Hybrid approach for predicting coreceptor used by HIV-1 from its V3 loop amino acid sequence

Covariation of amino acid substitutions in the HIV-1 envelope glycoprotein gp120 and the antisense protein ASP associated with coreceptor usage

The tropism of the Human Immunodeficiency Virus type 1 (HIV-1) is determined by the use of either or both of the chemokine coreceptors CCR5 (R5) or CXCR4 (X4) for entry into the target cell. The ability of HIV-1 to bind R5 or X4 is determined primarily by the third variable loop (V3) of the viral envelope glycoprotein gp120. HIV-1 strains of pandemic group M contain an antisense gene termed asp , which overlaps env outside the region encoding the V3 loop. We previously showed that the ASP protein localizes on the envelope of infectious HIV-1 virions, suggesting that it may play a role in viral entry. In this study, we first developed a statistical method to predict coreceptor tropism based on the Fisher's linear discriminant analysis. We obtained three linear discriminant functions able to predict coreceptor tropism with high accuracy (94.4%) when applied to a training dataset of V3 sequences of known tropism. Using these functions, we predicted the tropism in a dataset of HIV-1 strains containing a full-length asp gene. In the amino acid sequence of ASP proteins expressed from these asp genes we identified five positions with substitutions significantly associated with viral tropism. Interestingly, we found that these substitutions correlate significantly with substitutions at six amino acid positions of the V3 loop domain associated with tropism. Altogether, our computational analyses identify ASP amino acid signatures coevolving with V3 and potentially affecting HIV-1 tropism, which can be validated through in vitro and in vivo experiments.

Predicting mutant outcome by combining deep mutational scanning and machine learning

Deep mutational scanning provides unprecedented wealth of quantitative data regarding the functional outcome of mutations in proteins. A single experiment may measure properties (eg, structural stability) of numerous protein variants. Leveraging the experimental data to gain insights about unexplored regions of the mutational landscape is a major computational challenge. Such insights may facilitate further experimental work and accelerate the development of novel protein variants with beneficial therapeutic or industrially relevant properties. Here we present a novel, machine learning approach for the prediction of functional mutation outcome in the context of deep mutational screens. Using sequence (one-hot) features of variants with known properties, as well as structural features derived from models thereof, we train predictive statistical models to estimate the unknown properties of other variants. The utility of the new computational scheme is demonstrated using five sets of mutational scanning data, denoted "targets": (a) protease specificity of APPI (amyloid precursor protein inhibitor) variants; (b-d) three stability related properties of IGBPG (immunoglobulin G-binding β1 domain of streptococcal protein G) variants; and (e) fluorescence of GFP (green fluorescent protein) variants. Performance is measured by the overall correlation of the predicted and observed properties, and enrichment-the ability to predict the most potent variants and presumably guide further experiments. Despite the diversity of the targets the statistical models can generalize variant examples thereof and predict the properties of test variants with both single and multiple mutations.

Portable and sensitive Ag nanorods based SERS platform for rapid HIV-1 detection and tropism determination

In this study, surface-enhanced Raman scattering (SERS) based field-deployable platform has been explored for early detection and distinction of the human immunodeficiency virus (HIV-1). A highly optimized silver nanorods array, fabricated using glancing angle deposition technique was used as SERS substrate. Distinct signature peaks for varying concentrations (10² to 10⁶ copies/mL) were identified in five different HIV-1 subtypes (A, B, C, D, and CRF02_AG). Binding of viruses directly with Ag nanorods without using antibodies or intermediate reagents is shown. The purified viruses were spiked in water and healthy plasma to capture pure HIV-1 peaks. Distinct peaks were also captured for the X4 and R5 tropic strains suggesting tropism based detection. The above data was further confirmed and analyzed statistically using a multivariate tool. Thus, the present study indicates the ability of the SERS platform to detect and differentiate the HIV-1 virus implying its further validation using clinical specimens and isolates.

