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

Current methods for detecting and assessing HIV-1 antibody resistance

Antiretroviral therapy is the standard treatment for HIV, but it requires daily use and can cause side effects. Despite being available for decades, there are still 1.5 million new infections and 700,000 deaths each year, highlighting the need for better therapies. Broadly neutralizing antibodies (bNAbs), which are highly active against HIV-1, represent a promising new approach and clinical trials have demonstrated the potential of bNAbs in the treatment and prevention of HIV-1 infection. However, HIV-1 antibody resistance (HIVAR) due to variants in the HIV-1 envelope glycoproteins (HIV-1 Env) is not well understood yet and poses a critical problem for the clinical use of bNAbs in treatment. HIVAR also plays an important role in the future development of an HIV-1 vaccine, which will require elicitation of bNAbs to which the circulating strains are sensitive. In recent years, a variety of methods have been developed to detect, characterize and predict HIVAR. Structural analysis of antibody-HIV-1 Env complexes has provided insight into viral residues critical for neutralization, while testing of viruses for antibody susceptibility has verified the impact of some of these residues. In addition, in vitro viral neutralization and adaption assays have shaped our understanding of bNAb susceptibility based on the envelope sequence. Furthermore, in vivo studies in animal models have revealed the rapid emergence of escape variants to mono- or combined bNAb treatments. Finally, similar variants were found in the first clinical trials testing bNAbs for the treatment of HIV-1-infected patients. These structural, in vitro, in vivo and clinical studies have led to the identification and validation of HIVAR for almost all available bNAbs. However, defined assays for the detection of HIVAR in patients are still lacking and for some novel, highly potent and broad-spectrum bNAbs, HIVAR have not been clearly defined. Here, we review currently available approaches for the detection, characterization and prediction of HIVAR.

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.

Artificial intelligence in assisting pathogenic microorganism diagnosis and treatment: a review of infectious skin diseases

The skin, the largest organ of the human body, covers the body surface and serves as a crucial barrier for maintaining internal environmental stability. Various microorganisms such as bacteria, fungi, and viruses reside on the skin surface, and densely arranged keratinocytes exhibit inhibitory effects on pathogenic microorganisms. The skin is an essential barrier against pathogenic microbial infections, many of which manifest as skin lesions. Therefore, the rapid diagnosis of related skin lesions is of utmost importance for early treatment and intervention of infectious diseases. With the continuous rapid development of artificial intelligence, significant progress has been made in healthcare, transforming healthcare services, disease diagnosis, and management, including a significant impact in the field of dermatology. In this review, we provide a detailed overview of the application of artificial intelligence in skin and sexually transmitted diseases caused by pathogenic microorganisms, including auxiliary diagnosis, treatment decisions, and analysis and prediction of epidemiological characteristics.

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.

Next-Generation Vaccine Development with Nanomaterials: Recent Advances, Possibilities, and Challenges

Nanomaterials are becoming important tools for vaccine development owing to their tunable and adaptable nature. Unique properties of nanomaterials afford opportunities to modulate trafficking through various tissues, complement or augment adjuvant activities, and specify antigen valency and display. This versatility has enabled recent work designing nanomaterial vaccines for a broad range of diseases, including cancer, inflammatory diseases, and various infectious diseases. Recent successes of nanoparticle vaccines during the coronavirus disease 2019 (COVID-19) pandemic have fueled enthusiasm further. In this review, the most recent developments in nanovaccines for infectious disease, cancer, inflammatory diseases, allergic diseases, and nanoadjuvants are summarized. Additionally, challenges and opportunities for clinical translation of this unique class of materials are discussed.

Tackling the Antimicrobial Resistance "Pandemic" with Machine Learning Tools: A Summary of Available Evidence

Antimicrobial resistance is recognised as one of the top threats healthcare is bound to face in the future. There have been various attempts to preserve the efficacy of existing antimicrobials, develop new and efficient antimicrobials, manage infections with multi-drug resistant strains, and improve patient outcomes, resulting in a growing mass of routinely available data, including electronic health records and microbiological information that can be employed to develop individualised antimicrobial stewardship. Machine learning methods have been developed to predict antimicrobial resistance from whole-genome sequencing data, forecast medication susceptibility, recognise epidemic patterns for surveillance purposes, or propose new antibacterial treatments and accelerate scientific discovery. Unfortunately, there is an evident gap between the number of machine learning applications in science and the effective implementation of these systems. This narrative review highlights some of the outstanding opportunities that machine learning offers when applied in research related to antimicrobial resistance. In the future, machine learning tools may prove to be superbugs' kryptonite. This review aims to provide an overview of available publications to aid researchers that are looking to expand their work with new approaches and to acquaint them with the current application of machine learning techniques in this field.

