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EvoNB:一种基于蛋白质语言模型的工作流程,用于纳米体突变预测和优化

Danyang Xiong¹, Yongfan Ming², Yuting Li¹

¹Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, Shanghai Frontiers Science Center of Molecule Intelligent Syntheses, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200062, China.

Journal of pharmaceutical analysis

|July 18, 2025

概括

我们开发了进化纳米体 (EvoNB) 工作流程,以优化纳米体突变以提高治疗潜力. EvoNB结合了蛋白质语言模型和分子动力学模拟,以准确预测和验证突变,改善纳米体-抗原结合亲和力.

科学领域:

生物技术是生物技术.
计算生物学计算生物学
蛋白质工程是指蛋白质工程.

背景情况:

优化纳米体的治疗用途至关重要,但往往复杂和耗时.
目前用于识别有益的纳米体突变的方法可能是低效的,限制了它们的实际应用.

研究的目的:

开发一个高效的工作流来预测和优化纳米体突变.
通过提高它们对向抗原的结合亲和力来增强纳米体的治疗潜力.

主要方法:

开发了进化纳米体 (EvoNB) 工作流程,集成蛋白质语言模型 (PLM) 和分子动力学 (MD) 模拟.
在大型纳米体数据集上微调ESM2蛋白语言模型,以改进序列特征捕获.
使用MD模拟对代表性纳米体-抗原复合体进行验证预测突变,以评估结合亲和力变化.

主要成果:

EvoNB 工作流显示了对纳米体突变的增强预测准确性,特别是在保留和可变区域.
MD模拟证实,EvoNB识别的突变显著改善了纳米体-抗原结合亲和力.
基于序列的预测方法不太依赖结构数据,并且可以很好地与AlphaFold 3等工具集成.

结论:

EvoNB 工作流提供了一个有效的工具,用于快速设计和优化纳米体突变.
这种方法加速了对有希望的纳米体变体进行实验验证的识别,绕过了传统的进化方法.
EvoNB促进了PLM在纳米体工程和生物医学领域的更广泛应用.

关键词:

阿尔法折叠3 3 在ESM2模式下,进化型纳米人体 (EvoNB) 是一个模拟MDMD的模拟一个纳米人体.蛋白质语言模型 (PLM) 是一种蛋白质语言模型.

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