A central challenge in bioinformatics today is developing computational methods for predicting a protein's structure, function, and biologically relevant partners at the whole-genome level. The first two problems are well-studied, but research on determining partners for given protein sequences is just beginning. The long-term goal is to create an automated system for predicting protein-protein interactions at the whole-genome level. This proposal focuses on the coiled coil motif, whose regular and repeating structure makes it an ideal model for computational detection. Also, advances in predicting coiled coil formation have important biological and medical consequences, since the coiled coil is widespread in sequence databases (3-5%), appearing in proteins that participate in transcription, oncogenesis, cell structure, and cell fusion events. The proposed method combines experimental data, coiled coil sequence information, and statistical information from protein sequences and 3-D structures to determine partner specificity between individual helices. The method is general enough to be extended to other protein domains and thus predict other protein-protein interactions and extract functional information.
