Prediction of VRC01 neutralization sensitivity by HIV-1 gp160 sequence features

Learning patterns of HIV-1 resistance to broadly neutralizing antibodies with reduced subtype bias using multi-task learning

The ability to predict HIV-1 resistance to broadly neutralizing antibodies (bnAbs) will increase bnAb therapeutic benefits. Machine learning is a powerful approach for such prediction. One challenge is that some HIV-1 subtypes in currently available training datasets are underrepresented, which likely affects models' generalizability across subtypes. A second challenge is that combinations of bnAbs are required to avoid the inevitable resistance to a single bnAb, and computationally determining optimal combinations of bnAbs is an unsolved problem. Recently, machine learning models trained using resistance outcomes for multiple antibodies at once, a strategy called multi-task learning (MTL), have been shown to improve predictions. We develop a new model and show that, beyond the boost in performance, MTL also helps address the previous two challenges. Specifically, we demonstrate empirically that MTL can mitigate bias from underrepresented subtypes, and that MTL allows the model to learn patterns of co-resistance to combinations of antibodies, thus providing tools to predict antibodies' epitopes and to potentially select optimal bnAb combinations. Our analyses, publicly available at https://github.com/iaime/LBUM, can be adapted to other infectious diseases that are treated with antibody therapy.

Predicting neutralization susceptibility to combination HIV-1 monoclonal broadly neutralizing antibody regimens

Combination monoclonal broadly neutralizing antibodies (bnAbs) are currently being developed for preventing HIV-1 acquisition. Recent work has focused on predicting in vitro neutralization potency of both individual bnAbs and combination regimens against HIV-1 pseudoviruses using Env sequence features. To predict in vitro combination regimen neutralization potency against a given HIV-1 pseudovirus, previous approaches have applied mathematical models to combine individual-bnAb neutralization and have predicted this combined neutralization value; we call this the combine-then-predict (CP) approach. However, prediction performance for some individual bnAbs has exceeded that for the combination, leading to another possibility: combining the individual-bnAb predicted values and using these to predict combination regimen neutralization; we call this the predict-then-combine (PC) approach. We explore both approaches in both simulated data and data from the Los Alamos National Laboratory's Compile, Neutralize, and Tally NAb Panels repository. The CP approach is superior to the PC approach when the neutralization outcome of interest is binary (e.g., neutralization susceptibility, defined as inhibitory 80% concentration < 1 μg/mL). For continuous outcomes, the CP approach performs nearly as well as the PC approach when the individual-bnAb prediction algorithms have strong performance, and is superior to the PC approach when the individual-bnAb prediction algorithms have poor performance. This knowledge may be used when building prediction models for novel antibody combinations in the absence of in vitro neutralization data for the antibody combination; this, in turn, will aid in the evaluation and down-selection of these antibody combinations into prevention efficacy trials.

Prevention efficacy of the broadly neutralizing antibody VRC01 depends on HIV-1 envelope sequence features

In the Antibody Mediated Prevention (AMP) trials (HVTN 704/HPTN 085 and HVTN 703/HPTN 081), prevention efficacy (PE) of the monoclonal broadly neutralizing antibody (bnAb) VRC01 (vs. placebo) against HIV-1 acquisition diagnosis varied according to the HIV-1 Envelope (Env) neutralization sensitivity to VRC01, as measured by 80% inhibitory concentration (IC80). Here, we performed a genotypic sieve analysis, a complementary approach to gaining insight into correlates of protection that assesses how PE varies with HIV-1 sequence features. We analyzed HIV-1 Env amino acid (AA) sequences from the earliest available HIV-1 RNA-positive plasma samples from AMP participants diagnosed with HIV-1 and identified Env sequence features that associated with PE. The strongest Env AA sequence correlate in both trials was VRC01 epitope distance that quantifies the divergence of the VRC01 epitope in an acquired HIV-1 isolate from the VRC01 epitope of reference HIV-1 strains that were most sensitive to VRC01-mediated neutralization. In HVTN 704/HPTN 085, the Env sequence-based predicted probability that VRC01 IC80 against the acquired isolate exceeded 1 µg/mL also significantly associated with PE. In HVTN 703/HPTN 081, a physicochemical-weighted Hamming distance across 50 VRC01 binding-associated Env AA positions of the acquired isolate from the most VRC01-sensitive HIV-1 strain significantly associated with PE. These results suggest that incorporating mutation scoring by BLOSUM62 and weighting by the strength of interactions at AA positions in the epitope:VRC01 interface can optimize performance of an Env sequence-based biomarker of VRC01 prevention efficacy. Future work could determine whether these results extend to other bnAbs and bnAb combinations.