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.

A state-of-the-art analysis of pharmacological delivery and artificial intelligence techniques for inner ear disease treatment

Over the last several decades, both the academic and therapeutic fields have seen significant progress in the delivery of drugs to the inner ear due to recent delivery methods established for the systemic administration of drugs in inner ear treatment. Novel technologies such as nanoparticles and hydrogels are being investigated, in addition to the traditional treatment methods. Intracochlear devices, which utilize current developments in microsystems technology, are on the horizon of inner ear drug delivery methods and are designed to provide medicine directly into the inner ear. These devices are used for stem cell treatment, RNA interference, and the delivery of neurotrophic factors and steroids during cochlear implantation. An in-depth analysis of artificial neural networks (ANNs) in pharmaceutical research may be found in ANNs for Drug Delivery, Design, and Disposition. This prediction tool has a great deal of promise to assist researchers in more successfully designing, developing, and delivering successful medications because of its capacity to learn and self-correct in a very complicated environment. ANN achieved a high level of accuracy exceeding 0.90, along with a sensitivity of 95% and a specificity of 100%, in accurately distinguishing illness. Additionally, the ANN model provided nearly perfect measures of 0.99%. Nanoparticles exhibit potential as a viable therapeutic approach for bacterial infections that are challenging to manage, such as otitis media. The utilization of ANNs has the potential to enhance the effectiveness of nanoparticle therapy, particularly in the realm of automated identification of otitis media. Polymeric nanoparticles have demonstrated effectiveness in the treatment of prevalent bacterial infections in pediatric patients, suggesting significant potential for forthcoming therapeutic interventions. Finally, this study is based on a research of how inner ear diseases have been treated in the last ten years (2012-2022) using machine learning.

To prescreen or not to prescreen for broadly neutralizing antibody sensitivity in HIV cure-related trials

The use of broadly neutralizing antibodies (bNAbs) as a cure-related research strategy for human immunodeficiency virus (HIV) has gained attention from the scientific community. bNAbs are specialized antibodies that target HIV-1 by binding to proteins on the surface of the virus, preventing the infection of human cells. In HIV-1 clinical studies assessing the use of bNAbs, it has been common practice to prescreen potential participants for bNAb sensitivity. However, the use of pre-screening in HIV-1 bNAb clinical trials is a topic of ongoing debate, with regard to its potential benefits and limitations. In this paper, we examine the possible benefits and limitations of pre-screening for bNAb sensitivity in HIV-1 cure-related studies, and suggest alternative methods which may be more effective or efficient at saving costs and time. Ultimately, the decision to use pre-screening in HIV-1 bNAb clinical trials should be based on a careful assessment of the potential benefits and limitations of this approach, as well as the specific needs, goals, design, and population of the study in question.

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.

A calculated risk: Evaluating HIV resistance to the broadly neutralising antibodies10-1074 and 3BNC117

PURPOSE OF THIS REVIEW

Broadly neutralising antibodies (bNAbs) are a promising new therapy for the treatment of HIV infection. However, the effective use of bNAbs is impacted by the presence of preexisting virological resistance and the potential to develop new resistance during treatment. With several bNAb clinical trials underway, sensitive and scalable assays are needed to screen for resistance. This review summarises the data on resistance from published clinical trials using the bNAbs 10-1074 and 3BNC117 and evaluates current approaches for detecting bNAb sensitivity as well as their limitations.

RECENT FINDINGS

Analyses of samples from clinical trials of 10-1074 and 3BNC117 reveal viral mutations that emerge on therapy which may result in bNAb resistance. These mutations are also found in some potential study participants prior to bNAb exposure. These clinical data are further informed by ex-vivo neutralisation assays which offer an alternative measure of resistance and allow more detailed interrogation of specific viral mutations. However, the limited amount of publicly available data and the need for better understanding of other viral features that may affect bNAb binding mean there is no widely accepted approach to measuring bNAb resistance.

SUMMARY

Resistance to the bNAbs 10-1074 and 3BNC117 may significantly impact clinical outcome following their therapeutic administration. Predicting bNAb resistance may help to lower the risk of treatment failure and therefore a robust methodology to screen for bNAb sensitivity is needed.

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.

