结构信息化蛋白质语言模型是对变异效应的可靠预测器
Yuanfei Sun1, Yang Shen1,2,3
1Department of Electrical and Computer Engineering, Texas A&M University, College Station, 77843, Texas, USA.
Research square
|August 14, 2023
概括
结构信息化的蛋白质语言模型 (SI-pLMs) 通过结合结构上下文来改善变异效应预测. 这种方法提高了不需要实验标签的准确性,克服了仅序列模型的局限性.
科学领域:
- 计算生物学 计算生物学
- 机器学习 机器学习
- 蛋白质工程是指蛋白质工程.
背景情况:
- 预测蛋白质变异效应至关重要,但由于稀缺的实验数据而受到限制.
- 现有的蛋白质语言模型 (pLMs) 预测了无标签的变异效应,但将生物背景边缘化.
- 在pLM中的序列和结构意识影响预测的准确性,在过度微调中观察到的权衡.
研究的目的:
- 开发一个框架,将蛋白质结构上下文纳入pLMs.
- 为了提高蛋白质变异效应的预测准确度.
- 创建适用于现有的仅序列模型的结构信息化的pLM (SI-pLM).
主要方法:
- 引入了一个结构信息化的pLMs (SI-pLMs) 框架,通过将掩盖序列denoising扩展到跨模式denoising.
- SI-pLMs使用模型架构和培训目标修改仅序列的pLMs,在培训期间使用结构作为上下文和调整器.
- 在深度突变发生性扫描基准上评估SI-pLMs.
主要成果:
- SI-pLM与竞争方法 (包括其他pLM) 相比,表现强.
- 在不同进化信息含量和过拟合倾向的蛋白质家族中,性能是一致的.
- 在结构上下文中学习的分布增强了序列分布,以改善变异效应预测.
结论:
- 结构知情的plm有效地注入和利用蛋白质结构上下文,以增强变体效应预测.
- SI-pLMs提供了一种强大而通用的方法,克服了仅序列模型的局限性.
- 结构信息的整合提高了蛋白质语言模型的预测能力.
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