Numerical Methods for Unconstrained Optimization and Nonlinear Equations (Classics in Applied Mathematics, 16)
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The exponential growth of the sequence data produced by the genome projects motivates the development of better ways of inferring structural and functional information about those newly sequenced 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 procedure, known as sequence comparison, relies on the use of an appropriate score function that discriminates 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 protein sequences including such distantly-related homologs. We present a new method for generating 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 matrices for the detection of distantly related protein sequences.