Artificial Intelligence in Vaccine and Drug Design

Knowledge in the fields of biochemistry, structural biology, immunological principles, microbiology, and genomics has all increased dramatically in recent years. There has also been tremendous growth in the fields of data science, informatics, and artificial intelligence needed to handle this immense data flow. At the intersection of wet lab and data science is the field of bioinformatics, which seeks to apply computational tools to better understanding of the biological sciences. Like so many other areas of biology, bioinformatics has transformed immunology research leading to the discipline of immunoinformatics. Within this field, many new databases and computational tools have been created that increasingly drive immunology research, in many cases drawing upon artificial intelligence and machine learning to predict complex immune system behaviors, for example, prediction of B cell and T cell epitopes. In this book chapter, we provide an overview of computational tools and artificial intelligence being used for protein modeling, drug screening, vaccine design, and highlight how these tools are being used to transform approaches to pandemic countermeasure development, by reference to the current COVID-19 pandemic.

A Study of the Recent Trends of Immunology: Key Challenges, Domains, Applications, Datasets, and Future Directions

The human immune system is very complex. Understanding it traditionally required specialized knowledge and expertise along with years of study. However, in recent times, the introduction of technologies such as AIoMT (Artificial Intelligence of Medical Things), genetic intelligence algorithms, smart immunological methodologies, etc., has made this process easier. These technologies can observe relations and patterns that humans do and recognize patterns that are unobservable by humans. Furthermore, these technologies have also enabled us to understand better the different types of cells in the immune system, their structures, their importance, and their impact on our immunity, particularly in the case of debilitating diseases such as cancer. The undertaken study explores the AI methodologies currently in the field of immunology. The initial part of this study explains the integration of AI in healthcare and how it has changed the face of the medical industry. It also details the current applications of AI in the different healthcare domains and the key challenges faced when trying to integrate AI with healthcare, along with the recent developments and contributions in this field by other researchers. The core part of this study is focused on exploring the most common classifications of health diseases, immunology, and its key subdomains. The later part of the study presents a statistical analysis of the contributions in AI in the different domains of immunology and an in-depth review of the machine learning and deep learning methodologies and algorithms that can and have been applied in the field of immunology. We have also analyzed a list of machine learning and deep learning datasets about the different subdomains of immunology. Finally, in the end, the presented study discusses the future research directions in the field of AI in immunology and provides some possible solutions for the same.

Artificial intelligence and machine learning assisted drug delivery for effective treatment of infectious diseases

In the era of antimicrobial resistance, the prevalence of multidrug-resistant microorganisms that resist conventional antibiotic treatment has steadily increased. Thus, it is now unquestionable that infectious diseases are significant global burdens that urgently require innovative treatment strategies. Emerging studies have demonstrated that artificial intelligence (AI) can transform drug delivery to promote effective treatment of infectious diseases. In this review, we propose to evaluate the significance, essential principles, and popular tools of AI in drug delivery for infectious disease treatment. Specifically, we will focus on the achievements and key findings of current research, as well as the applications of AI on drug delivery throughout the whole antimicrobial treatment process, with an emphasis on drug development, treatment regimen optimization, drug delivery system and administration route design, and drug delivery outcome prediction. To that end, the challenges of AI in drug delivery for infectious disease treatments and their current solutions and future perspective will be presented and discussed.

Machine Learning in Discovery of New Antivirals and Optimization of Viral Infections Therapy

Nowadays, computational approaches play an important role in the design of new drug-like compounds and optimization of pharmacotherapeutic treatment of diseases. The emerging growth of viral infections, including those caused by the Human Immunodeficiency Virus (HIV), Ebola virus, recently detected coronavirus, and some others, leads to many newly infected people with a high risk of death or severe complications. A huge amount of chemical, biological, clinical data is at the disposal of the researchers. Therefore, there are many opportunities to find the relationships between the particular features of chemical data and the antiviral activity of biologically active compounds based on machine learning approaches. Biological and clinical data can also be used for building models to predict relationships between viral genotype and drug resistance, which might help determine the clinical outcome of treatment. In the current study, we consider machine-learning approaches in the antiviral research carried out during the past decade. We overview in detail the application of machine-learning methods for the design of new potential antiviral agents and vaccines, drug resistance prediction, and analysis of virus-host interactions. Our review also covers the perspectives of using the machine-learning approaches for antiviral research, including Dengue, Ebola viruses, Influenza A, Human Immunodeficiency Virus, coronaviruses, and some others.

