Novel Statistical Energy Function and Its Applications to Side-chain Modeling and Fold Recognition

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Computational methods to predict protein structure from sequence are becoming increasingly important and powerful, particularly in light of structural and functional annotation of genomic data. To improve the performance of computational algorithms, this project has three specific aims: (1) Development of an orientation-dependent statistical potential based on side-chain packing; (2) Development of a fast and accurate method for generating side-chain rotamer conformations; and (3) Development of structure profile for alignment and template identification of remote targets. Currently, the development of effective potential functions for side-chain modeling is still a challenging problem in the field. The new statistical potential function and its applications open a new path to improving the modeling of side-chains. Such a capability is vitally important for high-accuracy refinement of predicted structures, which is the most difficult step in structure prediction for the last few decades. As a faculty member for the last seven years at both Baylor College of Medicine and Rice University, the PI bears enormous educational responsibility and shares high enthusiasm for developing programs to expose underprivileged and minority students to modern computational techniques and tools of structural biology. Being in Houston, one of the largest populations of such students in the United States and having the inter-institutional appointment at both Baylor and Rice are two important factors that have dramatically enabled the PI to fulfill this important duty. The results of this research will be integrated into the extensive and on-going educational outreach activities at both Baylor and Rice and the computer programs will be made publicly available.