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Identification of novel molecular determinants of co-receptor usage in HIV-1 subtype F V3 envelope sequences.

ABSTRACT: HIV-1 determinants of coreceptor usage within the gp120 V3 loop have been broadly studied over the past years. This information has led to the development of state-of the-art bioinformatic tools that are useful to predict co-receptor usage based on the V3 loop sequence mainly of subtypes B, C and A. However, these methods show a poor performance for subtype F V3 loops, which are found in an increasing number of HIV-1 strains worldwide. In the present work we investigated determinants of viral tropisms in the understudied subtype F by looking at genotypic and structural information of coreceptor:V3 loop interactions in a novel group of 40 subtype F V3 loops obtained from HIV-1 strains phenotypically characterized either as syncytium inducing or non-syncytium inducing by the MT-2 assay. We provide novel information about estimated interactions energies between a set of V3 loops with known tropism in subtype F, that allowed us to improve predictions of the coreceptor usage for this subtype. Understanding genetic and structural features underlying HIV coreceptor usage across different subtypes is relevant for the rational design of preventive and therapeutic strategies aimed at limiting the HIV-1 epidemic worldwide.

SUBMITTER: Distefano M

PROVIDER: S-EPMC7387458 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Identification of novel molecular determinants of co-receptor usage in HIV-1 subtype F V3 envelope sequences.

Distefano Maximiliano M Lanzarotti Esteban E Fernández María Florencia MF Mangano Andrea A Martí Marcelo M Aulicino Paula P

Scientific reports 20200728 1

HIV-1 determinants of coreceptor usage within the gp120 V3 loop have been broadly studied over the past years. This information has led to the development of state-of the-art bioinformatic tools that are useful to predict co-receptor usage based on the V3 loop sequence mainly of subtypes B, C and A. However, these methods show a poor performance for subtype F V3 loops, which are found in an increasing number of HIV-1 strains worldwide. In the present work we investigated determinants of viral tr ...[more]

PMID: 32724045

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Project description:BackgroundHIV-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 in silico 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.ResultsClassifier 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.ConclusionsThe 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 http://proteins.gmu.edu/automute.

Project description:BackgroundThe majority of HIV-1 subjects worldwide are infected with HIV-1 subtype C (C-HIV). Although C-HIV predominates in developing regions of the world such as Southern Africa and Central Asia, C-HIV is also spreading rapidly in countries with more developed economies and health care systems, whose populations are more likely to have access to wider treatment options, including the CCR5 antagonist maraviroc (MVC). The ability to reliably determine C-HIV coreceptor usage is therefore becoming increasingly more important. In silico V3 sequence based coreceptor usage prediction algorithms are a relatively rapid and cost effective method for determining HIV-1 coreceptor specificity. In this study, we elucidated the V3 sequence determinants of C-HIV coreceptor usage, and used this knowledge to develop and validate a novel, user friendly, and highly sensitive C-HIV specific coreceptor usage prediction algorithm.ResultsWe characterized every phenotypically-verified C-HIV gp120 V3 sequence available in the Los Alamos HIV Database. Sequence analyses revealed that compared to R5 C-HIV V3 sequences, CXCR4-using C-HIV V3 sequences have significantly greater amino acid variability, increased net charge, increased amino acid length, increased frequency of insertions and substitutions within the GPGQ crown motif, and reduced frequency of glycosylation sites. Based on these findings, we developed a novel C-HIV specific coreceptor usage prediction algorithm (CoRSeqV3-C), which we show has superior sensitivity for determining CXCR4 usage by C-HIV strains compared to all other available algorithms and prediction rules, including Geno2pheno[coreceptor] and WebPSSMSINSI-C, which has been designed specifically for C-HIV.ConclusionsCoRSeqV3-C is now openly available for public use at http://www.burnet.edu.au/coreceptor. Our results show that CoRSeqV3-C is the most sensitive V3 sequence based algorithm presently available for predicting CXCR4 usage of C-HIV strains, without compromising specificity. CoRSeqV3-C may be potentially useful for assisting clinicians to decide the best treatment options for patients with C-HIV infection, and will be helpful for basic studies of C-HIV pathogenesis.

Dataset Information

Identification of novel molecular determinants of co-receptor usage in HIV-1 subtype F V3 envelope sequences.

Publications

Identification of novel molecular determinants of co-receptor usage in HIV-1 subtype F V3 envelope sequences.

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