使用Caliby集成条件蛋白质序列设计
Richard W Shuai1, Tianyu Lu2, Subhang Bhatti3
1Biophysics Program, Stanford University.
bioRxiv : the preprint server for biology
|November 19, 2025
概括
一种新的蛋白质设计方法Caliby通过对结构合集的调节来克服深度学习模型的局限性. 这种方法提高了复杂蛋白质结构的可设计性,扩大了蛋白质工程的范围.
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.
- 生物信息学是一种生物信息学.
背景情况:
- 结构条件序列设计旨在创建具有特定折叠的蛋白质.
- 当前的深度学习模型因培训目标的偏见而与非理想化的骨干作斗争.
- 原生序列恢复可以引入非结构信号,限制概括性.
研究的目的:
- 介绍Caliby,一种基于Potts模型的新型序列设计方法.
- 为解决当前蛋白质设计模型对非理想化脊柱的局限性.
- 为了提高蛋白质设计的准确性和范围.
主要方法:
- 卡利比条件对由目标骨干生成的一组结构的序列设计.
- 合奏调节平均取消对原生序列的偏见.
- 一种Caliby的变体被训练在可溶性蛋白质上,用于特定的粘合剂设计任务.
主要成果:
- 卡利比减少了原生序列恢复,同时提高了AlphaFold2的自我一致性.
- 在原生和de novo骨架上表现优于最先进的模型 (ProteinMPNN,ChromaDesign).
- 识别了以前无法指定的蛋白质结合剂,可以使用可溶性蛋白质训练变体来指定.
结论:
- 卡利比扩大了新的设计空间,超越了理想化的骨干.
- 集成条件设计有效地减轻了蛋白质序列生成中的偏差.
- 突出了蛋白质设计现有过管道的局限性.
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