This project will enhance the stability, reliability and efficiency of Prof. Werner Braun and coworkers' new computational tool, the self-correcting distance geometry (SECODG) method for NMR structure determination and protein design. Three-dimensional structures of proteins, RNA or DNA are calculated by the SECODG method from distance and dihedral angle constraints. Unlike previous distance geometric methods, which were designed for consistent data sets, the SECODG method can generate accurate structures even from sets of constraints that contain errors. The SECODG method dramatically reduces the time to generate protein structures from NMR data. Dr. Braun's group will further improve this method and explore new statistical methods for error detection. The SECODG method can also be used to predict the tertiary folds of proteins from the amino acid sequence, if the secondary structure is partially or completely known. It was successfully tested in a preliminary study for its ability to predict the fold of small helical proteins from their amino acid sequences, and it will now be applied to a broader range of protein folds. The SECODG method will be a powerful computational tool to speed up macromolecular structure determination from NMR data. Experimentally determined three-dimensional protein structures are the basis to design new drugs or proteins with improved or new functions. In combination with energy minimization and Monte Carlo simulations it will help in designing proteins with given structural and new functional properties.
