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
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.
不列顛哥倫比亞省維多利亞/ACCESSWIRE/2024年4月22日/人工智能驅動的生物治療研究和技術公司ImmunoPrecise Antibodiese Antibodies Ltd.(納斯達克股票代碼:IPA)最近宣佈擴建其已經成功的鏡頭ai TM 平台。鏡頭ai,由該公司的子公司BioStrand經營,通過鏈接來自整個生物圈的序列、結構、功能和文獻信息,爲生命科學數據提供獨特而全面的視圖。該平台現在正在將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.
表位分組是一種用於比較和分類一系列旨在靶向特定蛋白質的單克隆抗體的方法。在此過程中,每種抗體都要對所有其他抗體進行測試,以查看它們是否會干擾對方與靶蛋白結合的能力。通過這樣做,科學家可以確定哪些抗體在靶蛋白上具有相似或相關的結合位點。具有相似結合位點的抗體根據它們之間的相互作用組合在一起或 “合併”。
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.
表位合併的主要目標是對具有相似靶結合特性的抗體進行分組,這有助於研究人員了解不同抗體的特徵和行爲,以及它們在藥物開發或疾病診斷等各種應用中靶向特定蛋白質的潛力。
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.
爲了實現精確的表位分組,LENSai的算法包含多個組件。它分析抗體的序列和結構特徵,這意味着它會檢查抗體的特定序列和三維結構,以了解其結合能力。它還考慮了對接信息,其中考慮了可能影響抗體-抗原相互作用的空間阻礙和糖基化位點等因素。鏡頭ai然後,的算法研究抗體-抗原複合物之間的原子相互作用,以更好地了解其結合特異性。
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.
在最近發表的案例研究中,LENSai 將其表位合併算法應用於一組針對跨膜蛋白的29種抗體序列。從 LENS 獲得的結果ai的 在計算機模擬中 然後將聚類分析與傳統溼式實驗室分箱程序的數據進行了比較。
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.
結果顯示,LENS之間高度一致ai的 在計算機模擬中 Epitope Binning 和經典的溼實驗室分箱。換句話說,鏡頭ai的算法可以用與傳統實驗方法類似的方式對錶位進行準確的分類和識別。這些發現表明,LENSai Epitope Binning可以有效地匹配體外競爭分析的結果,爲研究人員提供對抗體-抗原相互作用的高信度預測。
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.
本案例研究凸顯了 LENS 的潛力ai該算法旨在應對發現活動中產生的抗體數量不斷增加所帶來的挑戰。通過提供高精度和可擴展性,LENSai的 在計算機模擬中 分組方法可以支持抗體發現的早期階段,使研究人員能夠高效地分析大量不同的抗體,並選擇最有前途的候選藥物進行進一步研究。
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.
在計算機中 由 LENS 提供支持的表位分組ai 因此,技術提供了關鍵的進步,它能夠分析超過5,000個序列,爲早期分類提供快速見解。其算法增強了生物學研究,提供準確、高通量的候選物選擇,同時減少了時間和成本。對於抗體少於 5,000 的小亞群,它可以在短短數小時內得出結果。此外,它只需要蛋白質序列而不需要物理材料,從而進一步減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.
該平台進一步鞏固了BioStrand在人工智能驅動的生物治療研究和技術前沿的地位。預計到2030年,醫療保健領域的人工智能市場將達到1879.5億美元。ImmunoPrecise Antibodies及其子公司似乎完全有能力在抗體領域引領人工智能和醫療保健行業。
Featured photo by National Cancer Institute on Unsplash.
美國國家癌症研究所在 Unsplash 上的精選照片。
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SOURCE: ImmunoPrecise Antibodies Ltd.
來源:ImmunoPrecise 抗體有限公司