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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Bibliographic Details
Published in:Genomics (San Diego, Calif.) Vol. 89; no. 5; pp. 602 - 612
Main Authors: Strope, Pooja K., Moriyama, Etsuko N.
Format: Journal Article
Language:English
Published: San Diego, CA Elsevier Inc 01.05.2007
Elsevier
Subjects:
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
Online Access:Get full text
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Summary: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.
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ISSN:0888-7543
1089-8646
DOI:10.1016/j.ygeno.2007.01.008