HIV-1 coreceptor usage prediction without multiple alignments: an application of string kernels.
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.


CONCLUSIONS
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.
Boisvert
Sébastien
S
Centre de recherche du centre hospitalier de l'Université Laval, Québec, Canada. Sebastien.Boisvert.3@ulaval.ca
Marchand
Mario
M
Laviolette
François
F
Corbeil
Jacques
J
D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D015658:HIV Infections / Q000235:genetics / Q000378:metabolism; D006801:Humans; D020407:Internet; D019713:Receptors, CCR5 / Q000737:chemistry* / Q000235:genetics / Q000378:metabolism; D019718:Receptors, CXCR4 / Q000737:chemistry* / Q000235:genetics* / Q000378:metabolism; D015728:Receptors, HIV / Q000737:chemistry* / Q000235:genetics / Q000378:metabolism; D017386:Sequence Homology, Amino Acid; D012984:Software; D014584:User-Computer Interface
D019713:Receptors, CCR5; D019718:Receptors, CXCR4; D015728:Receptors, HIV