THETA: a new genotypic approach for predicting HIV-1 CRF02-AG coreceptor usage

MOTIVATION

The circulating recombinant form of HIV-1 CRF02-AG is the most frequent non-B subtype in Europe. Anti-HIV therapy and pathophysiological studies on the impact of HIV-1 tropism require genotypic determination of HIV-1 tropism for non-B subtypes. But genotypic approaches based on analysis of the V3 envelope region perform poorly when used to determine the tropism of CRF02-AG. We, therefore, designed an algorithm based on information from the gp120 and gp41 ectodomain that better predicts the tropism of HIV-1 subtype CRF02-AG.

RESULTS

We used a bio-statistical method to identify the genotypic determinants of CRF02-AG coreceptor use. Toulouse HIV Extended Tropism Algorithm (THETA), based on a Least Absolute Shrinkage and Selection Operator method, uses HIV envelope sequence from phenotypically characterized clones. Prediction of R5X4/X4 viruses was 86% sensitive and that of R5 viruses was 89% specific with our model. The overall accuracy of THETA was 88%, making it sufficiently reliable for predicting the tropism of subtype CRF02-AG sequences.

AVAILABILITY AND IMPLEMENTATION

Binaries are freely available for download at https://github.com/viro-tls/THETA. It was implemented in Matlab and supported on MS Windows platform. The sequence data used in this work are available from GenBank under the accession numbers MK618182-MK618417.

Chemokine Coreceptor Usage Among HIV-1 Drug-Naive Patients Residing in the Rural Eastern Cape, South Africa

Sub-Saharan region in Africa still holds the highest burden of HIV/AIDS globally. HIV-1 requires coreceptor to gain entry into permissive cells to initiate infection. Molecular analysis of the chemokine coreceptor usage is important clinically and in the effective management of AIDS virus. This study aims to determine the coreceptor usage among HIV-1 drug-naive patients residing in the rural Eastern cape, South Africa. We collected blood samples from 55 HIV-infected patients into an anticoagulant vacutainer. RNA was extracted from separated plasma, and reverse transcription-polymerase chain reaction (RT-PCR) was performed followed by nested polymerase chain reaction to amplify the partial envelope fragment spanning the C2-C3 region. Sanger sequencing was done on the amplicons using the BigDye Terminator V3.1 sequencing kit (Applied Biosystems, Foster City, CA) while sequences were manually edited using BioEdit and Geneious 10.2.6 tools. The WebPSSM and Geno2pheno online tools were also utilized to predict coreceptor tropism while the phylogenetic analysis of the isolates was determined using MEGA 7. Of the 55 blood samples collected for the study, 50 (91%) were successfully amplified and sequenced. The mean age of the patients was 32 (18-56) years while the ratio of men to women was 35% and 65% correspondingly. Phylogenetic analysis revealed that all 50 sequences clustered with HIV-1 subtype C reference strains. Viral tropism of the V3 loop revealed 47 sequences to be R5 strains, while three sequences (T1E, T10E, and T11E,) were classified as X4 strains based on the WebPSSM and the Geno2pheno algorithm. HIV-1 R5 tropic strains were the most dominant virus obtained from this study, while HIV-1 subtype C still drives the epidemic in South Africa suggesting greater in vivo and host pathogen fitness. Documented data on mapping out cellular tropism based on viral tropism are important as maraviroc and the other CCR5 antagonist could be introduced as part of the treatment regimen in South Africa.

HIV-1 tropism prediction by the XGboost and HMM methods

Human Immunodeficiency Virus 1 (HIV-1) co-receptor usage, called tropism, is associated with disease progression towards AIDS. Furthermore, the recently developed and developing drugs against co-receptors CCR5 or CXCR4 open a new thought for HIV-1 therapy. Thus, knowledge about tropism is critical for illness diagnosis and regimen prescription. To improve tropism prediction accuracy, we developed two novel methods, the extreme gradient boosting based XGBpred and the hidden Markov model based HMMpred. Both XGBpred and HMMpred achieved higher specificities (72.56% and 72.09%) than the state-of-the-art methods Geno2pheno (61.6%) and G2p_str (68.60%) in a 10-fold cross validation test at the same sensitivity of 93.73%. Moreover, XGBpred had more outstanding performances (with AUCs 0.9483, 0.9464) than HMMpred (0.8829, 0.8774) on the Hivcopred and Newdb (created in this work) datasets containing larger proportions of hard-to-predict dual tropic samples in the X4-using tropic samples. Therefore, we recommend the use of our novel method XGBpred to predict tropism. The two methods and datasets are available via http://spg.med.tsinghua.edu.cn:23334/XGBpred/. In addition, our models identified that positions 5, 11, 13, 18, 22, 24, and 25 were correlated with HIV-1 tropism.