Predicting neutralization susceptibility to combination HIV-1 monoclonal broadly neutralizing antibody regimens

Combination monoclonal broadly neutralizing antibodies (bnAbs) are currently being developed for preventing HIV-1 infection. Recent work has focused on predicting in vitro neutralization potency of both individual bnAbs and combination regimens against HIV-1 pseudoviruses using Env sequence features. To predict in vitro combination regimen neutralization potency against a given HIV-1 pseudovirus, previous approaches have applied mathematical models to combine individual-bnAb neutralization and have predicted this combined neutralization value; we call this the combine-then-predict (CP) approach. However, prediction performance for some individual bnAbs has exceeded that for the combination, leading to another possibility: combining the individual-bnAb predicted values and using these to predict combination regimen neutralization; we call this the predict-then-combine (PC) approach. We explore both approaches in both simulated data and data from the Los Alamos National Laboratory's Compile, Neutralize, and Tally NAb Panels repository. The CP approach is superior to the PC approach when the neutralization outcome of interest is binary (e.g., neutralization susceptibility, defined as inhibitory concentration < 1 μg/mL. For continuous outcomes, the CP approach performs at least as well as the PC approach, and is superior to the PC approach when the individual-bnAb prediction algorithms have poor performance. This knowledge may be used when building prediction models for novel antibody combinations in the absence of in vitro neutralization data for the antibody combination; this, in turn, will aid in the evaluation and down-selection of these antibody combinations into prevention efficacy trials.

EFFICIENT ESTIMATION OF THE MAXIMAL ASSOCIATION BETWEEN MULTIPLE PREDICTORS AND A SURVIVAL OUTCOME

This paper develops a new approach to post-selection inference for screening high-dimensional predictors of survival outcomes. Post-selection inference for right-censored outcome data has been investigated in the literature, but much remains to be done to make the methods both reliable and computationally-scalable in high-dimensions. Machine learning tools are commonly used to provide predictions of survival outcomes, but the estimated effect of a selected predictor suffers from confirmation bias unless the selection is taken into account. The new approach involves the construction of semi-parametrically efficient estimators of the linear association between the predictors and the survival outcome, which are used to build a test statistic for detecting the presence of an association between any of the predictors and the outcome. Further, a stabilization technique reminiscent of bagging allows a normal calibration for the resulting test statistic, which enables the construction of confidence intervals for the maximal association between predictors and the outcome and also greatly reduces computational cost. Theoretical results show that this testing procedure is valid even when the number of predictors grows superpolynomially with sample size, and our simulations support this asymptotic guarantee at moderate sample sizes. The new approach is applied to the problem of identifying patterns in viral gene expression associated with the potency of an antiviral drug.

Application of the SLAPNAP statistical learning tool to broadly neutralizing antibody HIV prevention research

Combination monoclonal broadly neutralizing antibody (bnAb) regimens are in clinical development for HIV prevention, necessitating additional knowledge of bnAb neutralization potency/breadth against circulating viruses. Williamson et al. (2021) described a software tool, Super LeArner Prediction of NAb Panels (SLAPNAP), with application to any HIV bnAb regimen with sufficient neutralization data against a set of viruses in the Los Alamos National Laboratory's Compile, Neutralize, and Tally Nab Panels repository. SLAPNAP produces a proteomic antibody resistance (PAR) score for Env sequences based on predicted neutralization resistance and estimates variable importance of Env amino acid features. We apply SLAPNAP to compare HIV bnAb regimens undergoing clinical testing, finding improved power for downstream sieve analyses and increased precision for comparing neutralization potency/breadth of bnAb regimens due to the inclusion of PAR scores of Env sequences with much larger sample sizes available than for neutralization outcomes. SLAPNAP substantially improves bnAb regimen characterization, ranking, and down-selection.

