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

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

Prediction of Co-Receptor Usage of HIV-1 from Genotype

Human Immunodeficiency Virus 1 uses for entry into host cells a receptor (CD4) and one of two co-receptors (CCR5 or CXCR4). Recently, a new class of antiretroviral drugs has entered clinical practice that specifically bind to the co-receptor CCR5, and thus inhibit virus entry. Accurate prediction of the co-receptor used by the virus in the patient is important as it allows for personalized selection of effective drugs and prognosis of disease progression. We have investigated whether it is possible to predict co-receptor usage accurately by analyzing the amino acid sequence of the main determinant of co-receptor usage, i.e., the third variable loop V3 of the gp120 protein. We developed a two-level machine learning approach that in the first level considers two different properties important for protein-protein binding derived from structural models of V3 and V3 sequences. The second level combines the two predictions of the first level. The two-level method predicts usage of CXCR4 co-receptor for new V3 sequences within seconds, with an area under the ROC curve of 0.937±0.004. Moreover, it is relatively robust against insertions and deletions, which frequently occur in V3. The approach could help clinicians to find optimal personalized treatments, and it offers new insights into the molecular basis of co-receptor usage. For instance, it quantifies the importance for co-receptor usage of a pocket that probably is responsible for binding sulfated tyrosine

Accurate and efficient gp120 V3 loop structure based models for the determination of HIV-1 co-receptor usage

<p>Abstract</p> <p>Background</p> <p>HIV-1 targets human cells expressing both the CD4 receptor, which binds the viral envelope glycoprotein gp120, as well as either the CCR5 (R5) or CXCR4 (X4) co-receptors, which interact primarily with the third hypervariable loop (V3 loop) of gp120. Determination of HIV-1 affinity for either the R5 or X4 co-receptor on host cells facilitates the inclusion of co-receptor antagonists as a part of patient treatment strategies. A dataset of 1193 distinct gp120 V3 loop peptide sequences (989 R5-utilizing, 204 X4-capable) is utilized to train predictive classifiers based on implementations of random forest, support vector machine, boosted decision tree, and neural network machine learning algorithms. An <it>in silico </it>mutagenesis procedure employing multibody statistical potentials, computational geometry, and threading of variant V3 sequences onto an experimental structure, is used to generate a feature vector representation for each variant whose components measure environmental perturbations at corresponding structural positions.</p> <p>Results</p> <p>Classifier performance is evaluated based on stratified 10-fold cross-validation, stratified dataset splits (2/3 training, 1/3 validation), and leave-one-out cross-validation. Best reported values of sensitivity (85%), specificity (100%), and precision (98%) for predicting X4-capable HIV-1 virus, overall accuracy (97%), Matthew's correlation coefficient (89%), balanced error rate (0.08), and ROC area (0.97) all reach critical thresholds, suggesting that the models outperform six other state-of-the-art methods and come closer to competing with phenotype assays.</p> <p>Conclusions</p> <p>The trained classifiers provide instantaneous and reliable predictions regarding HIV-1 co-receptor usage, requiring only translated V3 loop genotypes as input. Furthermore, the novelty of these computational mutagenesis based predictor attributes distinguishes the models as orthogonal and complementary to previous methods that utilize sequence, structure, and/or evolutionary information. The classifiers are available online at <url>http://proteins.gmu.edu/automute</url>.</p

HIV-1 V3 envelope deep sequencing for clinical plasma specimens failing in phenotypic tropism assays

<p>Abstract</p> <p>Background</p> <p>HIV-1 infected patients for whom standard gp160 phenotypic tropism testing failed are currently excluded from co-receptor antagonist treatment. To provide patients with maximal treatment options, massively parallel sequencing of the envelope V3 domain, in combination with tropism prediction tools, was evaluated as an alternative tropism determination strategy. Plasma samples from twelve HIV-1 infected individuals with failing phenotyping results were available. The samples were submitted to massive parallel sequencing and to confirmatory recombinant phenotyping using a fraction of the gp120 domain.</p> <p>Results</p> <p>A cut-off for sequence reads interpretation of 5 to10 times the sequencing error rate (0.2%) was implemented. On average, each sample contained 7 different V3 haplotypes. V3 haplotypes were submitted to tropism prediction algorithms, and 4/14 samples returned with presence of a dual/mixed (D/M) tropic virus, respectively at 3%, 10%, 11%, and 95% of the viral quasispecies. V3 tropism prediction was confirmed by gp120 phenotyping, except for two out of 4 D/M predicted viruses (with 3 and 95%) which were phenotypically R5-tropic. In the first case, the result was discordant due to the limit of detection for the phenotyping technology, while in the latter case the prediction algorithms were not computing the viral tropism correctly.</p> <p>Conclusions</p> <p>Although only demonstrated on a limited set of samples, the potential of the combined use of "deep sequencing + prediction algorithms" in cases where routine gp160 phenotype testing cannot be employed was illustrated. While good concordance was observed between gp120 phenotyping and prediction of R5-tropic virus, the results suggest that accurate prediction of X4-tropic virus would require further algorithm development.</p

