Conformational dynamics of a crystalline protein from microsecond-scale molecular dynamics simulations and diffuse X-ray scattering
- Computer, Computational, and Statistical Sciences Division and
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158,
- Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, and
- Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, and, Department of Bioengineering, University of California, Berkeley, CA 94720
- Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM 87545,
X-ray diffraction from protein crystals includes both sharply peaked Bragg reflections and diffuse intensity between the peaks. The information in Bragg scattering is limited to what is available in the mean electron density. The diffuse scattering arises from correlations in the electron density variations and therefore contains information about collective motions in proteins. Previous studies using molecular-dynamics (MD) simulations to model diffuse scattering have been hindered by insufficient sampling of the conformational ensemble. To overcome this issue, we have performed a 1.1-μs MD simulation of crystalline staphylococcal nuclease, providing 100-fold more sampling than previous studies. This simulation enables reproducible calculations of the diffuse intensity and predicts functionally important motions, including transitions among at least eight metastable states with different active-site geometries. The total diffuse intensity calculated using the MD model is highly correlated with the experimental data. In particular, there is excellent agreement for the isotropic component of the diffuse intensity, and substantial but weaker agreement for the anisotropic component. Decomposition of the MD model into protein and solvent components indicates that protein–solvent interactions contribute substantially to the overall diffuse intensity. We conclude that diffuse scattering can be used to validate predictions from MD simulations and can provide information to improve MD models of protein motions.
- Research Organization:
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Sponsoring Organization:
- USDOE
- Grant/Contract Number:
- AC52-06NA25396; GM095887; STC-1231306; 1P01GM063210; OD009180; GM110580
- OSTI ID:
- 1235104
- Alternate ID(s):
- OSTI ID: 1221841; OSTI ID: 1321746
- Report Number(s):
- LA-UR-14-26310
- Journal Information:
- Proceedings of the National Academy of Sciences of the United States of America, Journal Name: Proceedings of the National Academy of Sciences of the United States of America Vol. 111 Journal Issue: 50; ISSN 0027-8424
- Publisher:
- Proceedings of the National Academy of SciencesCopyright Statement
- Country of Publication:
- United States
- Language:
- English
Web of Science
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