Contribution of the gp120 V3 loop to envelope glycoprotein trimer stability in primate immunodeficiency viruses

The V3 loop of the human immunodeficiency virus type 1 (HIV-1) gp120 exterior envelope glycoprotein (Env) becomes exposed after CD4 binding and contacts the coreceptor to mediate viral entry. Prior to CD4 engagement, a hydrophobic patch located at the tip of the V3 loop stabilizes the non-covalent association of gp120 with the Env trimer of HIV-1 subtype B strains. Here, we show that this conserved hydrophobic patch (amino acid residues 307, 309 and 317) contributes to gp120-trimer association in HIV-1 subtype C, HIV-2 and SIV. Changes that reduced the hydrophobicity of these V3 residues resulted in increased gp120 shedding and decreased Env-mediated cell-cell fusion and virus entry in the different primate immunodeficiency viruses tested. Thus, the hydrophobic patch is an evolutionarily conserved element in the tip of the gp120 V3 loop that plays an essential role in maintaining the stability of the pre-triggered Env trimer in diverse primate immunodeficiency viruses.

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The V3-loop of HIV-1 gp120 contributes to Env trimer stability and viral entry.

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The hydrophobic patch in the tip of the V3 loop is critical for pre-triggered Env trimer stability.

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The hydrophobic patch is a conserved motif in primate immunodeficiency viruses.

Classify vertebrate hemoglobin proteins by incorporating the evolutionary information into the general PseAAC with the hybrid approach

Hemoglobin is an oxygen-binding protein widely present in all kingdoms of life from prokaryotic to eukaryotic, but well established in the vertebrate system. An attempt was made to determine the Vertebrate hemoglobin (VerHb) protein on their animal classifications, based on general pseudo amino acid composition (PseAAC)'s evolutionary profiles and hybrid approach. The support vector machine (SVM) has been applied to develop all models, the prediction results further compared according to their animal classification. The performance of the approaches estimated using five-fold cross-validation techniques. The prediction performance was further investigated by receiver operating characteristic (ROC) and prediction score graphs. The prediction accuracy (ACC), sensitivity (SN) and specificity (SP) were examined to find the accurate predictions on the threshold level. Based on the approach, a web-tool has been developed for identifying the VerHb proteins.

HIV coreceptor tropism determination and mutational pattern identification

In the early stages of infection, Human Immunodeficiency Virus Type 1 (HIV-1) generally selects CCR5 as the primary coreceptor for entering the host cell. As infection progresses, the virus evolves and may exhibit a coreceptor-switch to CXCR4. Accurate determination coreceptor usage and identification key mutational patterns associated tropism switch are essential for selection of appropriate therapies and understanding mechanism of coreceptor change. We developed a classifier composed of two coreceptor-specific weight matrices (CMs) based on a full-scale dataset. For this classifier, we found an AUC of 0.97, an accuracy of 95.21% and an MCC of 0.885 (sensitivity 92.92%; specificity 95.54%) in a ten-fold cross-validation, outperforming all other methods on an independent dataset (13% higher MCC value than geno2pheno and 15% higher MCC value than PSSM). A web server (http://spg.med.tsinghua.edu.cn/CM.html) based on our classifier was provided. Patterns of genetic mutations that occur along with coreceptor transitions were further identified based on the score of each sequence. Six pairs of one-AA mutational patterns and three pairs of two-AA mutational patterns were identified to associate with increasing propensity for X4 tropism. These mutational patterns offered new insights into the mechanism of coreceptor switch and aided in monitoring coreceptor switch.