Predicting Antibody Neutralization Efficacy in Hypermutated Epitopes Using Monte Carlo Simulations

Human Immunodeficiency Virus 1 (HIV-1) evades adaptive immunity by means of its extremely high mutation rate, which allows the HIV envelope glycoprotein to continuously escape from the action of antibodies. However, some broadly neutralizing antibodies (bNAbs) targeting specific viral regions show the ability to block the infectivity of a large number of viral variants. The discovery of these antibodies opens new avenues in anti-HIV therapy; however, they are still suboptimal tools as their amplitude of action ranges between 50% and 90% of viral variants. In this context, being able to discriminate between sensitive and resistant strains to an antibody would be of great interest for the design of optimal clinical antibody treatments and to engineer potent bNAbs for clinical use. Here, we describe a hierarchical procedure to predict the antibody neutralization efficacy of multiple viral isolates to three well-known anti-CD4bs bNAbs: VRC01, NIH45-46 and 3BNC117. Our method consists of simulating the three-dimensional binding process between the gp120 and the antibody by using Protein Energy Landscape Exploration (PELE), a Monte Carlo stochastic approach. Our results clearly indicate that the binding profiles of sensitive and resistant strains to a bNAb behave differently, showing the latter’s weaker binding profiles, that can be exploited for predicting antibody neutralization efficacy in hypermutated HIV-1 strains.

Learning Robust Multilabel Sample Specific Distances for Identifying HIV-1 Drug Resistance

AIDS is a syndrome caused by the HIV. During the progression of AIDS, a patient's immune system is weakened, which increases the patient's susceptibility to infections and diseases. Although antiretroviral drugs can effectively suppress HIV, the virus mutates very quickly and can become resistant to treatment. In addition, the virus can also become resistant to other treatments not currently being used through mutations, which is known in the clinical research community as cross-resistance. Since a single HIV strain can be resistant to multiple drugs, this problem is naturally represented as a multilabel classification problem. Given this multilabel relationship, traditional single-label classification methods often fail to effectively identify the drug resistances that may develop after a particular virus mutation. In this work, we propose a novel multilabel Robust Sample Specific Distance (RSSD) method to identify multiclass HIV drug resistance. Our method is novel in that it can illustrate the relative strength of the drug resistance of a reverse transcriptase (RT) sequence against a given drug nucleoside analog and learn the distance metrics for all the drug resistances. To learn the proposed RSSDs, we formulate a learning objective that maximizes the ratio of the summations of a number of ℓ₁-norm distances, which is difficult to solve in general. To solve this optimization problem, we derive an efficient, nongreedy iterative algorithm with rigorously proved convergence. Our new method has been verified on a public HIV type 1 drug resistance data set with over 600 RT sequences and five nucleoside analogs. We compared our method against several state-of-the-art multilabel classification methods, and the experimental results have demonstrated the effectiveness of our proposed method.

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.

Effect of diversity in gp41 membrane proximal external region of primary HIV-1 Indian subtype C sequences on interaction with broadly neutralizing antibodies 4E10 and 10E8

Human Immunodeficiency Virus-1 Clade C (HIV-1C) dominates the AIDS epidemic in India, afflicting 2.1 million individuals within the country and more than 15 million people worldwide. Membrane proximal external region (MPER) is an attractive target for broadly neutralizing antibody (bNAb) based therapies. However, information on MPER sequence diversity from India is meagre due to limited sampling of primary viral sequences. In the present study, we examined the variation in MPER of HIV-1C from 24 individuals in Mumbai, India by high throughput sequencing of uncultured viral sequences. Deep sequencing of MPER (662-683; HXB2 envelope amino acid numbering) allowed quantification of intra-individual variation up to 65% at positions 662, 665, 668, 674 and 677 within this region. These variable positions included contact sites targeted by bNAbs 2F5, Z13e1, 4E10 as well as 10E8. Both major and minor epitope variants i.e. 'haplotypes' were generated for each sample dataset. A total of 23, 34 and 25 unique epitope haplotypes could be identified for bNAbs 2F5, Z13e1 and 4E10/10E8 respectively. Further analysis of 4E10 and 10E8 epitopes from our dataset and meta-analysis of previously reported HIV-1 sequences from India revealed 26 epitopes (7 India-specific), heretofore untested for neutralization sensitivity. Peptide-Ab docking predicted 13 of these to be non-binding to 10E8. ELISA, Surface Plasmon Resonance and peptide inhibition of HIV-1 neutralization assays were then performed which validated predicted weak/non-binding interactions for peptides corresponding to six of these epitopes. These results highlight the under-representation of 10E8 non-binding HIV-1C MPER sequences from India. Our study thus underscores the need for increased surveillance of primary circulating envelope sequences for development of efficacious bNAb-based interventions in India.

