Geno2pheno co-receptor

The EucoHIV has the objective to provide a validated and refined version of the geno2pheno[coreceptor] model and server that is targeted to replacing the classical model used today.

  1. Geno2pheno[coreceptor] has become a main stake in HIV therapy with Maraviroc over Europe. While the “classical” g2p[coreceptor] model [1] has become something like a “de facto” standard due to the rising number of independent retrospective validations and its support by medical expert guidelines, several improvements of the model are underway or envisioned.

  2. In a preliminary analysis, the explicit incorporation of a descriptor of the V3-loop structure has led to improvements both in the power of the tropism prediction and in the interpretability of the result [2] . Recently, advanced software incorporating the structure of the V3-loop in the tropism analysis has been made available of the geno2pheno website (geno2pheno[structure]) [3].

  3. The geno2pheno[454] server exercises the classical clonal g2p[coreceptor] model on sequence mixtures coming from deep sequencing measurements (454 technology) [4]. The result is a set of predictions for each clone in the mixture. From this set the final prediction as to whether Maraviroc can be used is made based on heuristic estimates of the false positive rate threshold for calling X4 and on the maximum fraction of X4 calls in the mixture allowable for using Maraviroc. In a recent study, this way of fusing the information on the tropism calls of all sequences in the mixture has been refined with two effects: (1) We have an improved accuracy of predicting therapy success (follow-up), (2) we can use the deep sequencing data to derive a more highly predictive model on population Sanger sequencing data [5].

In an exploratory study the prediction of viral tropism has been based on more detailed laboratory measurements of the efficiency of viral cell entry based on viral and cellular determinants. This model is currently being refined [6].

 

 

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1. Lengauer, T., et al., Bioinformatics prediction of HIV coreceptor usage. Nat Biotechnol, 2007. 25(12): p. 1407-10.
2. Sander, O., et al., Structural Descriptors of gp120 V3 Loop for the Prediction of HIV-1 Coreceptor Usage. PLoS Comput Biol, 2007. 3(3): p. e58.
3. Bozek, K., et al., Analysis of Physicochemical and Structural Properties Determining HIV-1 Coreceptor Usage. PLoS Comput Biol, 2013. 9(3): p. e1002977.
4. Thielen, A. and T. Lengauer, Geno2pheno[454]: A Web Server for the Prediction of HIV-1 Coreceptor Usage from Next-Generation Sequencing Data. Intervirology, 2012. 55(2): p. 113-7.
5. Pfeifer, N. and T. Lengauer, Improving HIV coreceptor usage prediction in the clinic using hints from next-generation sequencing data. Bioinformatics, 2012. 28(18): p. i589-i595.
6. Bozek, K., et al., An expanded model of HIV cell entry phenotype based on multi-parameter single-cell data. Retrovirology, 2012. 9(1): p. 60.