This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. Computational &Bio Co-design-Cracking UGT Structure-Function Relationships The immediate need for understanding protein structure intensifies as the applications for engineered proteins for drugs, carriers, enzymatic activities, receptors, vaccines, antibodies, biomaterials and nanotechnology, in vitro synthesis, and detection systems grow. There is great utility in being able to input primary protein sequences and predict protein structure-function relationships for rational protein or drug design. Current computational approaches provide limited accuracy (~80%), often cannot handle large proteins, and require significant computational time. The proposed research will develop novel approaches for protein structure prediction to improve predictive accuracy and computational efficiency. Our strategy incorporates a """"""""co-design"""""""" process enabling computational predictions to be directly tested and further optimized through a facile model of significant medical relevance -the UDP-glucuronosyltransferase (UGT) family. We hypothesize that algorithms integrating protein threading, graph theory, and parameterization will provide the accuracy and efficiency of protein structure prediction required for direct utility in assessing protein structure-function relationships of complex proteins such as UGTs. Further, combining comparative domain modeling with direct biological experimentation will delineate key structural parameters for UGTs'substrate selectivity. This information will be key to designing UGTs with altered specificities and developing UGT-specific inhibitors. Specifically we will: + Enhance computational efficiency and predictive accuracy of protein structure algorithms by integrating protein threading, side-chain packing, and newly-developed ideas in parameterized computation. + Model UGTs and UGT variants and validate with direct biological assessment of UGT proteins through mutagenesis, protein expression, and enzymatic activity. + Expand computational approaches to encompass modeling of protein-substrate and protein-inhibitor interactions, protein-protein interaction, and impacts of glycosylation and membrane association. Developing effective computational approaches for accurately modeling protein structures has significant potential to impact research in proteins'critical roles in mediating cell signaling, growth, and differentiation. Successful outcomes from this modeling and biological research interface could lead to more effective medicines and disease treatments.
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