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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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Hybridoma technology is used for the large-scale production of monoclonal antibodies. Monoclonal antibodies bind to only a single antigenic determinant or epitope. Such antibodies are used in research, diagnostics, and disease therapy. The hybridoma technology established in 1975 by Georges Köhler and Cesar Milstein was awarded the Nobel Prize in Medicine in 1984 for revolutionizing research and therapy.
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Antibodies, also known as immunoglobulins (Ig), are essential players of the adaptive immune system. These antigen-binding proteins are produced by B cells and make up 20 percent of the total blood plasma by weight. In mammals, antibodies fall into five different classes, which each elicits a different biological response upon antigen binding.
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Purification and Analytics of a Monoclonal Antibody from Chinese Hamster Ovary Cells Using an Automated Microbioreactor System
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ALLM-Ab:使用微调蛋白语言模型进行主动学习驱动的抗体优化.

Kairi Furui1, Masahito Ohue1

  • 1Department of Computer Science, School of Computing, Institute of Science Tokyo, Yokohama 226-8501, Japan.

Journal of chemical information and modeling
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此摘要是机器生成的。

使用蛋白质语言模型的积极学习框架ALLM-Ab加速了抗体序列优化. 它平衡了结合亲和力和可开发性,在发现高亲和力变体方面超过了其他方法.

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科学领域:

  • 生物技术是生物技术.
  • 计算生物学 计算生物学
  • 免疫学 免疫学 免疫学

背景情况:

  • 抗体工程面临的挑战是优化结合亲和力,同时保持可开发性.
  • 蛋白质语言模型为预测抗体序列适应性提供了潜力.

研究的目的:

  • 介绍ALLM-Ab,这是一个用于加速抗体序列优化的主动学习框架.
  • 为了利用精细调整的蛋白质语言模型来有效地生成候选序列.
  • 将抗体开发能力指标整合到优化过程中.

主要方法:

  • 利用了蛋白质语言模型的参数有效微调 (低级调整).
  • 用一种学习到等级的策略来评估突变体适应性.
  • 整合了一个多目标优化方案,包括可开发性指标.
  • 使用深度突变扫描数据和在线积极学习试验进行验证.

主要成果:

  • ALLM-Ab准确地评估突变体适应性,并有效地生成候选序列.
  • 该框架成功地平衡了改善的结合亲和力与治疗性抗体样性质.
  • 与基线方法相比,证明了高亲和度抗体变体的加快发现.
  • 在优化过程中保留了关键的抗体开发能力指标.

结论:

  • ALLM-Ab提供了一种高效可靠的抗体设计策略.
  • 该框架有可能显著降低治疗开发成本.
  • 这种方法推进了抗体工程和药物发现领域.