Development of screening assays for use of broadly neutralizing antibodies in people with HIV

PURPOSE OF REVIEW

Treatment with combinations of complementary broadly neutralizing antibodies (bnAbs) has increased the proportion of participants for whom bnAbs can maintain virus suppression upon cessation of antiretroviral therapy (ART). There remains, however, a population of trial participants who experience virus rebound despite high plasma concentrations of bnAbs. Thus, baseline resistance remains a critical barrier to the efficacy of bnAbs for use in the treatment and cure of HIV, and the development of a screening assay to guide bnAb selection is a high priority.

RECENT FINDINGS

There are two conceptual approaches to assess the putative rebound-competent HIV-1 reservoir for bnAb sensitivity: to assess neutralization sensitivity of reactivated virus in outgrowth assays and sequence-based approaches that include a selection for intact genomes and assessment of known resistance mutations within the env gene. Currently, the only phenotypic assay for bnAb screening that is clinical laboratory improvement amendments certified (CLIA certified) and available for clinical trial use is Monogram Biosciences' PhenoSense HIV Neutralizing Antibody Assay.

SUMMARY

Several new approaches for screening are currently under development and future screening methods must address three issues. First, complete sampling of the reservoir may be impossible, and determination of the relevance of partial sampling is needed. Second, multiple lines of evidence indicate that in vitro neutralization measures are at least one correlate of in vivo bnAb activity that should be included in screening, but more research is needed on how to use in vitro neutralization assays and other measures of antibody functions and measures of other antibody features. Third, the feasibility of screening assays must be a priority. A feasible, predictive bnAb screening assay will remain relevant until a time when bnAb combinations are substantially more broad and potent.

Strategies for HIV-1 vaccines that induce broadly neutralizing antibodies

After nearly four decades of research, a safe and effective HIV-1 vaccine remains elusive. There are many reasons why the development of a potent and durable HIV-1 vaccine is challenging, including the extraordinary genetic diversity of HIV-1 and its complex mechanisms of immune evasion. HIV-1 envelope glycoproteins are poorly recognized by the immune system, which means that potent broadly neutralizing antibodies (bnAbs) are only infrequently induced in the setting of HIV-1 infection or through vaccination. Thus, the biology of HIV-1-host interactions necessitates novel strategies for vaccine development to be designed to activate and expand rare bnAb-producing B cell lineages and to select for the acquisition of critical improbable bnAb mutations. Here we discuss strategies for the induction of potent and broad HIV-1 bnAbs and outline the steps that may be necessary for ultimate success.

Structure-Based Identification of Natural-Product-Derived Compounds with Potential to Inhibit HIV-1 Entry

