16th International HIV Drug Resistance Workshop 12-16 June 2007, Barbados

PREDICTION OF HIV-1 CORECEPTOR USAGE BASED ON STRUCTURAL DESCRIPTORS OF THE gp120 V3 LOOP

Antivir Ther. 2007; 12:S16 (abstract no. 14)

O Sander¹, T Sing¹, I Sommer¹, AJ Low², PK Cheung², PR Harrigan², T Lengauer¹ and FS Domingues^1
1Max-Planck-Institute for Informatics, Saarbruecken, Germany; ²British Columbia Centre for Excellence in HIV/AIDS, Vancouver, Canada

BACKGROUND: HIV cell entry requires one of the chemokine receptors CCR5 or CXCR4 as coreceptor, besides the cell surface receptor CD4. Monitoring coreceptor usage is of great importance due to its relation to disease progression towards AIDS as well as its relevance for therapeutic decisions regarding coreceptor inhibitors. Established prediction methods like the 11/25 charge rule, or newer methods based on statistical learning techniques or PSSMs are used to predict coreceptor tropism based on the V3 loop region of the viral envelope protein gp120, without requiring expensive phenotype testing. However, all predictive methods in current use are utilizing sequence information only, while neglecting structural information. The structural basis of coreceptor specificity is still unclear.

METHODS: We use the three-dimensional structure of the V3 loop structure (Huang et al. Science. 2005 Nov 11;310(5750):1025-8) as a template to model the V3 loop of viral variants. While the backbone conformation is kept rigid, the SCWRL method is used to predict side-chain conformations. These models of viral variants are then represented by a structural descriptor, using pairwise distance distributions between functional atoms in the loop (donor, acceptor, ambivalent donor/acceptor, aliphatic or aromatic). This vectorial representation captures the spatial arrangement of physico-chemical properties in the V3 loop and allows statistical learning methods like SVMs and random forests to discriminate between CCR5- and CXCR4-using variants. The method is evaluated by 10 replicates of 10-fold cross validation on a dataset of 432 non-identical V3 sequences from clonal samples, 97 of the samples being X4 variants.

RESULTS AND CONCLUSION: The structural descriptor significantly improved prediction of coreceptor usage compared to a linear support vector machine trained on sequence data. For a given specificity of 0.95, a sensitivity of 0.77 was achieved, improving further to 0.80 when combined with a sequence-based representation using amino acid indicators. This compares favourably to the sensitivity of 0.73 for purely sequence-based prediction. By using statistical importance measures, structural features relevant for cocreceptor usage can be mapped onto the structure allowing for visual and quantitative interpretation.

2007-06-12
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