HIV-1 coreceptor usage prediction without multiple alignments: an application of string kernels.
Retrovirology
; 5: 110, 2008 Dec 04.
Article
em En
| MEDLINE
| ID: mdl-19055831
ABSTRACT
BACKGROUND:
Human immunodeficiency virus type 1 (HIV-1) infects cells by means of ligand-receptor interactions. This lentivirus uses the CD4 receptor in conjunction with a chemokine coreceptor, either CXCR4 or CCR5, to enter a target cell. HIV-1 is characterized by high sequence variability. Nonetheless, within this extensive variability, certain features must be conserved to define functions and phenotypes. The determination of coreceptor usage of HIV-1, from its protein envelope sequence, falls into a well-studied machine learning problem known as classification. The support vector machine (SVM), with string kernels, has proven to be very efficient for dealing with a wide class of classification problems ranging from text categorization to protein homology detection. In this paper, we investigate how the SVM can predict HIV-1 coreceptor usage when it is equipped with an appropriate string kernel.RESULTS:
Three string kernels were compared. Accuracies of 96.35% (CCR5) 94.80% (CXCR4) and 95.15% (CCR5 and CXCR4) were achieved with the SVM equipped with the distant segments kernel on a test set of 1425 examples with a classifier built on a training set of 1425 examples. Our datasets are built with Los Alamos National Laboratory HIV Databases sequences. A web server is available at http//genome.ulaval.ca/hiv-dskernel.CONCLUSION:
We examined string kernels that have been used successfully for protein homology detection and propose a new one that we call the distant segments kernel. We also show how to extract the most relevant features for HIV-1 coreceptor usage. The SVM with the distant segments kernel is currently the best method described.
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Base de dados:
MEDLINE
Assunto principal:
Receptores de HIV
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Biologia Computacional
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Receptores CCR5
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Receptores CXCR4
Idioma:
En
Ano de publicação:
2008
Tipo de documento:
Article