Broadly neutralizing antibodies (bNAbs) are potent in neutralizing a wide range of HIV strains. VRC01 is a CD4-binding-site (CD4-bs) class of bNAbs that binds to the conserved CD4-binding region of HIV-1 envelope (env) protein. Natural products that mimic VRC01 bNAbs by interacting with the conserved CD4-binding regions may serve as a new generation of HIV-1 entry inhibitors by being broadly reactive and potently neutralizing. This study aimed to identify compounds that mimic VRC01 by interacting with the CD4-bs of HIV-1 gp120 and thereby inhibiting viral entry into target cells. Libraries of purchasable natural products were virtually screened against clade A/E recombinant 93TH057 (PDB: 3NGB) and clade B (PDB ID: 3J70) HIV-1 env protein. Protein-ligand interaction profiling from molecular docking and dynamics simulations showed that the compounds had intermolecular hydrogen and hydrophobic interactions with conserved amino acid residues on the CD4-binding site of recombinant clade A/E and clade B HIV-1 gp120. Four potential lead compounds, NP-005114, NP-008297, NP-007422, and NP-007382, were used for cell-based antiviral infectivity inhibition assay using clade B (HXB2) env pseudotype virus (PV). The four compounds inhibited the entry of HIV HXB2 pseudotype viruses into target cells at 50% inhibitory concentrations (IC₅₀) of 15.2 µM (9.7 µg/mL), 10.1 µM (7.5 µg/mL), 16.2 µM (12.7 µg/mL), and 21.6 µM (12.9 µg/mL), respectively. The interaction of these compounds with critical residues of the CD4-binding site of more than one clade of HIV gp120 and inhibition of HIV-1 entry into the target cell demonstrate the possibility of a new class of HIV entry inhibitors.

The applications of machine learning in HIV neutralizing antibodies research-A systematic review

Machine learning algorithms play an essential role in bioinformatics and allow exploring the vast and noisy biological data in unrivaled ways. This paper is a systematic review of the applications of machine learning in the study of HIV neutralizing antibodies. This significant and vast research domain can pave the way to novel treatments and to a vaccine. We selected the relevant papers by investigating the available literature from the Web of Science and PubMed databases in the last decade. The computational methods are applied in neutralization potency prediction, neutralization span prediction against multiple viral strains, antibody-virus binding sites detection, enhanced antibodies design, and the study of the antibody-induced immune response. These methods are viewed from multiple angles spanning data processing, model description, feature selection, evaluation, and sometimes paper comparisons. The algorithms are diverse and include supervised, unsupervised, and generative types. Both classical machine learning and modern deep learning were taken into account. The review ends with our ideas regarding future research directions and challenges.

An analytical study on the identification of N-linked glycosylation sites using machine learning model

N-linked is the most common type of glycosylation which plays a significant role in identifying various diseases such as type I diabetes and cancer and helps in drug development. Most of the proteins cannot perform their biological and psychological functionalities without undergoing such modification. Therefore, it is essential to identify such sites by computational techniques because of experimental limitations. This study aims to analyze and synthesize the progress to discover N-linked places using machine learning methods. It also explores the performance of currently available tools to predict such sites. Almost seventy research articles published in recognized journals of the N-linked glycosylation field have shortlisted after the rigorous filtering process. The findings of the studies have been reported based on multiple aspects: publication channel, feature set construction method, training algorithm, and performance evaluation. Moreover, a literature survey has developed a taxonomy of N-linked sequence identification. Our study focuses on the performance evaluation criteria, and the importance of N-linked glycosylation motivates us to discover resources that use computational methods instead of the experimental method due to its limitations.

Prediction of HIV sensitivity to monoclonal antibodies using aminoacid sequences and deep learning

MOTIVATION

Knowing the sensitivity of a viral strain versus a monoclonal antibody is of interest for HIV vaccine development and therapy. The HIV strains vary in their resistance to antibodies, and the accurate prediction of virus-antibody sensitivity can be used to find potent antibody combinations that broadly neutralize multiple and diverse HIV strains. Sensitivity prediction can be combined with other methods such as generative algorithms to design novel antibodies in silico or with feature selection to uncover the sites of interest in the sequence. However, these tools are limited in the absence of in silico accurate prediction methods.

RESULTS

Our method leverages the CATNAP dataset, probably the most comprehensive collection of HIV-antibodies assays, and predicts the antibody-virus sensitivity in the form of binary classification. The methods proposed by others focus primarily on analyzing the virus sequences. However, our article demonstrates the advantages gained by modeling the antibody-virus sensitivity as a function of both virus and antibody sequences. The input is formed by the virus envelope and the antibody variable region aminoacid sequences. No structural features are required, which makes our system very practical, given that sequence data is more common than structures. We compare with two other state-of-the-art methods that leverage the same dataset and use sequence data only. Our approach, based on neuronal networks and transfer learning, measures increased predictive performance as measured on a set of 31 specific broadly neutralizing antibodies.

