PROTEIN STRUCTURAL REFINEMENT USING COARSE-GRAINED MODELS OVER HIGH PERF COMPUT

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This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Protein structures can be determined through either experimental methods, such as X-ray crystallography and Nuclear Magnetic Resonance (NMR) technology, or theoretical methods, such as protein folding and homology modeling. However, many of determine protein structures are usually not as accurate as desired. Therefore, their applications have been severely limited in important fields, and structure refinement is always important and critical. In recent decades, the number of high resolution protein structures determined and deposited in PDB database is increasing, which makes it possible to conduct statistical analysis in the structural database. In this project, we propose a knowledge-based approach for protein structure refinement. From 2007-2008, we have finished the following work. Besides studying distance distributions of pair wise inter-atomic distances, we have studied side chain torsion angles Cb-Ca-Ca-Cb and many of them have significant distributions. The statistical analysis is essentially the most important step in this project, and also we have developed a user-friendly web service for protein structure modeling community ( www.wku.edu/~di.wu/psd ) so that such geometric prosperities with statistical analysis can be accessible and useful for refinement and analysis of protein models. A related report is in preparation and will be presented in bioinformatics related conferences. We have also developed a computational approach for structure refinement. Additional statistical potentials derived from distributions of distances and dihedral angles can be incorporated into the empirical energy function. We applied such idea specifically on X-ray structures with disorder regions and results have shown that such regions can be greatly refined with employing additional statistical potentials. Using obtained results, we have submitted a NIH R15 grant proposal for the project of protein structure refinement. We also plan to finish a few things from 2008-2009, including conducting a large number of samples in modeling disorder regions of crystal structures and employing torsion angle derived potentials in NMR structure refinement and crystal structure refinement. A more detailed report will be prepared and submitted for publication elsewhere in the future.