Estimation of Protein Function Using Optimized Finite State Automaton Based on Accumulated Amino Acid Residue Scores
Shinji Chiba* and Ken Sugawara**
*Department of Information Engineering, Sendai National College of Technology, 4-16-1 Ayashi-chuou, Aoba-ku, Sendai 989-3128, Japan
**Department of Information Science, Tohoku Gakuin University, 2-1-1 Tenjinzawa, Izumi-ku, Sendai 981-3193, Japan
The function of unknown proteins is currently most effective determined by retrieving similar known sequences. Some effective techniques involve sequence retrieval. We propose retrieval using a finite state automaton (FSA). The FSA is created with accumulated amino acid residue scores that express a property of a protein family. We calculate the similarity of known and unknown protein sequences using the FSA and used it to determine protein functions. To improve accuracy, we optimized the FSA using a genetic algorithm. Results from determining protein functions indicated that our proposal was superior to general motif analysis.
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