AVAILABILITY AND IMPLEMENTATION

https://github.com/vlad-danaila/deep_hiv_ab_pred/tree/fc-att-fix.

Optimizing clinical dosing of combination broadly neutralizing antibodies for HIV prevention

Broadly neutralizing antibodies (bNAbs) are promising agents to prevent HIV infection and achieve HIV remission without antiretroviral therapy (ART). As with ART, bNAb combinations are likely needed to cover HIV's extensive diversity. Not all bNAbs are identical in terms of their breadth, potency, and in vivo longevity (half-life). Given these differences, it is important to optimally select the composition, or dose ratio, of combination bNAb therapies for future clinical studies. We developed a model that synthesizes 1) pharmacokinetics, 2) potency against a wide HIV diversity, 3) interaction models for how drugs work together, and 4) correlates that translate in vitro potency to clinical protection. We found optimization requires drug-specific balances between potency, longevity, and interaction type. As an example, tradeoffs between longevity and potency are shown by comparing a combination therapy to a bi-specific antibody (a single protein merging both bNAbs) that takes the better potency but the worse longevity of the two components. Then, we illustrate a realistic dose ratio optimization of a triple combination of VRC07, 3BNC117, and 10-1074 bNAbs. We apply protection estimates derived from both a non-human primate (NHP) challenge study meta-analysis and the human antibody mediated prevention (AMP) trials. In both cases, we find a 2:1:1 dose emphasizing VRC07 is nearly optimal. Our approach can be immediately applied to optimize the next generation of combination antibody prevention and cure studies.

Immunotherapeutic approaches to HIV cure and remission

PURPOSE OF REVIEW

Despite improvements in the effectiveness of antiretroviral therapy (ART), there are still unmet needs for people living with HIV which drive the search for a cure for HIV infection. The goal of this review is to discuss the challenges and recent immunotherapeutic advances towards developing a safe, effective and durable cure strategy for HIV.

RECENT FINDINGS

In recent years, advances have been made in uncovering the mechanisms of persistence of latent HIV and in developing more accurate assays to measure the intact proviral reservoir. Broadly neutralising antibodies and modern techniques to enhance antibody responses have shown promising results. Other strategies including therapeutic vaccination, latency reversal agents, and immunomodulatory agents have shown limited success, but newer interventions including engineered T cells and other immunotherapies may be a potent and flexible strategy for achieving HIV cure.

SUMMARY

Although progress with newer cure strategies may be encouraging, challenges remain and it is essential to achieve a high threshold of safety and effectiveness in the era of safe and effective ART. It is likely that to achieve sustained HIV remission or cure, a multipronged approach involving a combination of enhancing both adaptive and innate immunity is required.

Immune correlates analysis of the mRNA-1273 COVID-19 vaccine efficacy clinical trial

In the coronavirus efficacy (COVE) phase 3 clinical trial, vaccine recipients were assessed for neutralizing and binding antibodies as correlates of risk for COVID-19 disease and as correlates of protection. These immune markers were measured at the time of second vaccination and 4 weeks later, with values reported in standardized World Health Organization international units. All markers were inversely associated with COVID-19 risk and directly associated with vaccine efficacy. Vaccine recipients with postvaccination 50% neutralization titers 10, 100, and 1000 had estimated vaccine efficacies of 78% (95% confidence interval, 54 to 89%), 91% (87 to 94%), and 96% (94 to 98%), respectively. These results help define immune marker correlates of protection and may guide approval decisions for messenger RNA (mRNA) COVID-19 vaccines and other COVID-19 vaccines.

