4.4 Article

Mapping peptide hormone-receptor interactions using a disulfide-trapping approach

期刊

BIOCHEMISTRY
卷 47, 期 22, 页码 5889-5895

出版社

AMER CHEMICAL SOC
DOI: 10.1021/bi800122f

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资金

  1. NIDDK NIH HHS [R01 DK047940-13, DK47490, R01 DK047940-12, R01 DK047940-11] Funding Source: Medline
  2. NIGMS NIH HHS [GM54082, R01 GM054082, R29 GM054082] Funding Source: Medline

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Efforts to elucidate the nature of the bimolecular interaction of parathyroid hormone (PTH) with its cognate receptor, the PTH receptor type 1 (PTHR1), have relied heavily on benzoylphenylalanine(Bpa-) based photoaffinity cross-linking. However, given the flexibility, size, and shape of Bpa, the resolution at the PTH-PTHR1 interface appears to be reaching the limit of this technique. Here we employ a disulfide-trapping approach developed by others primarily for use in screening compound libraries to identify novel ligands. In this method, cysteine substitutions are introduced into a specific site within the ligand and a region in the receptor predicted to interact with each other. Upon ligand binding, if these cysteines are in close proximity, they form a disulfide bond. Since the geometry governing disulfide bond formation is more constrained than Bpa cross-linking, this novel approach can be employed to generate a more refined molecular model of the PTH-PTHR1 complex. Using a PTH analogue containing a cysteine at position 1, we probed 24 sites and identified 4 in PTHR1 to which cross-linking occurred. Importantly, previous photoaffinity cross-linking studies using a PTH analogue with Bpa at position 1 only identified a single interaction site. The new sites identified by the disulfide-trapping procedure were used as constraints in molecular dynamics simulations to generate an updated model of the PTH-PTHR1 complex. Mapping by disulfide trapping extends and complements photoaffinity cross-linking. It is applicable to other peptide-receptor interfaces and should yield insights about yet unknown sites of ligand-receptor interactions, allowing for generation of more refined models.

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