基于蛋白质语言模型和结构模型的双目标蛋白质序列设计方法
Liu Cheng1, Ting Wei1, Xiaochen Cui2
1Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
Briefings in bioinformatics
|August 26, 2025
概括
设计蛋白质以结合两个点对于新疗法至关重要. 一种新的人工智能方法,即蛋白质双目标设计网络 (ProDualNet),成功地设计了双目标蛋白质,性能优于现有的方法.
科学领域:
- 计算生物学
- 蛋白质工程
- 生物信息学
背景情况:
- 与多个合作伙伴相互作用的蛋白质是生物调节的关键.
- 多目标类药物具有前景,需要多目标蛋白质设计策略.
- 目前的蛋白质设计方法主要集中在单受体相互作用上,限制了双目标设计能力.
研究的目的:
- 介绍ProDualNet,一种基于结构的新计算方法,用于设计与两个不同的受体结合的蛋白序列.
- 解决对双目标蛋白质结构的有限实验数据的挑战.
主要方法:
- ProDualNet使用异质图形网络进行预训练.
- 该模型使用噪声增强单目标数据和真实双目标数据的组合进行了微调.
- 它整合了两个目标受体的序列和结构信息.
主要成果:
- 与现有的多状态设计方法相比,ProDualNet在各种测试集中表现出优异的恢复和成功率.
- 在基评估中显示了设计双蛋白的潜力,包括具有全结合和非重叠接口的情况.
- 该方法有效地克服了稀缺的双蛋白结构数据所带来的局限性.
结论:
- ProDualNet代表了多目标应用的计算蛋白质设计的重大进步.
- 开发的方法有望加速新型治疗蛋白质和生物制剂的设计.
- 代码和数据的可用性促进了蛋白质工程的进一步研究和应用.
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