HIV-1 Neutralizing Antibody Signatures and Application to Epitope-Targeted Vaccine Design

Eliciting HIV-1-specific broadly neutralizing antibodies (bNAbs) remains a challenge for vaccine development, and the potential of passively delivered bNAbs for prophylaxis and therapeutics is being explored. We used neutralization data from four large virus panels to comprehensively map viral signatures associated with bNAb sensitivity, including amino acids, hypervariable region characteristics, and clade effects across four different classes of bNAbs. The bNAb signatures defined for the variable loop 2 (V2) epitope region of HIV-1 Env were then employed to inform immunogen design in a proof-of-concept exploration of signature-based epitope targeted (SET) vaccines. V2 bNAb signature-guided mutations were introduced into Env 459C to create a trivalent vaccine, and immunization of guinea pigs with V2-SET vaccines resulted in increased breadth of NAb responses compared with Env 459C alone. These data demonstrate that bNAb signatures can be utilized to engineer HIV-1 Env vaccine immunogens capable of eliciting antibody responses with greater neutralization breadth.

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HIV-1 bNAb sensitivity signatures from 4 large virus panels mapped across 4 Ab classes

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Non-contact hypervariable region characteristics are critical for bNAb sensitivity

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HIV-1 Env 459C used alone as a vaccine can elicit modest tier 2 NAbs in guinea pigs

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V2 bNAb signature-guided modifications in 459C enhanced neutralization breadth

Prediction of HIV-1 sensitivity to broadly neutralizing antibodies shows a trend towards resistance over time

Treatment with broadly neutralizing antibodies (bNAbs) has proven effective against HIV-1 infections in humanized mice, non-human primates, and humans. Due to the high mutation rate of HIV-1, resistance testing of the patient’s viral strains to the bNAbs is still inevitable. So far, bNAb resistance can only be tested in expensive and time-consuming neutralization experiments. Here, we introduce well-performing computational models that predict the neutralization response of HIV-1 to bNAbs given only the envelope sequence of the virus. Using non-linear support vector machines based on a string kernel, the models learnt even the important binding sites of bNAbs with more complex epitopes, i.e., the CD4 binding site targeting bNAbs, proving thereby the biological relevance of the models. To increase the interpretability of the models, we additionally provide a new kind of motif logo for each query sequence, visualizing those residues of the test sequence that influenced the prediction outcome the most. Moreover, we predicted the neutralization sensitivity of around 34,000 HIV-1 samples from different time points to a broad range of bNAbs, enabling the first analysis of HIV resistance to bNAbs on a global scale. The analysis showed for many of the bNAbs a trend towards antibody resistance over time, which had previously only been discovered for a small non-representative subset of the global HIV-1 population.

Several sequence-based approaches exist to predict the epitope of broadly neutralizing antibodies (bNAbs) against HIV based on the correlation between variation in the viral sequence and neutralization response to the antibody. Though the potential epitope sites can be used to predict the neutralization response, the methods are not optimized for the task, using additional structural information, additional preselection steps to identify the epitope sites, and assuming independence and/or only linear relationship between the potential sites and the neutralization response. To model also the neutralization response to bNAbs with more complex binding sites, including for example several non-consecutive residues or accompanying conformational changes, we used non-linear, multivariate machine learning techniques. Though we used only the viral sequence information, the models learnt the corresponding binding sites of the bNAbs. In general only few residues were learnt to be responsible for a change in neutralization response, which can additionally reduce the sequencing cost for application in clinical routine. We propose our tailored models to aid the patient selection process for current clinical trials for bNAb immunotherapy, but also as a basis to predict the best combinations of bNAbs, which will be required for routine clinical practice in the future.

Antibody-virus co-evolution in HIV infection: paths for HIV vaccine development

Induction of HIV-1 broadly neutralizing antibodies (bnAbs) to date has only been observed in the setting of HIV-1 infection, and then only years after HIV transmission. Thus, the concept has emerged that one path to induction of bnAbs is to define the viral and immunologic events that occur during HIV-1 infection, and then to mimic those events with a vaccine formulation. This concept has led to efforts to map both virus and antibody events that occur from the time of HIV-1 transmission to development of bnAbs. This work has revealed that a virus-antibody "arms race" occurs in which a HIV-1 transmitted/founder (TF) Env induces autologous neutralizing antibodies that can not only neutralize the TF virus but also can select virus escape mutants that in turn select affinity-matured neutralizing antibodies. From these studies has come a picture of bnAb development that has led to new insights in host-pathogen interactions and, as well, led to insight into immunologic mechanisms of control of bnAb development. Here, we review the progress to date in elucidating bnAb B cell lineages in HIV-1 infection, discuss new research leading to understanding the immunologic mechanisms of bnAb induction, and address issues relevant to the use of this information for the design of new HIV-1 sequential envelope vaccine candidates.