Optimizing for Success: A New Score Function For Distantly Related Protein Sequence Comparison Abstract

The exponential growth of the sequence data pro-duced by the genome projects motivates the de-velopment of better ways of inferring structural and functional information about those newly se-quenced proteins. Looking for homologies between these probe protein sequences and other protein sequences in the database has proved to be one of the most useful current techniques. This pro-cedure, known as sequence comparison, relies on the use of an appropriate score function that dis-criminates homologs from non-homologs. Current score functions have difficulty identifying distantly-related homologs with low sequence similarity. As a result, there is an increased demand for a new score function that yields statistically-significant higher scores for all the pairs of homologous pro-tein sequences including such distantly-related ho-mologs. We present a new method for generat-ing a score function by optimizing it for successful discrimination between homologous and unrelated proteins. The new score function (OPTIMA) out-performs other commonly used substitution matri-ces for the detection of distantly related protein sequences.