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Probing conformational disorder in neurotensin by two-dimensional solid-state NMR and comparison to molecular dynamics simulations.

ABSTRACT: An approach is introduced to characterize conformational ensembles of intrinsically unstructured peptides on the atomic level using two-dimensional solid-state NMR data and their combination with molecular dynamics simulations. For neurotensin, a peptide that binds with high affinity to a G-protein coupled receptor, this method permits the investigation of the changes in conformational preferences of a neurotransmitter transferred from a frozen aqueous solution via a lipid model phase to the receptor-bound form. The results speak against a conformational pre-organization of the ligand in detergents in which the receptor has been shown to be functional. Further extensions to the study of protein folding are possible.

SUBMITTER: Heise H

PROVIDER: S-EPMC1366713 | biostudies-literature | 2005 Sep

REPOSITORIES: biostudies-literature

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Publications

Probing conformational disorder in neurotensin by two-dimensional solid-state NMR and comparison to molecular dynamics simulations.

Heise Henrike H Luca Sorin S de Groot Bert L BL Grubmüller Helmut H Baldus Marc M

Biophysical journal 20050701 3

An approach is introduced to characterize conformational ensembles of intrinsically unstructured peptides on the atomic level using two-dimensional solid-state NMR data and their combination with molecular dynamics simulations. For neurotensin, a peptide that binds with high affinity to a G-protein coupled receptor, this method permits the investigation of the changes in conformational preferences of a neurotransmitter transferred from a frozen aqueous solution via a lipid model phase to the rec ...[more]

PMID: 15994900

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Project description:A challenge in the application of solid-state NMR spectroscopy to membrane peptides and proteins is the relatively broad line widths compared to those for solution NMR spectra. To understand the linewidth contributions to membrane protein NMR spectra, we have measured the inhomogeneous and homogeneous line widths of several well-studied membrane peptides under immobilized conditions. (13)C T(2) relaxation times of uniformly (13)C-labeled residues show that the homogeneous line widths of the peptides are comparable to those of crystalline model compounds under identical (1)H decoupling and magic angle spinning conditions, indicating that the homogeneous line widths are determined by conformation-independent factors, including residual dipolar coupling, J-coupling, and intrinsic T(2) relaxation. However, the membrane peptides exhibit larger apparent line widths than the crystalline compounds, indicating conformational disorder. A cationic cell-penetrating peptide, the human immunodeficiency virus TAT, exhibits the largest apparent line widths, which are about five-fold larger than the homogeneous line widths, while the transmembrane helix of the influenza M2 peptide and the ?-hairpin antimicrobial peptide PG-1 show moderately larger apparent line widths than the crystalline compounds. These results are consistent with the random coil nature of the TAT peptide, which contrasts with the intramolecularly hydrogen bonded M2 and PG-1. Cross peak line shapes of 2D double-quantum correlation spectra show that the conformational disorder can occur at the residue level and can result from three origins, lipid-peptide interaction, intrinsic conformational disorder encoded in the amino acid sequence, and side-chain rotameric averaging. A particularly important lipid-peptide interaction for cationic membrane peptides is guanidinium-phosphate ion pair interaction. Thus, NMR line widths and line shapes are useful for understanding the conformational disorder of membrane peptides and proteins.

Dataset Information

Probing conformational disorder in neurotensin by two-dimensional solid-state NMR and comparison to molecular dynamics simulations.

Publications

Probing conformational disorder in neurotensin by two-dimensional solid-state NMR and comparison to molecular dynamics simulations.

Similar Datasets

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