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使用序列,结构和机器学习相似性测量用于抗体发现应用的基准抗体聚类方法

Dawid Chomicz¹, Jarosław Kończak¹, Sonia Wróbel¹

¹NaturalAntibody, Szczecin, West Pomeranian, Poland.

Frontiers in molecular biosciences

|April 12, 2024

概括

研究人员对抗体分组方法进行了基准比较,用于生物治疗发现. 结合多种方法,包括克隆型,帕拉托普和嵌入,比单个方法单独产生更多样化的抗体候选池.

科学领域:

生物化学生物化学
免疫学免疫学免疫学
生物信息学是一种生物信息学.

背景情况:

抗体是来自免疫系统蛋白质的关键生物治疗药物.
发现治疗性抗体需要从菌体显示等方法分析大量序列数据.
目前的方法通常仅依赖于序列相似性来分组抗体候选者,这可能会限制多样性.

研究的目的:

为了比较不同的抗体分组方法,超越序列相似性.
评估结合物检测和表位图绘制任务上的分组性能.
评估结合直角分组策略的多样性收益.

主要方法:

使用克隆型,序列,帕拉托普预测,结构预测和嵌入进行基准抗体分组.
评估结合物检测和表位图绘制的方法.
开发一个在线工具 (CLAP) 用于可视化和对比抗体分组.

主要成果:

没有一个单一的方法在粘合剂检测方面显著超过其他方法.
克隆型,帕拉托普和基于嵌入的分组是表位图绘制的最佳表现.
所有测试的方法都提供了直角分组,在组合使用时增强了候选池的多样性.

结论:

结合多种抗体分组方法可以增加候选人池的多样性.
该CLAP工具有助于在不同的分组策略中探索和可视化抗体多样性.
这种方法有助于选择更多样化的抗体候选组用于治疗开发.

关键词:

抗体是对抗体的重要组成部分.生物药物和生物类似药物聚类集群是指聚类的聚类.发现药物的发现.语言模型 (LMs) 是一种语言模型.机器学习是机器学习.

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Antibody Structure and Classes

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Antibodies, also known as immunoglobulins, are produced by B cells in response to foreign substances, such as bacteria and viruses. These proteins are critical for recognizing and neutralizing these substances, protecting the body from potential harm.
The basic structure of an antibody consists of four protein chains: two identical heavy chains and two identical light chains. These chains are held together by disulfide bonds and other non-covalent interactions, forming a Y-shaped structure.