The biological characteristics of predominant strains of HIV-1 genotype: modeling of HIV-1 infection among men who have sex with men

To investigate the molecular subtypes of prevalent HIV-1 strains and characterize the genetics of dominant strains among men who have sex with men. Molecular epidemiology surveys in this study concentrated on the prevalent HIV-1 strains in Liaoning province by year. 229 adult patients infected with HIV-1 and part of a high-risk group of men who have sex with men were recruited. Reverse transcription and nested PCR amplification were performed. Sequencing reactions were conducted and edited, followed by codon-based alignment. NJ phylogenetic tree analyses detected two distinct CRF01_AE phylogenetic clusters, designated clusters 1 and 2. Clusters 1 and 2 accounted for 12.8% and 84.2% of sequences in the pol gene and 17.6% and 73.1% of sequences in the env gene, respectively. Another six samples were distributed on other phylogenetic clusters. Cluster 1 increased significantly from 5.6% to 20.0%, but cluster 2 decreased from 87.5% to 80.0%. Genetic distance analysis indicated that CRF01_AE cluster 1 in Liaoning was homologous to epidemic CRF01_AE strains, but CRF01_AE cluster 2 was different from other scattered strains. Additionally, significant differences were found in tetra-peptide motifs at the tip of V3 loop between cluster 1 and 2; however, differences in coreceptor usage were not detected. This study shows that subtype CRF01_AE strain may be the most prevalent epidemic strain in the men who have sex with men. Genetic characteristics of the subtype CRF01_AE cluster strain in Liaoning showed homology to the prevalent strains of men who have sex with men in other parts of China.

Harnessing Computational Biology for Exact Linear B-Cell Epitope Prediction: A Novel Amino Acid Composition-Based Feature Descriptor

Proteins embody epitopes that serve as their antigenic determinants. Epitopes occupy a central place in integrative biology, not to mention as targets for novel vaccine, pharmaceutical, and systems diagnostics development. The presence of T-cell and B-cell epitopes has been extensively studied due to their potential in synthetic vaccine design. However, reliable prediction of linear B-cell epitope remains a formidable challenge. Earlier studies have reported discrepancy in amino acid composition between the epitopes and non-epitopes. Hence, this study proposed and developed a novel amino acid composition-based feature descriptor, Dipeptide Deviation from Expected Mean (DDE), to distinguish the linear B-cell epitopes from non-epitopes effectively. In this study, for the first time, only exact linear B-cell epitopes and non-epitopes have been utilized for developing the prediction method, unlike the use of epitope-containing regions in earlier reports. To evaluate the performance of the DDE feature vector, models have been developed with two widely used machine-learning techniques Support Vector Machine and AdaBoost-Random Forest. Five-fold cross-validation performance of the proposed method with error-free dataset and dataset from other studies achieved an overall accuracy between nearly 61% and 73%, with balance between sensitivity and specificity metrics. Performance of the DDE feature vector was better (with accuracy difference of about 2% to 12%), in comparison to other amino acid-derived features on different datasets. This study reflects the efficiency of the DDE feature vector in enhancing the linear B-cell epitope prediction performance, compared to other feature representations. The proposed method is made as a stand-alone tool available freely for researchers, particularly for those interested in vaccine design and novel molecular target development for systems therapeutics and diagnostics: https://github.com/brsaran/LBEEP.

An in silico platform for predicting, screening and designing of antihypertensive peptides

High blood pressure or hypertension is an affliction that threatens millions of lives worldwide. Peptides from natural origin have been shown recently to be highly effective in lowering blood pressure. In the present study, we have framed a platform for predicting and designing novel antihypertensive peptides. Due to a large variation found in the length of antihypertensive peptides, we divided these peptides into four categories (i) Tiny peptides, (ii) small peptides, (iii) medium peptides and (iv) large peptides. First, we developed SVM based regression models for tiny peptides using chemical descriptors and achieved maximum correlation of 0.701 and 0.543 for dipeptides and tripeptides, respectively. Second, classification models were developed for small peptides and achieved maximum accuracy of 76.67%, 72.04% and 77.39% for tetrapeptide, pentapeptide and hexapeptides, respectively. Third, we have developed a model for medium peptides using amino acid composition and achieved maximum accuracy of 82.61%. Finally, we have developed a model for large peptides using amino acid composition and achieved maximum accuracy of 84.21%. Based on the above study, a web-based platform has been developed for locating antihypertensive peptides in a protein, screening of peptides and designing of antihypertensive peptides.

