预训练的蛋白质语言模型在序列新性和结构完整性之间进行选择
Arjuna M Subramanian1, Zachary A Martinez1, Matt Thomson1
1Division of Biology and Biological Engineering, California Institute of Technology, CA, USA.
bioRxiv : the preprint server for biology
|November 19, 2025
概括
蛋白质语言模型 (PLM) 可以产生新的序列,但与结构多样性作斗争,经常陷入"螺旋捆陷". 需要新的策略来引导PLM产生多样化,生物物理上有效的蛋白质设计.
科学领域:
- 计算生物学是一种计算生物学.
- 蛋白质工程是一种蛋白质工程.
- 生物化学中的人工智能
背景情况:
- 蛋白质语言模型 (PLM) 越来越多地用于蛋白质设计和变异效应预测.
- 了解生物物理规则和自然蛋白质空间的多样性对于推进PLM能力至关重要.
研究的目的:
- 以计算方式描述和描述PLM生成的小蛋白质的序列和结构统计.
- 评估PLM输出对自然蛋白质多样性和生物物理规则的总结程度.
主要方法:
- 免费,不受约束的几十万个小蛋白质的生成使用建筑上不同的PLM.
- 序列和结构统计和属性的计算分析.
主要成果:
- PLM显示出产生新型氨基酸序列的高能力.
- 与天然蛋白质相比,PLM生成的蛋白质具有有限的结构变异.
- 在序列新性和结构宽度之间存在权衡,在自然序列空间之外倾向于"螺旋捆陷".
结论:
- PLM可以产生新的序列,但难以复制自然蛋白质的结构多样性.
- 当前的PLM设计策略可能受到特定结构动机的趋势的限制.
- 开发新方法对于引导PLM产生更广泛的生物物理有效蛋白质结构和功能至关重要.
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