Technologies to rapidly obtain information about the structure of macromolecular complexes are needed to understand how these complexes function and, in particular, how aberrant interactions between molecules may result in human disease. Small-angle X-ray scattering is a relatively simple experimental method to obtain low-resolution information that offers advantages over methods such as X-ray crystallography and NMR. However, recovering information about the structure of a molecule from a scattering pattern is an extremely ill-posed and ill-conditioned mathematical inverse problem. In addition, evaluating mathematical models for a scattering pattern requires a computationally demanding, high dimensional integration. As a consequence of these difficulties, the current state of practice in SAXS reconstruction of molecular structure falls short of its potential as a scientific tool. The proposed research project is a multidisciplinary investigation into the inverse problem of determining structural information about complex bio-molecules from SAXS scattering patterns. In a close, well-established partnership with biochemists using SAXS in their research, a team of computational scientists, mathematicians, and statisticians will undertake a systematic investigation of the mathematical properties of the SAXS inverse problem using new statistical and mathematical tools, devise novel computational methods for computing solutions of the inverse problem for specific data sets, undertake an analysis quantifying the effects of model uncertainty, experimental error, and computational error on identifications made using SAXS data, and apply these methods to biomedically relevant experimental systems. The project is driven by specific research problems in biochemistry and structural biology, where extracting all available information from experimental data is essential to revealing low-resolution information for large macromolecules or complexes in solution. Such information advances understanding of the function of proteins and macromolecular assemblies in health and disease.

Public Health Relevance

The research will have a positive impact on the science of biochemistry through the construction of robust tools for identification of structure of macromolecules from small angle X-ray scattering data. Obtaining structural information of proteins and macromolecular assemblies is key to understanding their function in health and disease. This information opens the potential to the development of more effective drugs.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM096192-03
Application #
8307892
Study Section
Special Emphasis Panel (ZGM1-CBCB-5 (BM))
Program Officer
Wehrle, Janna P
Project Start
2010-08-20
Project End
2014-07-31
Budget Start
2012-08-01
Budget End
2013-07-31
Support Year
3
Fiscal Year
2012
Total Cost
$269,268
Indirect Cost
$81,386
Name
Colorado State University-Fort Collins
Department
Biostatistics & Other Math Sci
Type
Schools of Arts and Sciences
DUNS #
785979618
City
Fort Collins
State
CO
Country
United States
Zip Code
80523
Chaudhry, Jehanzeb Hameed; Estep, Don; Tavener, Simon et al. (2016) A POSTERIORI ERROR ANALYSIS OF TWO STAGE COMPUTATION METHODS WITH APPLICATION TO EFFICIENT DISCRETIZATION AND THE PARAREAL ALGORITHM. SIAM J Numer Anal 54:2974-3002
Bugbee, Bruce D; Breidt, F Jay; van der Woerd, Mark J (2016) Laplace Variational Approximation for Semiparametric Regression in the Presence of Heteroskedastic Errors. J Comput Graph Stat 25:225-245
Butler, T; Graham, L; Estep, D et al. (2015) Definition and solution of a stochastic inverse problem for the Manning's n parameter field in hydrodynamic models. Adv Water Resour 78:60-79
Brehove, Matthew; Wang, Tao; North, Justin et al. (2015) Histone core phosphorylation regulates DNA accessibility. J Biol Chem 290:22612-21
Kanewala, Upulee; Bieman, James M (2014) Testing Scientific Software: A Systematic Literature Review. Inf Softw Technol 56:1219-1232
Butler, T; Estep, D (2013) A numerical method for solving a stochastic inverse problem for parameters. Ann Nucl Energy 52:
Breidt, F Jay; Erciulescu, Andreea; van der Woerd, Mark (2012) Autocovariance Structures for Radial Averages in Small Angle X-Ray Scattering Experiments. J Time Ser Anal 33:704-717
Butler, T; Estep, D; Sandelin, J (2012) A COMPUTATIONAL MEASURE THEORETIC APPROACH TO INVERSE SENSITIVITY PROBLEMS II: A POSTERIORI ERROR ANALYSIS. SIAM J Numer Anal 50:22-45
Wilcox, Chris; Strout, Michelle Mills; Bieman, James M (2011) Tool support for software lookup table optimization. Sci Program 19:213-229
Breidt, J; Butler, T; Estep, D (2011) A MEASURE-THEORETIC COMPUTATIONAL METHOD FOR INVERSE SENSITIVITY PROBLEMS I: METHOD AND ANALYSIS. SIAM J Numer Anal 49:1836-1859

Showing the most recent 10 out of 11 publications