title: Automated derivation and refinement of sequence length patterns for protein sequences using evolutionary computation.
abstract: Several stratagems are used in protein bioinformatics for the classification of proteins based on sequence, structure or function. We explore the concept of a minimal signature embedded in a sequence that defines the likely position of a protein in a classification. Specifically, we address the derivation of sparse profiles for the G-protein coupled receptor (GPCR) clan of integral membrane proteins. We present an evolutionary algorithm (EA) for the derivation of sparse profiles (signatures) without the need to supply a multiple alignment. We also apply an evolution strategy (ES) to the problem of pattern and profile refinement. Patterns were derived for the GPCR 'superfamily' and GPCR families 1-3 individually from starting populations of randomly generated signatures, using a database of integral membrane protein sequences and an objective function using a modified receiver operator characteristic (ROC) statistic. The signature derived for the family 1 GPCR sequences was shown to perform very well in a stringent cross-validation test, detecting 76% of unseen GPCR sequences at 5% error. Application of the ES refinement method to a signature developed by a previously described method [Sadowski, M.I., Parish, J.H., 2003. Automated generation and refinement of protein signatures: case study with G-protein coupled receptors. Bioinformatics 19, 727-734] resulted in a 6% increase of coverage for 5% error as measured in the validation test. We note that there might be a limit to this or any classification of proteins based on patterns or schemata.
authors.affiliation: Bioinformatics Unit, Department of Computer Science, University College London, Gower Street, London WC1E 6BT, UK.
authors.forename: M I
authors.initials: MI
authors.lastname: Sadowski
authors.forename: J H
authors.initials: JH
authors.lastname: Parish
authors.forename: D R
authors.initials: DR
authors.lastname: Westhead
mesh_terms: D000465:Algorithms; D020816:Amino Acid Motifs / Q000235:genetics; D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D010363:Pattern Recognition, Automated / Q000379:methods*; D043562:Receptors, G-Protein-Coupled / Q000145:classification / Q000235:genetics*; D015203:Reproducibility of Results; D020539:Sequence Analysis, Protein / Q000379:methods*
chemical_list: D043562:Receptors, G-Protein-Coupled