share_log

Epitope Binning Powered By LENSai TM Technology Can Analyze Over 5,000 Sequences With No Physical Materials Needed, Matches Classical Wet Lab Binning Results

Epitope Binning Powered By LENSai TM Technology Can Analyze Over 5,000 Sequences With No Physical Materials Needed, Matches Classical Wet Lab Binning Results

由 LenSAI TM 技术提供支持的 Epitope Binning 可以分析 5,000 多个序列,无需物理材料,与传统的湿式实验室分箱结果相匹配
Accesswire ·  04/22 09:00

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 LENSai TM Platform. LENSai, 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.

不列颠哥伦比亚省维多利亚/ACCESSWIRE/2024年4月22日/人工智能驱动的生物治疗研究和技术公司ImmunoPrecise Antibodiese Antibodies Ltd.(纳斯达克股票代码:IPA)最近宣布扩建其已经成功的镜头ai TM 平台。镜头ai,由该公司的子公司BioStrand经营,通过链接来自整个生物圈的序列、结构、功能和文献信息,为生命科学数据提供独特而全面的视图。该平台现在正在将表位分箱集成到其公式中。

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.

表位分组是一种用于比较和分类一系列旨在靶向特定蛋白质的单克隆抗体的方法。在此过程中,每种抗体都要对所有其他抗体进行测试,以查看它们是否会干扰对方与靶蛋白结合的能力。通过这样做,科学家可以确定哪些抗体在靶蛋白上具有相似或相关的结合位点。具有相似结合位点的抗体根据它们之间的相互作用组合在一起或 “合并”。

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.

表位合并的主要目标是对具有相似靶结合特性的抗体进行分组,这有助于研究人员了解不同抗体的特征和行为,以及它们在药物开发或疾病诊断等各种应用中靶向特定蛋白质的潜力。

To achieve accurate epitope binning, LENSai'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. LENSai's algorithm then looks at the atomic interactions between the antibody-antigen complexes to gain a better understanding of their binding specificity.

为了实现精确的表位分组,LENSai的算法包含多个组件。它分析抗体的序列和结构特征,这意味着它会检查抗体的特定序列和三维结构,以了解其结合能力。它还考虑了对接信息,其中考虑了可能影响抗体-抗原相互作用的空间阻碍和糖基化位点等因素。镜头ai然后,的算法研究抗体-抗原复合物之间的原子相互作用,以更好地了解其结合特异性。

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

在最近发表的案例研究中,LENSai 将其表位合并算法应用于一组针对跨膜蛋白的29种抗体序列。从 LENS 获得的结果ai在计算机模拟中 然后将聚类分析与传统湿式实验室分箱程序的数据进行了比较。

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

结果显示,LENS之间高度一致ai在计算机模拟中 Epitope Binning 和经典的湿实验室分箱。换句话说,镜头ai的算法可以用与传统实验方法类似的方式对表位进行准确的分类和识别。这些发现表明,LENSai Epitope Binning可以有效地匹配体外竞争分析的结果,为研究人员提供对抗体-抗原相互作用的高信度预测。

This case study highlights the potential of LENSai's algorithm in addressing the challenges presented by the increasing number of antibodies generated in discovery campaigns. By offering both high accuracy and scalability, LENSai'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.

本案例研究凸显了 LENS 的潜力ai该算法旨在应对发现活动中产生的抗体数量不断增加所带来的挑战。通过提供高精度和可扩展性,LENSai在计算机模拟中 分组方法可以支持抗体发现的早期阶段,使研究人员能够高效地分析大量不同的抗体,并选择最有前途的候选药物进行进一步研究。

In silico epitope binning powered by LENSai 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.

在计算机中 由 LENS 提供支持的表位分组ai 因此,技术提供了关键的进步,它能够分析超过5,000个序列,为早期分类提供快速见解。其算法增强了生物学研究,提供准确、高通量的候选物选择,同时减少了时间和成本。对于抗体少于 5,000 的小亚群,它可以在短短数小时内得出结果。此外,它只需要蛋白质序列而不需要物理材料,从而进一步减少了所涉及的工作量。

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.

该平台进一步巩固了BioStrand在人工智能驱动的生物治疗研究和技术前沿的地位。预计到2030年,医疗保健领域的人工智能市场将达到1879.5亿美元。ImmunoPrecise Antibodies及其子公司似乎完全有能力在抗体领域引领人工智能和医疗保健行业。

Featured photo by National Cancer Institute on Unsplash.

美国国家癌症研究所在 Unsplash 上的精选照片。

Contact:
investors@ipatherapeutics.com

联系人:
investors@ipatherapeutics.com

SOURCE: ImmunoPrecise Antibodies Ltd.

来源:ImmunoPrecise 抗体有限公司


声明:本内容仅用作提供资讯及教育之目的,不构成对任何特定投资或投资策略的推荐或认可。 更多信息
    抢沙发