Comparative determination of HIV-1 co-receptor tropism by Enhanced Sensitivity Trofile, gp120 V3-loop RNA and DNA genotyping

BACKGROUND: Trofile is the prospectively validated HIV-1 tropism assay. Its use is limited by high costs, long turn-around time, and inability to test patients with very low or undetectable viremia. We aimed at assessing the efficiency of population genotypic assays based on gp120 V3-loop sequencing for the determination of tropism in plasma viral RNA and in whole-blood viral DNA. Contemporary and follow-up plasma and whole-blood samples from patients undergoing tropism testing via the enhanced sensitivity Trofile (ESTA) were collected. Clinical and clonal geno2pheno[coreceptor] (G2P) models at 10% and at optimised 5.7% false positive rate cutoff were evaluated using viral DNA and RNA samples, compared against each other and ESTA, using Cohen's kappa, phylogenetic analysis, and area under the receiver operating characteristic (AUROC). RESULTS: Both clinical and clonal G2P (with different false positive rates) showed good performances in predicting the ESTA outcome (for V3 RNA-based clinical G2P at 10% false positive rate AUROC = 0.83, sensitivity = 90%, specificity = 75%). The rate of agreement between DNA- and RNA-based clinical G2P was fair (kappa = 0.74, p < 0.0001), and DNA-based clinical G2P accurately predicted the plasma ESTA (AUROC = 0.86). Significant differences in the viral populations were detected when comparing inter/intra patient diversity of viral DNA with RNA sequences. CONCLUSIONS: Plasma HIV RNA or whole-blood HIV DNA V3-loop sequencing interpreted with clinical G2P is cheap and can be a good surrogate for ESTA. Although there may be differences among viral RNA and DNA populations in the same host, DNA-based G2P may be used as an indication of viral tropism in patients with undetectable plasma viremia

Selected amino acid mutations in HIV-1 B subtype gp41 are Associated with Specific gp120V3 signatures in the regulation of Co-Receptor usage

<p>Abstract</p> <p>Background</p> <p>The third variable loop (V3) of the HIV-1 gp120 surface protein is a major determinant of cellular co-receptor binding. However, HIV-1 can also modulate its tropism through other regions in gp120, such as V1, V2 and C4 regions, as well as in the gp41 protein. Moreover, specific changes in gp41 are likely to be responsible for of damage in gp120-CCR5 interactions, resulting in potential resistance to CCR5 inhibitors.</p> <p>In order to genetically characterize the two envelope viral proteins in terms of co-receptor usage, we have analyzed 526 full-length <it>env </it>sequences derived from HIV-1 subtype-B infected individuals, from our and public (Los Alamos) databases. The co-receptor usage was predicted by the analysis of V3 sequences using Geno2Pheno (G2P) algorithm. The binomial correlation phi coefficient was used to assess covariation among gp120_V3and gp41 mutations; subsequently the average linkage hierarchical agglomerative clustering was performed.</p> <p>Results</p> <p>According to G2P false positive rate (FPR) values, among 526 env-sequences analyzed, we further characterized 196 sequences: 105 with FPR <5% and 91 with FPR >70%, for X4-using and R5-using viruses, respectively.</p> <p>Beyond the classical signatures at 11/25 V3 positions (S11S and E25D, R5-tropic viruses; S11KR and E25KRQ, X4-tropic viruses), other specific V3 and gp41 mutations were found statistically associated with the co-receptor usage. Almost all of these specific gp41 positions are exposed on the surface of the glycoprotein. By the covariation analysis, we found several statistically significant associations between V3 and gp41 mutations, especially in the context of CXCR4 viruses. The topology of the dendrogram showed the existence of a cluster associated with R5-usage involving E25D_V3, S11S_V3, T22A_V3, S129DQ_gp41and A96N_gp41signatures (bootstrap = 0.88). Conversely, a large cluster was found associated with X4-usage involving T8I_V3, S11KR_V3, F20IVY_V3, G24EKR_V3, E25KR_V3, Q32KR_V3, A30T_gp41, A189S_gp41, N195K_gp41and L210P_gp41mutations (bootstrap = 0.84).</p> <p>Conclusions</p> <p>Our results show that gp120_V3and several specific amino acid changes in gp41 are associated together with CXCR4 and/or CCR5 usage. These findings implement previous observations that determinants of tropism may reside outside the V3-loop, even in the gp41. Further studies will be needed to confirm the degree to which these gp41 mutations contribute directly to co-receptor use.</p

