCA) (Sohocki et al., 2000a). Up to 12% of recessive LCA is caused by mutations in AIPL1 (Sohocki et al., 2000b). In addition to AIPL1, LCA-causing mutations have also been identified in RetGC1, RPE65, CRX, LRAT, CRB1 and RPGRIP1, and a further two loci have been identified on 14q24 and 6q11–16 (www.retina-international.org/sci-news/mutation.htm). Although the function of AIPL1 is unknown, AIPL1 shares 49% identity with the human aryl hydrocarbon receptor (AhR)-interacting protein (AIP), also named XAP2 or ARA9 (Sohocki et al., 2000a). AIP in turn shares similarity with members of the immunophilin family of proteins including the co-chaperones FK506-binding protein (FKBP) 51 and 52 (reviewed in Chapple et al., 2001; van der Spuy and Cheetham, 2004a). Both AIP and the FKBP co-chaperones exist in a cytosolic ternary complex with the molecular chaperone Hsp90 and a specific cognate receptor, and have been shown to regulate the nuclear translocation and transactivation of the associated receptor. At the primary structural level, the tetratricopeptide repeat (TPR) motif is conserved in AIPL1, AIP and FKBP51/52. The TPR motif is an evolutionary and functionally conserved but degenerate motif found in a number of structurally unrelated proteins and mediates the binding of specific protein-interaction partners. The TPR motif in both AIP and FKBP51/52 form a TPR carboxylate clamp that mediates their interaction with the C-terminal MEEVD TPR acceptor site of Hsp90. The similarity of AIPL1 to AIP has led to suggestions that AIPL1 could function in a similar manner to AIP in facilitating protein translocation and as a component of chaperone complexes.KeywordsAryl Hydrocarbon ReceptorOuter Nuclear LayerCone PhotoreceptorRetinal Cell TypeFarnesylated ProteinThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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