FvFold:一个模型来预测抗体Fv结构使用蛋白质语言模型与残余网络和罗塞塔最小化
Pasang Sherpa1, Kil To Chong2, Hilal Tayara3
1Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju, 54896, South Korea.
Computers in biology and medicine
|September 13, 2024
概括
新的深度学习模型FvFold准确地预测了抗体的可变碎片 (Fv) 结构. 这一进步改善了抗体建模,对于开发新疗法至关重要,特别是用于癌症治疗.
科学领域:
- 免疫学 免疫学 免疫学
- 结构生物学 结构生物学
- 生物信息学是一种生物信息学.
背景情况:
- 抗体 (Abs) 对于免疫反应至关重要,识别各种抗原.
- 准确预测抗体可变片段 (Fv) 结构对于治疗开发至关重要,特别是在瘤学中.
- 现有的深度学习模型在精确建模抗体互补性确定区域 (CDR) 和整体Fv结构方面面临挑战.
研究的目的:
- 介绍FvFold,一个新的深度学习模型,用于准确的抗体Fv结构预测.
- 解决当前模型在预测CDR和Fv结构方面的局限性.
- 促进基于抗体的治疗方法的开发.
主要方法:
- 开发了FvFold,这是一个深度学习模型,利用ProtT5-XL-UniRef50蛋白质语言模型.
- 评估了FvFold在多个基准上的表现,包括RosettaAntibody,治疗和IgFold.
- 与现有的最先进的抗体结构预测模型进行FvFold比较.
主要成果:
- 与之前的模型相比,FvFold在预测抗体Fv结构方面表现出更高的准确性.
- 在大多数循环中实现了较低的根平均平方偏差 (RMSD) 值.
- 在RosettaAntibody,Therapeutic和IgFold基准测试中显示出更好的方向坐标距离 (OCD) 值.
结论:
- FvFold代表了对抗体可变片段结构预测的重大进步.
- 该模型在预测CDR和整体Fv结构方面的高准确性有助于抗体工程和药物发现.
- FvFold对加速开发针对癌症等各种疾病的基于抗体的新型疗法有很大的前景.
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