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RESP2:对抗体发现的不确定性意识多目标多属性优化人工智能管道

Jonathan Parkinson^1,2, Ryan Hard¹, Young Su Ko¹

¹Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, CA, 92093-0359, USA.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)

|September 5, 2025

概括

我们开发了RESP2,一个用于发现治疗抗体的新计算管道. 它能够有效地发现与多个向变体强度相结合的抗体,其性能优于当前的AI方法.

科学领域:

计算生物学
免疫学
蛋白质工程

背景情况:

由于病原体的突变,治疗传染病的抗体发现面临挑战.
抗体需要对多个抗原变体具有很高的亲和力和最佳的发育性质.

研究的目的:

推出RESP2,一个用于发现治疗抗体的增强计算管道.
为了同时优化抗体结合亲和力和对不断变化的标的开发能力.

主要方法:

在RESP2管道使用Absolut! 抗体-抗原对接数据库.
使用COVID-19尖端蛋白受体结合域 (RBD) 和其变体的案例研究.

主要成果:

与训练数据相比,RESP2发现的抗体序列与向抗原组的结合亲和度显著高 (≥85%的成功率).
RESP2的表现优于流行的生成人工智能技术,成功率为≤1.5%.
发现了一种新型人类抗体,与至少8种COVID-19 RBD变种结合.

结论:

RESP2管道为发现针对快速演变的传染病目标的抗体提供了强大的优势.
RESP2促进了广泛的保护性治疗抗体的产生.
为RESP2提供一个公开的Python包,用于更广泛的研究.

关键词:

人工智能药物发现发现抗体生物信息学药物耐药性不确定性意识的机器学习

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