Automatic Discovery Using Genetic Programming of an Unknown-Sized Detector of Protein Motifs Containing Repeatedly-Used Subexpressions

Automated methods of machine learning may be useful in discovering biologically meaningful patterns that are hidden in the rapidly growing databases of genomic and protein sequences. However, almost all existing methods of automated discovery require that the user specify, in advance, the size and shape of the pattern that is to be discovered. Moreover, existing methods do not have a workable analog of the idea of a reusable subroutine to exploit the recurring subpatterns of a problem environment. Genetic programming can evolve complicated problem-solving expressions of unspecified size and shape. When automatically defined functions are added to genetic programming, genetic programming becomes capable of efficiently capturing and exploiting recurring sub-patterns. This paper describes how genetic programming with automatically defined functions successfully evolved motifs for detecting the D-E-A-D box family of proteins and for detecting the manganese superoxide dismutase family. Both motifs were evolved without prespecifying their length. Both evolved motifs employed automatically defined functions to capture the repeated use of common subexpressions. When tested against the SWISS-PROT database of proteins, the two genetically evolved consensus motifs detect the two families either as well, or slightly better than, the comparable humanwritten motifs found in the PROSITE database.