Particle Swarm Optimisation for Protein Motif Discovery

Authors:
Bill C. Chang;Asanga Ratnaweera;Saman K. Halgamuge;Harry C. Watson
Affiliations:
Mechatronics Research Group, Mechanical and Manufacturing Engineering, University of Melbourne, Australia;Mechatronics Research Group, Mechanical and Manufacturing Engineering, University of Melbourne, Australia;Mechatronics Research Group, Mechanical and Manufacturing Engineering, University of Melbourne, Australia;Thermofluids Research Group, Mechanical and Manufacturing Engineering, University of Melbourne, Australia
Venue:
Genetic Programming and Evolvable Machines
Year:
2004

Citing 12
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Abstract

In this paper, a modified particle swarm optimisation algorithm is proposed for protein sequence motif discovery. Protein sequences are represented as a chain of symbols and a protein sequence motif is a short sequence that exists in most of the protein sequence families. Protein sequence symbols are converted into numbers using a one to one amino acid translation table. The simulation uses EGF protein and C2H2 Zinc Finger protein families obtained from the PROSITE database. Simulation results show that the modified particle swarm optimisation algorithm is effective in obtaining global optimum sequence patterns, achieving 96.9 and 99.5 classification accuracy respectively in EGF and C2H2 Zinc Finger protein families. A better true positive hit result is achieved when compared to the motifs published in PROSITE database.