What did the Scientists Discover?
X-ray solution scattering is a biophysical probe that is highly sensitive to changes in the shape or dynamics of a protein. Although it is possible to predict the x-ray scattering from a rigid molecule, it has been much more challenging to predict the scattering from a protein undergoing structural rearrangements during function. By combining molecular dynamics simulations with conventional calculation of x-ray scattering, the authors demonstrated an approach that allowed them to predict to within experimental errors, the scattering from a flexible protein undergoing structural re-arrangements during function. They then demonstrated its use in testing models for the structure and dynamics of specific molecular systems including the human protein 'ras' and the protease from HIV. This capability was further used to validate specific molecular dynamics algorithms and to identify those that resulted in over-estimate or under-estimate of the magnitude of protein motions.
Impact:
This approach is of particular utility for the characterization of the large-scale motions that are involved in allosteric regulation of protein functions. Aggressive use of these methods should lead to novel approaches to the development of pharmaceutical agents capable of altering molecular function and addressing biomedical problems arising in human disease.
Collaborators:
Lee Makowski, Department of Bioengineering, Northeastern University l.makowski@neu.edu
Hao Zhou, Department of Electrical and Computer Engineering, Northeastern University
Hugu Guterres, Department of Chemistry and Chemical Biology, Northeastern University
Carla Mattos, Department of Chemistry and Chemical Biology, Northeastern University
Publication citation:
Zhou H, Guterres H, Mattos C, Makowski L. Predicting X-ray solution scattering from flexible macromolecules. Protein Sci. 2018 Dec;27(12):2023-2036. doi: 10.1002/pro.3508. Epub 2018 Oct 16.
Funding:
Funding Agency | Grant Number |
---|---|
National Science Foundation |
DMR-1332208 MCB‐1517295 |
NIH |
GM103485 P41 GM103622 |
DOE Office of Science |
DE-AC02-06CH11357 |