Identification of functionally important protein residues by means of entropy based methods, and experimental validation by mutational analysis

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Our first generation program H2r analyses correlated mutations by means of entropy-based methods in order to identify crucial residue positions in proteins. In the current funding period, predictions made by H2r have been experimentally tested by mutational analysis of two enzymes involved in the biosynthesis of tryptophan. It turned out that amino acid exchanges at the identified positions alter catalytic efficiency or protein stability. Building up on these promising results, we want to further increase specificity and sensitivity of H2r. Specificity shall be improved by replacing Shannon’s entropy with correlation measures based on the von Neumann entropy. This allows us to consider physical and chemical properties of residue- pairs. Corresponding similarity matrices were prepared during the current finding period and a first prototypic algorithm was implemented. Sensitivity will increase, if H2r exploits all statistically significant correlation signals. An MSA-specific null-model and a residue-pair-specific approach to compute a cut-off are nearly finished and will be integrated within the next months. The resulting algorithms will be utilized to identify a larger fraction of crucial positions within individual proteins than hitherto possible. In addition to predict crucial residue-positions in individual proteins, we plan to extend correlation analysis to proteinprotein complexes. These positions will be assessed in silico and predictions will be validated by mutational experiments using well characterized enzymes and enzyme complexes.