A general framework for inference on algorithm-agnostic variable importance

In many applications, it is of interest to assess the relative contribution of features (or subsets of features) toward the goal of predicting a response - in other words, to gauge the variable importance of features. Most recent work on variable importance assessment has focused on describing the importance of features within the confines of a given prediction algorithm. However, such assessment does not necessarily characterize the prediction potential of features, and may provide a misleading reflection of the intrinsic value of these features. To address this limitation, we propose a general framework for nonparametric inference on interpretable algorithm-agnostic variable importance. We define variable importance as a population-level contrast between the oracle predictiveness of all available features versus all features except those under consideration. We propose a nonparametric efficient estimation procedure that allows the construction of valid confidence intervals, even when machine learning techniques are used. We also outline a valid strategy for testing the null importance hypothesis. Through simulations, we show that our proposal has good operating characteristics, and we illustrate its use with data from a study of an antibody against HIV-1 infection.

Super LeArner Prediction of NAb Panels (SLAPNAP): a containerized tool for predicting combination monoclonal broadly neutralizing antibody sensitivity

MOTIVATION

A single monoclonal broadly neutralizing antibody (bnAb) regimen was recently evaluated in two randomized trials for prevention efficacy against HIV-1 infection. Subsequent trials will evaluate combination bnAb regimens (e.g. cocktails, multi-specific antibodies), which demonstrate higher potency and breadth in vitro compared to single bnAbs. Given the large number of potential regimens, methods for down-selecting these regimens into efficacy trials are of great interest.

RESULTS

We developed Super LeArner Prediction of NAb Panels (SLAPNAP), a software tool for training and evaluating machine learning models that predict in vitro neutralization sensitivity of HIV Envelope (Env) pseudoviruses to a given single or combination bnAb regimen, based on Env amino acid sequence features. SLAPNAP also provides measures of variable importance of sequence features. By predicting bnAb coverage of circulating sequences, SLAPNAP can improve ranking of bnAb regimens by their potential prevention efficacy. In addition, SLAPNAP can improve sieve analysis by defining sequence features that impact bnAb prevention efficacy.

AVAILABILITYAND IMPLEMENTATION

SLAPNAP is a freely available docker image that can be downloaded from DockerHub (https://hub.docker.com/r/slapnap/slapnap). Source code and documentation are available at GitHub (https://github.com/benkeser/slapnap and https://benkeser.github.io/slapnap/).

Antibodies for Human Immunodeficiency Virus-1 Cure Strategies

Human immunodeficiency virus (HIV) infection leads to the establishment of a long-lived latent cellular reservoir. One strategy to eliminate quiescent reservoir cells is to reactivate virus replication to induce HIV envelope glycoprotein (Env) expression on the cell surface exposing them to subsequent antibody targeting. Via the interactions between the antibody Fc domain and Fc-γ receptors (FcγRs) that are expressed on innate effector cells, such as natural killer cells, monocytes, and neutrophils, antibodies can mediate the elimination of infected cells. Over the last decade, a multitude of human monoclonal antibodies that are broadly neutralizing across many HIV-1 subtypes have been identified and are currently being explored for HIV eradication strategies. Antibody development also includes novel Fc engineering approaches to increase engagement of effector cells and optimize antireservoir efficacy. In this review, we discuss the usefulness of antibodies for HIV eradication approaches specifically focusing on antibody-mediated strategies to target latently infected cells and options to increase antibody efficacy.

Broadly Neutralizing Antibodies for HIV-1 Prevention

Given the absence of an effective vaccine for protection against HIV-1 infection, passive immunization strategies that utilize potent broadly neutralizing antibodies (bnAbs) to block acquisition of HIV-1 are being rigorously pursued in the clinical setting. bnAbs have demonstrated robust protection in preclinical animal models, and several leading bnAb candidates have shown favorable safety and pharmacokinetic profiles when tested individually or in combinations in early phase human clinical trials. Furthermore, passive administration of bnAbs in HIV-1 infected individuals has resulted in prolonged suppression of viral rebound following interruption of combination antiretroviral therapy, and robust antiviral activity when administered to viremic individuals. Recent results from the first efficacy trials testing repeated intravenous administrations of the anti-CD4 binding site bnAb VRC01 have demonstrated positive proof of concept that bnAb passive immunization can confer protection against HIV-1 infection in humans, but have also highlighted the considerable barriers that remain for such strategies to effectively contribute to control of the epidemic. In this review, we discuss the current status of clinical studies evaluating bnAbs for HIV-1 prevention, highlight lessons learned from the recent Antibody Mediated Prevention (AMP) efficacy trials, and provide an overview of strategies being employed to improve the breadth, potency, and durability of antiviral protection.

