VICTORIA, BC / ACCESSWIRE / April 22, 2024 / ImmunoPrecise Antibodies Ltd. (NASDAQ:IPA), an AI-driven biotherapeutic research and technology company, has recently announced an expansion of its already successful LENS^{ai TM} Platform. LENS^ai, which is run by the company's subsidiary, BioStrand, provides a unique and comprehensive view of life sciences data by linking sequence, structure, function and literature information from the entire biosphere. The platform is now integrating epitope binning into its formulas.

Epitope binning is a method used to compare and categorize a collection of monoclonal antibodies that are designed to target a specific protein. In this process, each antibody is tested against all the others to see if they interfere with each other's ability to bind to the target protein. By doing this, scientists can determine which antibodies have similar or related binding sites on the target protein. Antibodies with similar binding sites are grouped together, or "binned," based on their interactions with each other.

The main goal of epitope binning is to group antibodies that have similar target binding properties, which helps researchers understand the characteristics and behavior of different antibodies and their potential in targeting specific proteins for various applications, such as drug development or disease diagnosis.

To achieve accurate epitope binning, LENS^ai's algorithm incorporates multiple components. It analyzes the sequential and structural profiles of the antibodies, which means it examines the specific sequence and 3D structure of the antibodies to understand their binding capabilities. It also takes into account docking information, which considers factors like steric hindrance and glycosylation sites that may affect the antibody-antigen interaction. LENS^ai's algorithm then looks at the atomic interactions between the antibody-antigen complexes to gain a better understanding of their binding specificity.

In a recently published case study, LENS^ai applied its epitope binning algorithm to a set of 29 antibody sequences that targeted a transmembrane protein. The results obtained from LENS^ai's in silico clustering analysis were then compared to the data from classical wet lab binning procedures.

The results showed a high level of agreement between LENS^ai's in silico Epitope Binning and classical wet lab binning. In other words, LENS^ai's algorithm could accurately categorize and identify the epitopes in a similar manner to the traditional experimental approach. These findings demonstrate that LENS^ai Epitope Binning can effectively match the results of in vitro competition assays, providing researchers with high-confidence predictions of antibody-antigen interactions.

This case study highlights the potential of LENS^ai's algorithm in addressing the challenges presented by the increasing number of antibodies generated in discovery campaigns. By offering both high accuracy and scalability, LENS^ai's in silico binning approach can support the early stages of antibody discovery, enabling researchers to efficiently analyze a large volume of diverse antibodies and select the most promising candidates for further investigation.

In silico epitope binning powered by LENS^ai technology thus offers a pivotal advancement, with its ability to analyze over 5,000 sequences, delivering rapid insights for early triaging. Its algorithms enhance biological research, offering accurate, high-throughput candidate selection while reducing time and costs. For small subsets with less than 5,000 antibodies, it can deliver results within mere hours. Furthermore, it requires only protein sequences and no physical materials - reducing the effort involved even more.

This platform is further reinforcing BioStrand's position at the forefront of AI-driven biotherapeutic research and technology. The market for AI in healthcare is forecasted to reach $187.95 billion by 2030. ImmunoPrecise Antibodies and its subsidiary seem well-positioned to lead the AI and healthcare industry in the field of antibodies.

Featured photo by National Cancer Institute on Unsplash.

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