ITR/AP: Computational Analysis of Proteins: From Structure to Sequence to Function

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Accurate determination of protein structure and function is the focus of proteomics, the next step in the genomic revolution. Traditional knowledge discovery in molecular biology has followed a research paradigm corresponding to the information flow in functional genomics, that is from gene sequence to protein structure to function. This computational bioinformatics proposal exploits the information content of the protein structural and sequence databases following a research paradigm formulated in the title of this proposal,namely from structure to sequence to function prediction. The important postulate of this research is that spatial nearest neighbor residue motifs defined by the means of computational geometry (Delaunay tessellation) provide unique determinants of protein structure and function. The major goals of this proposal include: (1) development and validation of multibody statistical potentials on the basis of non-redundant subsets of the protein crystallographic database, (2) prediction of 3D protein structure by the means of novel implementation of the chain growth algorithm using Monte Carlo sampling, statistical potentials, and spatial constraints defined by specific residue motifs, and (3) determination of sequence and structure specific amino acid residue motifs for known proteins and large scale prediction of protein structural and functional classes for genomic sequences. The successful implementation of this proposal will have a broad impact on functional genomics and proteomics.