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Innovative Machine Learning-Driven Discovery of Broadly Neutralizing Antibodies Against HIV-1 Using the RAIN Computational Pipeline

Innovative Machine Learning-Driven Discovery of Broadly Neutralizing Antibodies Against HIV-1 Using the RAIN Computational Pipeline
Innovative Machine Learning-Driven Discovery of Broadly Neutralizing Antibodies Against HIV-1 Using the RAIN Computational Pipeline


Broadly neutralizing antibodies (bNAbs) are key in combating HIV-1. They target the virus’s envelope proteins and show promise in reducing viral loads and preventing infection. Despite their potential, identifying bNAbs remains labor-intensive, involving B-cell isolation and high-throughput next-generation sequencing. Only 255 bNAbs are known, and discovering new ones is challenging due to the virus’s rapid mutation and immune evasion mechanisms. AI tools could revolutionize this field by automatically detecting bNAbs from large immune datasets, but robust criteria for distinguishing bNAbs are still needed.

Researchers from various institutions, including Lausanne University Hospital, National Institutes of Health, and others, developed RAIN, a computational method for rapidly identifying bNAbs against HIV-1. Unlike traditional methods relying on amino acid sequences or structural alignment, RAIN uses selected sequence-based features and machine learning. Tested on experimentally obtained BCR repertoires, RAIN accurately predicted HIV-1 bNAbs, achieving 100% prediction accuracy and high AUC values. The validation included in vitro neutralization assays and cryo-EM structural analysis, confirming RAIN’s efficacy in identifying bNAbs from immune donors with broad neutralizing sera.

The study adhered to rigorous ethical guidelines, securing approvals from multiple institutional review boards, including those in Switzerland and Tanzania, and obtaining informed consent from all 25 participants. To investigate the immune response against HIV-1, serum IgG antibodies were isolated using a Protein G Sepharose method. This process involved incubating serum samples with the resin, eluting the IgGs, and desalting them before storage. Memory B cells were also isolated from peripheral blood mononuclear cells (PBMCs) using magnetic microbeads, followed by fluorescence-activated cell sorting (FACS) to achieve high purity of CD20+ IgG+ cells. These cells were subsequently subjected to single-cell B-cell receptor sequencing using three advanced platforms: 10X Genomics, BD Rhapsody, and Singleton, each employing specific protocols for cell capture, library preparation, and sequencing.

For functional analysis, recombinant antibodies and Fab fragments were produced in Expi293 cells and purified via Protein A or HisTrap chromatography. Neutralization assays were conducted to evaluate the antibodies’ effectiveness against a panel of HIV-1 strains, with binding kinetics assessed through biolayer interferometry. Structural studies of the antibodies interacting with the HIV-1 envelope glycoprotein (SOSIP) involved negative stain electron microscopy and high-resolution cryo-electron microscopy. Advanced data processing and structural modeling tools like CryoSPARC, ChimeraX, and Phenix were used to analyze these interactions. Furthermore, B-cell receptor (BCR) repertoires were sequenced and annotated to identify paired sequences targeting HIV-1, utilizing the CATNAP database and various machine-learning models to classify these BCRs based on their immunological features.

Identifying bNAbs against HIV-1 is challenging due to their significant sequence diversity. Traditional methods relying on sequence similarity fall short due to this variability. However, bNAbs exhibit characteristics like high somatic hypermutation, specific germline usage, and unique structural features, which can be leveraged. Researchers developed a machine-learning framework to automatically identify bNAbs by analyzing these traits. They curated antibody sequences, extracted distinctive features, and used algorithms like anomaly detection and random forests. These models effectively distinguished bNAbs from other antibodies, highlighting key predictive features and improving accuracy in identifying potential bNAbs from immune repertoires.

Innovative Machine Learning-Driven Discovery of Broadly Neutralizing Antibodies Against HIV-1 Using the RAIN Computational Pipeline

In the study, researchers aimed to identify bNAbs against HIV-1 from infected donors. They isolated and sequenced IgG-class B cells, focusing on a donor with known broad neutralization capabilities. Using a computational pipeline (RAIN), they identified three potential bNAbs, which showed high-affinity binding to the HIV-1 envelope and strong neutralizing activity. These findings were confirmed through biophysical and neutralization assays. The identified bNAbs, particularly bNAb4251, demonstrated broad and potent neutralization, underscoring the pipeline’s effectiveness in discovering therapeutic antibodies against HIV-1.


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Innovative Machine Learning-Driven Discovery of Broadly Neutralizing Antibodies Against HIV-1 Using the RAIN Computational Pipeline

Sana Hassan, a consulting intern at Marktechpost and dual-degree student at IIT Madras, is passionate about applying technology and AI to address real-world challenges. With a keen interest in solving practical problems, he brings a fresh perspective to the intersection of AI and real-life solutions.



Innovative Machine Learning-Driven Discovery of Broadly Neutralizing Antibodies Against HIV-1 Using the RAIN Computational Pipeline

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