Simple alignment-free methods for protein classification: A case study from G-protein-coupled receptors

Computational methods of predicting protein functions rely on detecting similarities among proteins. However, sufficient sequence information is not always available for some protein families. For example, proteins of interest may be new members of a divergent protein family. The performance of prot...

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Veröffentlicht in:	Genomics (San Diego, Calif.) Jg. 89; H. 5; S. 602 - 612
Hauptverfasser:	Strope, Pooja K., Moriyama, Etsuko N.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	San Diego, CA Elsevier Inc 01.05.2007 Elsevier
Schlagworte:	Algorithms Amino acid composition Amino Acids - analysis Amino Acids - chemistry Animals Biological and medical sciences Classification - methods Drosophila melanogaster - genetics Expressed Sequence Tags - chemistry Fundamental and applied biological sciences. Psychology G-protein-coupled receptors Genes. Genome Genetics of eukaryotes. Biological and molecular evolution Markov Chains Models, Chemical Molecular and cellular biology Molecular genetics Profile hidden Markov models Protein classification Receptors, G-Protein-Coupled - chemistry Receptors, G-Protein-Coupled - classification Receptors, G-Protein-Coupled - metabolism Support vector machines Support vector machines Amino acid composition Protein classification G-protein-coupled receptors Profile hidden Markov models Genomics Method Protein Case study Protein G-coupled receptor Aminoacid Classification Hidden Markov model Vector
ISSN:	0888-7543, 1089-8646
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Zusammenfassung:	Computational methods of predicting protein functions rely on detecting similarities among proteins. However, sufficient sequence information is not always available for some protein families. For example, proteins of interest may be new members of a divergent protein family. The performance of protein classification methods could vary in such challenging situations. Using the G-protein-coupled receptor superfamily as an example, we investigated the performance of several protein classifiers. Alignment-free classifiers based on support vector machines using simple amino acid compositions were effective in remote-similarity detection even from short fragmented sequences. Although it is computationally expensive, a support vector machine classifier using local pairwise alignment scores showed very good balanced performance. More commonly used profile hidden Markov models were generally highly specific and well suited to classifying well-established protein family members. It is suggested that different types of protein classifiers should be applied to gain the optimal mining power.
Bibliographie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	0888-7543 1089-8646
DOI:	10.1016/j.ygeno.2007.01.008