Reliable genotypic tropism tests for the major HIV-1 subtypes

Over the past decade antiretroviral drugs have dramatically improved the prognosis for HIV-1 infected individuals, yet achieving better access to vulnerable populations remains a challenge. The principal obstacle to the CCR5-antagonist, maraviroc, from being more widely used in anti-HIV-1 therapy regimens is that the pre-treatment genotypic "tropism tests" to determine virus susceptibility to maraviroc have been developed primarily for HIV-1 subtype B strains, which account for only 10% of infections worldwide. We therefore developed PhenoSeq, a suite of HIV-1 genotypic tropism assays that are highly sensitive and specific for establishing the tropism of HIV-1 subtypes A, B, C, D and circulating recombinant forms of subtypes AE and AG, which together account for 95% of HIV-1 infections worldwide. The PhenoSeq platform will inform the appropriate use of maraviroc and future CCR5 blocking drugs in regions of the world where non-B HIV-1 predominates, which are burdened the most by the HIV-1 pandemic.

IDEPI: rapid prediction of HIV-1 antibody epitopes and other phenotypic features from sequence data using a flexible machine learning platform

Since its identification in 1983, HIV-1 has been the focus of a research effort unprecedented in scope and difficulty, whose ultimate goals--a cure and a vaccine--remain elusive. One of the fundamental challenges in accomplishing these goals is the tremendous genetic variability of the virus, with some genes differing at as many as 40% of nucleotide positions among circulating strains. Because of this, the genetic bases of many viral phenotypes, most notably the susceptibility to neutralization by a particular antibody, are difficult to identify computationally. Drawing upon open-source general-purpose machine learning algorithms and libraries, we have developed a software package IDEPI (IDentify EPItopes) for learning genotype-to-phenotype predictive models from sequences with known phenotypes. IDEPI can apply learned models to classify sequences of unknown phenotypes, and also identify specific sequence features which contribute to a particular phenotype. We demonstrate that IDEPI achieves performance similar to or better than that of previously published approaches on four well-studied problems: finding the epitopes of broadly neutralizing antibodies (bNab), determining coreceptor tropism of the virus, identifying compartment-specific genetic signatures of the virus, and deducing drug-resistance associated mutations. The cross-platform Python source code (released under the GPL 3.0 license), documentation, issue tracking, and a pre-configured virtual machine for IDEPI can be found at https://github.com/veg/idepi.

HIV-1 tropism dynamics and phylogenetic analysis from longitudinal ultra-deep sequencing data of CCR5- and CXCR4-using variants

OBJECTIVE

Coreceptor switch from CCR5 to CXCR4 is associated with HIV disease progression. The molecular and evolutionary mechanisms underlying the CCR5 to CXCR4 switch are the focus of intense recent research. We studied the HIV-1 tropism dynamics in relation to coreceptor usage, the nature of quasispecies from ultra deep sequencing (UDPS) data and their phylogenetic relationships.

METHODS

Here, we characterized C2-V3-C3 sequences of HIV obtained from 19 patients followed up for 54 to 114 months using UDPS, with further genotyping and phylogenetic analysis for coreceptor usage. HIV quasispecies diversity and variability as well as HIV plasma viral load were measured longitudinally and their relationship with the HIV coreceptor usage was analyzed. The longitudinal UDPS data were submitted to phylogenetic analysis and sampling times and coreceptor usage were mapped onto the trees obtained.

RESULTS

Although a temporal viral genetic structuring was evident, the persistence of several viral lineages evolving independently along the infection was statistically supported, indicating a complex scenario for the evolution of viral quasispecies. HIV X4-using variants were present in most of our patients, exhibiting a dissimilar inter- and intra-patient predominance as the component of quasispecies even on antiretroviral therapy. The viral populations from some of the patients studied displayed evidences of the evolution of X4 variants through fitness valleys, whereas for other patients the data favored a gradual mode of emergence.

CONCLUSIONS

CXCR4 usage can emerge independently, in multiple lineages, along the course of HIV infection. The mode of emergence, i.e. gradual or through fitness valleys seems to depend on both virus and patient factors. Furthermore, our analyses suggest that, besides becoming dominant after population-level switches, minor proportions of X4 viruses might exist along the infection, perhaps even at early stages of it. The fate of these minor variants might depend on both viral and host factors.