4.1 Article

Structure and flexibility within proteins as identified through small angle X-ray scattering

Journal

GENERAL PHYSIOLOGY AND BIOPHYSICS
Volume 28, Issue 2, Pages 174-189

Publisher

AEPRESS SRO
DOI: 10.4149/gpb_2009_02_174

Keywords

Small angle X-ray scattering; Protein flexibility; Molecular dynamics; Rigid body modeling

Funding

  1. National Science Foundation [ECS-0547013]
  2. Air Force Office of Scientific Research
  3. Air Force Materiel Command
  4. USAF [FA9550-06-1-0096]
  5. University Of Missouri in St. Louis
  6. Research Award and Research Board programs
  7. NERSC [m870]
  8. Austrian Science Fund (FWF) [M870] Funding Source: Austrian Science Fund (FWF)

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Flexibility between domains of proteins is often critical for function. These motions and proteins with large scale flexibility in general are often not readily amenable to conventional structural analysis such as X-ray crystallography, nuclear magnetic resonance spectroscopy (NMR) or electron microscopy. A common evolution of a crystallography project, once a high resolution structure has been determined, is to postulate possible sights of flexibility. Here we describe an analysis tool using relatively inexpensive small angle X-ray scattering (SAXS) measurements to identify flexibility and validate a constructed minimal ensemble of models, which represent highly populated conformations in solution. The resolution of these results is sufficient to address the questions being asked: what kinds of conformations do the domains sample in solution? In our rigid body modeling strategy BILBOMD, molecular dynamics (MD) simulations are used to explore conformational space. A common strategy is to perform the MD simulation on the domains connections at very high temperature, where the additional kinetic energy prevents the molecule from becoming trapped in a local minimum. The MD simulations provide an ensemble of molecular models from which a SAXS curve is calculated and compared to the experimental curve. A genetic algorithm is used to identify the minimal ensemble (minimal ensemble search, MES) required to best fit the experimental data. We demonstrate the use of MES in several model and in four experimental examples.

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