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Abstract

HIV-1 cell entry commonly uses, in addition to CD4, one of the chemokine
receptors CCR5 or CXCR4 as coreceptor. Knowledge of coreceptor usage is
critical for monitoring disease progression as well as for supporting therapy
with the novel drug class of coreceptor antagonists. Predictive methods for
inferring coreceptor usage based on the third hypervariable (V3) loop region of
the viral gene coding for the envelope protein gp120 can provide these
monitoring facilities while avoiding expensive phenotypic tests. All simple
heuristics (like the 11/25 rule) as well as statistical learning methods
proposed to date predict coreceptor usage based on sequence features of the V3
loop exclusively. Here, we show, based on a recently resolved structure of
gp120 with an untruncated V3 loop, that using structural information on the V3
loop in combination with sequence features of V3 variants improves prediction
of coreceptor usage. In particular, we propose a distance-based descriptor of
the spatial arrangement of physicochemical properties that increases
discriminative performance. For a fixed 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
favorably with the sensitivities of 0.62 for the traditional 11/25 rule and
0.73 for a prediction based on sequence information as input to a support
vector machine (SVM) and constitutes a statistically significant improvement. A
detailed analysis and interpretation of structural features important for
classification shows the relevance of several specific hydrogen-bond donor
sites and aliphatic side chains to coreceptor specificity towards CCR5 or
CXCR4. Furthermore, an analysis of side chain orientation of the specificity
determining residues suggests a major role of one side of the V3 loop in the
selection of the coreceptor. The proposed method constitutes the first approach
to an improved prediction of coreceptor usage based on an original integration
of structural bioinformatics methods with statistical learning.