This renewal application expands upon two themes of the previous grant period - modern statistical analysis of protein structural features for use in structure determination and prediction, and the analysis and prediction of the structures of biological assemblies. It also extends our work into a new area - experimental tests of novel observations of biologically relevant interactions in protein crystals. In the first aim, we pursue preliminary observations that the position of the Cbeta atom of protein side chains varies with rotamer and with the backbone dihedrals phi and psi. This variation affects the position of the entire side chain in structure determination and structure prediction. We will perform a new regression analysis of the angles required to place Cbeta for each chi1 rotamer as a function of phi and psi with a novel formulation based on Gaussian process regression. These will be applied to side chain bond angles and dihedral angles as well as backbone bond angles we have investigated in the previous grant period. These backbone and side- chain regression functions will be added to modeling side chains in our program SCWRL4 and in the program Rosetta. We will further modify SCWRL4 to perform prediction of multiple side-chain positions and their occupancies for use in ligand docking and drug design and to allow the incorporation of NMR experimental constraints. Our second major aim is to improve the comparative modeling of biological assemblies for multiple query sequences. This will involve improved family and superfamily classifications of the entire PDB as well as improvements in how biological assemblies are inferred from asymmetric units and crystal symmetry of protein structures. We will develop a server for automated modeling of biological assemblies that will provide several predicted assemblies based on the available templates and include substantial biological annotations for the query and the template structures. In a new direction, we will investigate our observation of homodimer interactions in crystals of kinases that may be snapshots of trans- autophosphorylation events. These include the activation-loop swapped dimers of LCK and IGF1R that may be models of the activation autophosphorylation events for nearly all human tyrosine kinases. Several experimental approaches will be utilized and further structural analysis of the autophosphorylation interfaces will be performed.

Public Health Relevance

Knowledge of protein structure is required for the development of new approaches for drug design in the interests of improving human health. In this project, we will further pursue statistical analyses required for high-resolution structure determination and structure prediction and the development of methods for the prediction of oligomeric protein assemblies. We will also test experimentally some novel interactions in protein crystals that may be examples of kinase autophosphorylation that have not previously been recognized.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Research Project (R01)
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Macromolecular Structure and Function D Study Section (MSFD)
Program Officer
Swain, Amy L
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Research Institute of Fox Chase Cancer Center
United States
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Shagisultanova, Elena; Dunbrack Jr, Roland L; Golemis, Erica A (2015) Issues in interpreting the in vivo activity of Aurora-A. Expert Opin Ther Targets 19:187-200
Shapovalov, Maxim V; Wang, Qiang; Xu, Qifang et al. (2014) BioAssemblyModeler (BAM): user-friendly homology modeling of protein homo- and heterooligomers. PLoS One 9:e98309
Adolf-Bryfogle, Jared; Dunbrack Jr, Roland L (2013) The PyRosetta Toolkit: a graphical user interface for the Rosetta software suite. PLoS One 8:e66856
Wei, Qiong; Dunbrack Jr, Roland L (2013) The role of balanced training and testing data sets for binary classifiers in bioinformatics. PLoS One 8:e67863
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Mehra, Ranee; Serebriiskii, Ilya G; Dunbrack Jr, Roland L et al. (2011) Protein-intrinsic and signaling network-based sources of resistance to EGFR- and ErbB family-targeted therapies in head and neck cancer. Drug Resist Updat 14:260-79

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