We propose to develop a more accurate refinement algorithm that addresses the major goals from the RFA on High Accuracy Protein Structure Modeling. Better refinement of the starting template toward the native structure is a primary step in improving the predictions from close as well as remote homologs. At this level of structure prediction, where the conformational space is limited to a single fold family, sequence-specific differences in tertiary structure determine the perturbations necessary to refine a template toward its native structure. However, current refinement methods are dominated by random searches of local backbone conformations and only consider tertiary structure (such as side-chain packing) indirectly as an outcome to these main-chain movements. As supported by the data in the Preliminary Results section, this proposal is based on the hypothesis that a more accurate refinement method needs to be driven by tertiary structure. Therefore, the major goal of this proposal is to statistically model and apply more exact descriptions of the variation in tertiary structure to improve protein structure refinement in comparative modeling. In particular, our analysis will more clearly define the contributions to protein conformation from multi-bodied, tertiary interactions versus those determined by the linear protein backbone. As a new investigator, this proposal continues my group's long-term objective of discovering the determinants of protein structure and function, and we have assembled a collaborative, multi-disciplinary team of computational biochemists and statisticians with expertise in development of algorithms modeling protein structure, Bayesian non-parametric techniques, and high performance computing. With our computational resources and environment, we will complete the following objectives framed in the three stages of our refinement algorithm. First, we will create conformationally """"""""relaxed"""""""" starting structures that will have a higher likelihood of reaching the native state. Secondly, we will use our relative packing group construct to develop a side-chain centric, refinement move set. This move set will be incorporated into a structure build-up routine based on distance geometry. Lastly, we will derive selection algorithms that will identify near native models. By emphasizing that sequence specific variation in tertiary structure determines a protein's backbone, the proposed research represents a subtle but innovative shift in perspective to protein refinement of comparative models.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM081631-03
Application #
7673312
Study Section
Special Emphasis Panel (ZGM1-CBB-3 (HM))
Program Officer
Smith, Ward
Project Start
2007-08-01
Project End
2011-01-31
Budget Start
2009-08-01
Budget End
2011-01-31
Support Year
3
Fiscal Year
2009
Total Cost
$281,770
Indirect Cost
Name
University of the Pacific-Stockton
Department
Chemistry
Type
Schools of Arts and Sciences
DUNS #
625948831
City
Stockton
State
CA
Country
United States
Zip Code
95211
Fraga, Keith J; Joo, Hyun; Tsai, Jerry (2016) An amino acid code to define a protein's tertiary packing surface. Proteins 84:201-16
Day, Ryan; Joo, Hyun; Chavan, Archana C et al. (2013) Understanding the general packing rearrangements required for successful template based modeling of protein structure from a CASP experiment. Comput Biol Chem 42:40-8
Joo, Hyun; Chavan, Archana G; Phan, Jamie et al. (2012) An amino acid packing code for ?-helical structure and protein design. J Mol Biol 419:234-54
Day, Ryan; Qu, Xiaotao; Swanson, Rosemarie et al. (2011) Relative packing groups in template-based structure prediction: cooperative effects of true positive constraints. J Comput Biol 18:17-26
Joo, Hyun; Chavan, Archana G; Day, Ryan et al. (2011) Near-native protein loop sampling using nonparametric density estimation accommodating sparcity. PLoS Comput Biol 7:e1002234
Lennox, Kristin P; Dahl, David B; Vannucci, Marina et al. (2010) A DIRICHLET PROCESS MIXTURE OF HIDDEN MARKOV MODELS FOR PROTEIN STRUCTURE PREDICTION. Ann Appl Stat 4:916-942
Day, Ryan; Lennox, Kristin P; Dahl, David B et al. (2010) Characterizing the regularity of tetrahedral packing motifs in protein tertiary structure. Bioinformatics 26:3059-66
Joo, Hyun; Qu, Xiaotao; Swanson, Rosemarie et al. (2010) Fine grained sampling of residue characteristics using molecular dynamics simulation. Comput Biol Chem 34:172-83
Lennox, Kristin P; Dahl, David B; Vannucci, Marina et al. (2009) Density Estimation for Protein Conformation Angles Using a Bivariate von Mises Distribution and Bayesian Nonparametrics. J Am Stat Assoc 104:586-596
Qu, Xiaotao; Swanson, Rosemarie; Day, Ryan et al. (2009) A guide to template based structure prediction. Curr Protein Pept Sci 10:270-85

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