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Deciphering protein dynamics from NMR data using explicit structure sampling and selection.

ABSTRACT: Perhaps one of the most prominent realizations of recent years is the critical role that protein dynamics plays in many facets of cellular function. While characterization of protein dynamics is fundamental to our understanding of protein function, the ability to explicitly detect an ensemble of protein conformations from dynamics data is a paramount challenge in structural biology. Here, we report a new computational method, Sample and Select, for determining the ensemble of protein conformations consistent with NMR dynamics data. This method can be generalized and extended to different sources of dynamics data, enabling broad applicability in deciphering protein dynamics at different timescales. The structural ensemble derived from Sample and Select will provide structural and dynamic information that should aid us in understanding and manipulating protein function.

SUBMITTER: Chen Y

PROVIDER: S-EPMC1965439 | biostudies-literature | 2007 Oct

REPOSITORIES: biostudies-literature

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Deciphering protein dynamics from NMR data using explicit structure sampling and selection.

Chen Yiwen Y Campbell Sharon L SL Dokholyan Nikolay V NV

Biophysical journal 20070608 7

Perhaps one of the most prominent realizations of recent years is the critical role that protein dynamics plays in many facets of cellular function. While characterization of protein dynamics is fundamental to our understanding of protein function, the ability to explicitly detect an ensemble of protein conformations from dynamics data is a paramount challenge in structural biology. Here, we report a new computational method, Sample and Select, for determining the ensemble of protein conformatio ...[more]

PMID: 17557784

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Project description:Solid-state NMR has been used to determine the structures of membrane proteins in native-like lipid bilayer environments. Most structure calculations based on solid-state NMR observables are performed using simulated annealing with restrained molecular dynamics and an energy function, where all nonbonded interactions are represented by a single, purely repulsive term with no contributions from van der Waals attractive, electrostatic, or solvation energy. To our knowledge, this is the first application of an ensemble dynamics technique performed in explicit membranes that uses experimental solid-state NMR observables to obtain the refined structure of a membrane protein together with information about its dynamics and its interactions with lipids. Using the membrane-bound form of the fd coat protein as a model membrane protein and its experimental solid-state NMR data, we performed restrained ensemble dynamics simulations with different ensemble sizes in explicit membranes. For comparison, a molecular dynamics simulation of fd coat protein was also performed without any restraints. The average orientation of each protein helix is similar to a structure determined by traditional single-conformer approaches. However, their variations are limited in the resulting ensemble of structures with one or two replicas, as they are under the strong influence of solid-state NMR restraints. Although highly consistent with all solid-state NMR observables, the ensembles of more than two replicas show larger orientational variations similar to those observed in the molecular dynamics simulation without restraints. In particular, in these explicit membrane simulations, Lys(40), residing at the C-terminal side of the transmembrane helix, is observed to cause local membrane curvature. Therefore, compared to traditional single-conformer approaches in implicit environments, solid-state NMR restrained ensemble simulations in explicit membranes readily characterize not only protein dynamics but also protein-lipid interactions in detail.

Dataset Information

Deciphering protein dynamics from NMR data using explicit structure sampling and selection.

Publications

Deciphering protein dynamics from NMR data using explicit structure sampling and selection.

Similar Datasets

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