This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. In the past, when attempting to determine molecular function for a large set of proteins from known and predicted protein strctures, has been a very labor-intensive enterprise. Not only must the work be done to determine the structure, which is particularly challenging and labor-intensive on the part of ab intio protein stricture predictions, but those structures must be mapped to protein molecular function and correlated with what is known biologically about the protein to pick the most likely correct answers. In our recent publication covering the uncharacterized ORFs in yeast, this assignment of function was attempted for roughly 100 open reading frames in S. cerevesiae. This process involved literally hundreds of man hours. Our goal is complete automation of this procedure--resulting in the production of molecular function predictions from the Gene Ontology database for proteins of unknown function. This will be accomplished using the Gene Ontology database's annotation of gene products as representing our biological knowledge for a protein.