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SAGE-prot:用于多目标蛋白质设计的得分辅助生成探索

Hocheol Lim¹, Geon-Ho Lee¹, Hyein Cho¹

¹Bioinformatics and Molecular Design Research Center (BMDRC), 85 Songdogwahak-ro, Yeonsu-gu, Incheon, 21983, Republic of Korea.

Briefings in bioinformatics

|November 9, 2025

概括

我们开发了SAGE-Prot,这是一种新的蛋白质设计框架,使用人工智能创建具有改进功能的蛋白质. 这种方法加速了用于生物技术应用的高性能蛋白质变体的发现.

科学领域:

生物技术是生物技术.
蛋白质工程是指蛋白质工程.
计算生物学计算生物学

背景情况:

设计具有多个优化性质的蛋白质具有挑战性,尤其是在冲突的目标或缺乏结构模板的情况下.
现有的方法往往依赖于结构信息,或缺乏有效的优化策略,用于复杂的设计任务.

研究的目的:

介绍SAGE-Prot,一个直接优化蛋白序列的框架,集成生成模型和遗传算法.
为了证明SAGE-Prot在改善蛋白质特性 (如结合亲和力,稳定性,酶活性和可溶性) 的有效性.

主要方法:

在封闭循环系统中,SAGE-Prot采用自回归序列生成,基于遗传算法 (GA) 的多样化和以评分为导向的属性评估.
它直接在没有模板的情况下优化蛋白质序列,使结构意识评估成为可能.
使用课程学习 (CL) 策略来加快融合并提高设计质量.

主要成果:

使用混合语言模型/GA策略的SAGE-Prot在10种治疗蛋白质的重新发现和相似性基准中表现优于基线方法.
该框架成功地确定了蛋白质G域B1和TEM-1β-乳酸酶的高性能变体.
实验验证显示,对于设计的TEM-1β-乳酸酶变体,催化活性增加了752倍.

结论:

SAGE-Prot提供了一种可泛化,数据驱动的方法,用于在各种优化场景中进行蛋白质工程.
将深度生成建模与结构知情评估和代微调相结合,可以有效地发现功能性蛋白质变体.
这一框架对于推动生物技术和治疗性蛋白质设计具有重大实用性.

关键词:

计算型蛋白质设计生成式建模生成式建模代微调是可以进行的.定量结构财产关系

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