Determinação do tropismo viral por ensaios genotípicos e fenotípicos em pacientes brasileiros infectados por HIV-1

The clinical application of CCR5 antagonists involves first determining the coreceptor usage by the infecting viral strain. Bioinformatics programs that predict coreceptor usage could provide an alternative method to screen candidates for treatment with CCR5 antagonists, particularly in countries with limited financial resources. Thus, the present study aims to identify the best approach using bioinformatics tools for determining HIV-1 coreceptor usage in clinical practice. Proviral DNA sequences and Trofile results from 99 HIV-1-infected subjects under clinical monitoring were analyzed in this study. Based on the Trofile results, the viral variants present were 81.1% R5, 21.4% R5X4 and 1.8% X4. Determination of tropism using a Geno2pheno[coreceptor] analysis with a false positive rate of 10% gave the most suitable performance in this sampling: the R5 and X4 strains were found at frequencies of 78.5% and 28.4%, respectively, and there was 78.6% concordance between the phenotypic and genotypic results. Further studies are needed to clarify how genetic diversity amongst virus strains affects bioinformatics-driven approaches for determining tropism. Although this strategy could be useful for screening patients in developing countries, some limitations remain that restrict the wider application of coreceptor usage tests in clinical practice.A aplicação clínica dos antagonistas de CCR5 envolve em primeiro lugar determinar o uso de co-receptor pela cepa viral infectante. Programas de bioinformática que prevêem o uso co-receptor poderiam fornecer um método alternativo para selecionar candidatos para o tratamento com os antagonistas do CCR5, particularmente em países com poucos recursos financeiros. Assim, o presente estudo teve por objetivo identificar a melhor abordagem utilizando ferramentas de bioinformática para determinar qual o tipo de co-receptor do HIV-1 que poderia ser usado na prática clínica. Sequências de DNA proviral e Trofile resultados a partir de 99 pacientes infectados pelo HIV-1 sob monitorização clínica foram avaliadas. Com base nos resultados do Teste Trofile, as variantes virais presentes eram R5 (81,1%), R5X4 (21,4%) e X4 (1,8%). Determinação do tropismo pela análise do Geno2pheno, com taxa de falso positivos de 10% apresentou desempenho mais adequado para esta amostragem: as cepas R5 e X4 foram encontradas em frequências de 78,5% e 28,4%, respectivamente, e foi de 78,6% a concordância entre os resultados fenotípicos e genotípicos. Mais estudos são necessários para esclarecer como a diversidade genética entre as cepas do vírus afeta abordagens baseadas na determinação do tropismo pelas ferramentas de bioinformática. Embora esta estratégia possa ser útil para o rastreio de pacientes em países em desenvolvimento, permanecem algumas limitações que restringem a aplicação mais ampla para utilização de testes de co-receptor na prática clínica

Highly Accurate Structure-Based Prediction of HIV-1 Coreceptor Usage Suggests Intermolecular Interactions Driving Tropism

HIV-1 entry into host cells is mediated by interactions between the V3-loop of viral glycoprotein gp120 and chemokine receptor CCR5 or CXCR4, collectively known as HIV-1 coreceptors. Accurate genotypic prediction of coreceptor usage is of significant clinical interest and determination of the factors driving tropism has been the focus of extensive study. We have developed a method based on nonlinear support vector machines to elucidate the interacting residue pairs driving coreceptor usage and provide highly accurate coreceptor usage predictions. Our models utilize centroid-centroid interaction energies from computationally derived structures of the V3-loop:coreceptor complexes as primary features, while additional features based on established rules regarding V3-loop sequences are also investigated. We tested our method on 2455 V3-loop sequences of various lengths and subtypes, and produce a median area under the receiver operator curve of 0.977 based on 500 runs of 10-fold cross validation. Our study is the first to elucidate a small set of specific interacting residue pairs between the V3-loop and coreceptors capable of predicting coreceptor usage with high accuracy across major HIV-1 subtypes. The developed method has been implemented as a web tool named CRUSH, CoReceptor USage prediction for HIV-1, which is available at http://ares.tamu.edu/CRUSH/