A probabilistic regular motif language for protein sequences is evaluated. SRE-DNA is a stochastic regular expression language that combines characteristics of regular expressions and stochastic representations such as Hidden Markov Models. To evaluate its expressive merits, genetic programming is used to evolve SRE-DNA motifs for aligned sets of protein sequences. Different constrained grammatical forms of SRE-DNA expressions are applied to aligned protein sequences from the PROSITE database. Some sequences patterns were precisely determined, while others resulted in good solutions having considerably different features from the PROSITE equivalents. This research establishes the viability of SRE-DNA as a new representation language for protein sequence identification. The practicality of using grammatical genetic programming in stochastic biosequence expression classification is also demonstrated.
展开▼
