Automated derivation and refinement of sequence length patterns for protein sequences using evolutionary computation Export

@article{citeulike:1772142, 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.}, author = {Sadowski, M. I. and Parish, J. H. and Westhead, D. R.}, citeulike-article-id = {1772142}, citeulike-linkout-0 = {http://dx.doi.org/10.1016/j.biosystems.2005.05.001}, citeulike-linkout-1 = {http://www.sciencedirect.com/science/article/B6T2K-4GSC2NR-1/2/f5c10c237a74e12a1ab12853dff15cd3}, doi = {10.1016/j.biosystems.2005.05.001}, journal = {Biosystems}, keywords = {algorithm, computing, evolutionary, genetic}, month = {September}, number = {3}, pages = {247--254}, posted-at = {2007-10-16 04:47:04}, priority = {3}, title = {Automated derivation and refinement of sequence length patterns for protein sequences using evolutionary computation}, url = {http://dx.doi.org/10.1016/j.biosystems.2005.05.001}, volume = {81}, year = {2005} }

TY - JOUR ID - citeulike:1772142 L3 - citeulike-article-id:1772142 N2 - 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. IS - 3 JF - Biosystems EP - 254 TI - Automated derivation and refinement of sequence length patterns for protein sequences using evolutionary computation VL - 81 SP - 247 KW - algorithm KW - computing KW - evolutionary KW - genetic AU - Sadowski, MI AU - Parish, JH AU - Westhead, DR PY - 2005/09// UR - http://dx.doi.org/10.1016/j.biosystems.2005.05.001 ER -