Accurate Prediction for Antibody Resistance of Clinical HIV-1 Isolates

Broadly neutralizing antibodies (bNAbs) targeting the HIV-1 envelope glycoprotein (Env) have promising utility in prevention and treatment of HIV-1 infection, and several are currently undergoing clinical trials. Due to the high sequence diversity and mutation rate of HIV-1, viral isolates are often resistant to specific bNAbs. Currently, resistant isolates are commonly identified by time-consuming and expensive in vitro neutralization assays. Here, we report machine learning classifiers that accurately predict resistance of HIV-1 isolates to 33 bNAbs. Notably, our classifiers achieved an overall prediction accuracy of 96% for 212 clinical isolates from patients enrolled in four different clinical trials. Moreover, use of gradient boosting machine – a tree-based machine learning method – enabled us to identify critical features, which had high accordance with epitope residues that distinguished between antibody resistance and sensitivity. The availability of an in silico antibody resistance predictor should facilitate informed decisions of antibody usage and sequence-based monitoring of viral escape in clinical settings.

Predicting the broadly neutralizing antibody susceptibility of the HIV reservoir

Broadly neutralizing antibodies (bNAbs) against HIV-1 are under evaluation for both prevention and therapy. HIV-1 sequence diversity observed in most HIV-infected individuals and archived variations in critical bNAb epitopes present a major challenge for the clinical application of bNAbs, as preexistent resistant viral strains can emerge, resulting in bNAb failure to control HIV. In order to identify viral resistance in patients prior to antibody therapy and to guide the selection of effective bNAb combination regimens, we developed what we believe to be a novel Bayesian machine-learning model that uses HIV-1 envelope protein sequences and foremost approximated glycan occupancy information as variables to quantitatively predict the half-maximal inhibitory concentrations (IC50) of 126 neutralizing antibodies against a variety of cross clade viruses. We then applied this model to peripheral blood mononuclear cell-derived proviral Env sequences from 25 HIV-1-infected individuals mapping the landscape of neutralization resistance within each individual's reservoir and determined the predicted ideal bNAb combination to achieve 100% neutralization at IC50 values <1 μg/ml. Furthermore, predicted cellular viral reservoir neutralization signatures of individuals before an analytical antiretroviral treatment interruption were consistent with the measured neutralization susceptibilities of the respective plasma rebound viruses, validating our model as a potentially novel tool to facilitate the advancement of bNAbs into the clinic.

Peptide-Based Vaccination for Antibody Responses Against HIV

HIV-1 is responsible for a global pandemic of 35 million people and continues to spread at a rate of >2 million new infections/year. It is widely acknowledged that a protective vaccine would be the most effective means to reduce HIV-1 spread and ultimately eliminate the pandemic, whereas a therapeutic vaccine might help to mitigate the clinical course of the disease and to contribute to virus eradication strategies. However, despite more than 30 years of research, we do not have a vaccine capable of protecting against HIV-1 infection or impacting on disease progression. This, in part, denotes the challenge of identifying immunogens and vaccine modalities with a reduced risk of failure in late stage development. However, progress has been made in epitope identification for the induction of broadly neutralizing antibodies. Thus, peptide-based vaccination has become one of the challenges of this decade. While some researchers reconstitute envelope protein conformation and stabilization to conserve the epitope targeted by neutralizing antibodies, others have developed strategies based on peptide-carrier vaccines with a similar goal. Here, we will review the major peptide-carrier based approaches in the vaccine field and their application and recent development in the HIV-1 field.