Structural Descriptors of gp120 V3 Loop for the Prediction of HIV-1 Coreceptor Usage

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 us with these monitoring facilities while avoiding expensive phenotypic tests. All simple heuristics (such as 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 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.

Author Summary

HIV-1 cell entry requires a chemokine coreceptor in addition to the CD4 cell surface receptor. The two most common types of HIV coreceptors are called CCR5 and CXCR4. Whereas CCR5-using viral variants dominate directly after infection and during early stages of the disease, in about 50% of the patients, CXCR4-using variants appear in later stages of the disease, suggesting the coreceptor switch to be a determinant of disease progression. HIV coreceptors received substantial attention as antiviral drug targets, with CCR5 antagonists being currently tested in phase III clinical studies. Treatment with coreceptor antagonists requires continuous monitoring of coreceptor usage. The prominent role of coreceptors in disease progression and their potential as antiviral drug targets provides incentives for methodological improvements in coreceptor prediction and better understanding of the underlying determining factors regarding sequence and structural aspects. Our proposed method is the first approach to predict coreceptor usage based on structural information as opposed to established sequence-based methods. Including structural information improves predictive performance and is a first step towards a deeper understanding of the structural aspects of coreceptor usage.