使用生物启发的抗体语言模型准确预测抗体功能和结构
Hongtai Jing1,2,3, Zhengtao Gao1, Sheng Xu4
1Research Institute of Intelligent Complex Systems, Fudan University, Shanghai 200433, China.
Briefings in bioinformatics
|May 27, 2024
概括
我们开发了BALMFold,这是一种深度学习模型,可以从序列中准确预测抗体结构. 该工具通过克服现有的蛋白质结构预测方法的局限性,加速治疗抗体的工程和开发.
科学领域:
- * 计算生物学 计算生物学
- * * 结构生物学 结构生物学
- * 免疫学 免疫学
背景情况:
- *抗体是关键的治疗药物,但它们的发展受到稀缺的结构数据和复杂的工程技术的限制.
- *深度学习推进了蛋白质结构预测,但由于独特的进化和灵活的结合区域,抗体构造仍然具有挑战性.
研究的目的:
- * 引入一种新的深度学习模型,即生物灵感抗体语言模型 (BALM),用于抗体特异序列分析.
- * 介绍BALMFold,一个端到端的方法,用于从单个序列中快速,原子级抗体结构预测.
- * 增强治疗性抗体的发现和工程.
主要方法:
- *培训BALM在336万个未标记的抗体序列的大数据集上.
- * 开发BALMFold作为一个从BALM衍生的端到端结构预测管道.
- *评估BALMFold的性能与已建立的方法如AlphaFold2,IgFold,ESMFold和OmegaFold在抗体基准上的表现.
主要成果:
- *BALM在四个抗原结合预测任务中表现出高性能.
- *与现有的最先进的方法相比,BALMFold在预测完整的原子抗体结构方面取得了更高的准确性.
- *该模型成功地从单个序列快速预测了抗体结构.
结论:
- * BALMFold显著提升了抗体结构预测,超过了当前领先的工具.
- *这种方法有可能简化治疗抗体的开发并降低实验成本.
- * BALMFold结构预测服务器是公开可访问的,用于研究和开发.
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