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Updated: Feb 10, 2026

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Constructing Cyclic Peptides Using an On-Tether Sulfonium Center
Published on: September 28, 2022
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循环MPNN:稳定的循环序列生成
bioRxiv : the preprint server for biology
|February 9, 2026
概括
我们开发了CyclicMPNN,这是一个新的AI模型,可以设计稳定的循环序列用于治疗. 这种方法提高了开发新循环类药物的成功率.
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:
- 循环提供治疗优势,如稳定性和细胞透性.
- 像CyclicCAE和RFPeptide这样的先进模型加速了循环骨干的生成.
- 设计稳定的循环序列用于特定的脊柱仍然具有挑战性.
研究的目的:
- 开发一种人工智能驱动的方法来设计能量有利的循环序列.
- 为了提高治疗循环开发的成功率.
主要方法:
- 微调 ProteinMPNN 深度学习模型.
- 使用来自蛋白质数据库的X射线晶体结构.
- 在培训中将基生成的循环片纳入培训.
主要成果:
- 循环MPNN在循环序列设计中表现优于蛋白MPNN.
- 生成的序列表现出增强的能量稳定性和折叠成功率.
- 该模型有效地用于图案绘制和 de novo 序列设计.
结论:
- 循环MPNN促进了稳定的循环序列的快速设计.
- 预计这一进步将增加治疗循环开发的成功率.
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