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Antibody Structure

Antibody Structure

Overview
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.
The Y-Shaped Structure of Antibodies Consists of Four Polypeptide Chains
Antibodies consist of four polypeptide chains: two identical heavy...

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Updated: Jul 12, 2025

Identification of Mouse and Human Antibody Repertoires by Next-Generation Sequencing

Identification of Mouse and Human Antibody Repertoires by Next-Generation Sequencing

Published on: March 15, 2019

IgLM:用于抗体序列设计的充填语言建模.

Richard W Shuai¹, Jeffrey A Ruffolo², Jeffrey J Gray³

¹Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, USA.

|November 1, 2023

概括

本研究介绍了免疫球蛋白语言模型 (IgLM),这是一种用于设计合成抗体库的新人工智能工具. 通过使用双向语境来生成序列,IgLM提高了抗体开发能力.

关键词:

抗体是对抗体的重要组成部分.深度学习是一种深度学习.语言建模语言建模

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Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins

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科学领域:

生物技术是生物技术.
人工智能的人工智能
免疫学免疫学免疫学

背景情况:

治疗性抗体的发现需要大量的序列库,但面临着抗体开发能力的挑战,包括低溶解性,聚合性和免疫性.
生成式语言模型提供了一种强大的方法,可以在需求时创建多样化和现实的蛋白质序列.

研究的目的:

引入免疫球蛋白语言模型 (IgLM),这是一个用于设计合成抗体库的深度生成语言模型.
为了利用双向上下文和文本填充方法来生成和重新设计抗体序列.

主要方法:

在558万个抗体重链和轻链变量序列上训练了IgLM,根据链类型和物种进行调节.
采用文本填充配方来生成序列,从而实现双向上下文利用.
评估了该模型生成全长抗体序列的能力,并透了互补性确定区域 (CDR) 循环库.

主要成果:

在各种物种中,IgLM成功生成了全长抗体序列.
填充配方使得CDR循环库的生成成为可能,这些CDR循环库具有增强的in silico可开发性配置.
与单向上下文方法相比,演示了改进的序列生成能力.

结论:

免疫球蛋白语言模型 (IgLM) 代表了人工智能驱动的抗体设计的重大进步.
IgLM的双向上下文和充填方法增强了合成抗体库的生成,提高了开发能力.
这项技术有可能加速治疗抗体的发现和优化.