Protein structure refinement through effective sampling and scoring. Detailed structural information is essential in understanding biological processes in detail and in allowing the rational development of therapeutic strategies against a variety of diseases. Experimental methods allow the accurate determination of high-resolution structures, but are encumbered by significant effort and experimental constraints. As an alternative, computational methods can predict protein structures to some degree of accuracy. However, it has remained a challenge to routinely predict protein structures at near-experimental accuracy. A high level of resolution may be reached through refinement of initial models. Successful protein structure refinement requires sampling methods that can generate native-like conformations and scoring methods that are able to identify the most native structures from a set of candidates without any knowledge of the true experimental structure. In order to achieve these goals novel protein structure prediction and refinement protocols are developed. In particular, effective conformational sampling strategies based on existing and new methods with constraints to reduce conformational search space are introduced;novel statistical methods to enhance and combine existing scoring functions in the selection of refined models from a set of decoys are developed;and an intermediate resolution model PRIMO is developed to obtain a better balance between energetic accuracy, model resolution, and sampling efficiency. These new methods are combined into an integrated refinement strategy and applied in the context of an automated protein structure pipeline.

Public Health Relevance

New computational methods for the accurate prediction of protein structures are developed as an alternative to experimental approaches. Such structural information is crucial in understanding detailed biological mechanisms and allowing the development of therapeutic strategies against a variety of diseases.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM084953-05
Application #
8324267
Study Section
Macromolecular Structure and Function D Study Section (MSFD)
Program Officer
Wehrle, Janna P
Project Start
2008-09-01
Project End
2013-08-31
Budget Start
2012-09-01
Budget End
2013-08-31
Support Year
5
Fiscal Year
2012
Total Cost
$247,123
Indirect Cost
$80,506
Name
Michigan State University
Department
Biochemistry
Type
Schools of Arts and Sciences
DUNS #
193247145
City
East Lansing
State
MI
Country
United States
Zip Code
48824
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Feig, Michael; Sugita, Yuji (2013) Reaching new levels of realism in modeling biological macromolecules in cellular environments. J Mol Graph Model 45:144-56
Panahi, Afra; Feig, Michael (2013) Dynamic Heterogeneous Dielectric Generalized Born (DHDGB): An implicit membrane model with a dynamically varying bilayer thickness. J Chem Theory Comput 9:1709-1719
Mirjalili, Vahid; Feig, Michael (2013) Protein Structure Refinement through Structure Selection and Averaging from Molecular Dynamics Ensembles. J Chem Theory Comput 9:1294-1303
Harada, Ryuhei; Tochio, Naoya; Kigawa, Takanori et al. (2013) Reduced native state stability in crowded cellular environment due to protein-protein interactions. J Am Chem Soc 135:3696-701
Harada, Ryuhei; Sugita, Yuji; Feig, Michael (2012) Protein crowding affects hydration structure and dynamics. J Am Chem Soc 134:4842-9
Jaskierny, Adam J; Panahi, Afra; Feig, Michael (2011) Effect of flanking residues on the conformational sampling of the internal fusion peptide from Ebola virus. Proteins 79:1109-17
Gopal, Srinivasa M; Mukherjee, Shayantani; Cheng, Yi-Ming et al. (2010) PRIMO/PRIMONA: a coarse-grained model for proteins and nucleic acids that preserves near-atomistic accuracy. Proteins 78:1266-81
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