p-IgGen:一种配对抗体生成语言模型
Oliver M Turnbull1, Dino Oglic2, Rebecca Croasdale-Wood3
1Department of Statistics, University of Oxford, Oxford, OX1 3LB, United Kingdom.
Bioinformatics (Oxford, England)
|November 9, 2024
概括
我们开发了p-IgGen,一种蛋白质语言模型,用于生成新的抗体序列. 这种工具有助于克服抗体药物发现的挑战,通过设计具有改进的可开发性和生物物理性质的序列.
科学领域:
- 生物化学 生物化学
- 计算生物学 计算生物学
- 药物发现 药物发现 药物发现
背景情况:
- 抗体药物发现在设计具有最佳开发能力的序列方面面临挑战.
- 诸如聚合,多特异性,表达不良和溶解性低等问题阻碍了治疗性抗体的开发.
研究的目的:
- 介绍p-IgGen,一种用于配对重光链抗体生成的新型蛋白质语言模型.
- 开发一种微调的p-IgGen版本,用于产生具有理想3D生物物理性质的抗体.
主要方法:
- 利用蛋白质语言模型进行抗体序列生成.
- 在抗体序列数据上训练和微调p-IgGen模型.
- 对抗体样性质和生物物理特征进行评估的生成序列.
主要成果:
- p-IgGen产生多样化,类似抗体的序列,具有自然配对特性.
- 这种微调的模型倾向于产生具有临床阶段生物物理分布的抗体.
- 该模型解决了抗体设计中的关键可开发性挑战.
结论:
- p-IgGen为设计具有增强开发能力的新型抗体序列提供了强大的工具.
- 该模型有助于产生具有改善生物物理性质的治疗抗体.
- 这种方法有助于克服抗体药物发现中的关键障碍.
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