HIV-1 coreceptor usage prediction without multiple alignments: an application of string kernels
Human immunodeficiency virus type 1 (HIV-1) infects cells by means of ligand-receptorinteractions. This lentivirus uses the CD4 receptor in conjunction with a chemokine coreceptor, either CXCR4 orCCR5, to enter a target cell.
HIV-1 is characterized by high sequence variability. Nonetheless, within thisextensive variability, certain features must be conserved to define functions and phenotypes.
The determinationof coreceptor usage of HIV-1, from its protein envelope sequence, falls into a well-studied machine learningproblem known as classification. The support vector machine (SVM), with string kernels, has proven to be veryefficient for dealing with a wide class of classification problems ranging from text categorization to proteinhomology detection.
In this paper, we investigate how the SVM can predict HIV-1 coreceptor usage when it isequipped 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 of1425 examples with a classifier built on a training set of 1425 examples.
Our datasets are built with Los AlamosNational Laboratory HIV Databases sequences. A web server is available athttp://genome.ulaval.ca/hiv-dskernel.
Conclusions: We examined string kernels that have been used successfully for protein homology detection andpropose a new one that we call the distant segments kernel.
We also show how to extract the most relevantfeatures for HIV-1 coreceptor usage. The SVM with the distant segments kernel is currently the best methoddescribed.
Author: Sebastien Boisvert, Mario Marchand, Francois Laviolette and Jacques Corbeil
Credits/Source: Retrovirology 2008, 5:110
Published on: 2008-12-04
Copyright by the authors listed above - made available via BioMedCentral (Open Access). Please
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