关注注意力:高注意力位作为蛋白质家族和语言模型中的功能指标
Gowri Nayar1, Alp Tartici2, Russ B Altman1,2,3,4
1Department of Biomedical Data Science, Stanford University, Stanford, California, United States of America.
PLoS computational biology
|September 12, 2025
概括
蛋白质语言模型 (PLM) 通过注意力模式识别关键残留物或高注意力 (HA) 区域. 这些HA位点与生物功能联系在一起,并改善未注释的蛋白质的预测.
科学领域:
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
- 蛋白质科学 蛋白质科学
背景情况:
- 蛋白质语言模型 (PLMs) 利用变压器架构来表示蛋白质序列.
- 在PLM中的注意力机制捕获了远程依赖性,并突出了残留物相互作用.
- 注意矩阵为发现蛋白质功能提供了潜力.
研究的目的:
- 引入一种使用进化规模建模 (ESM) 方法识别高度关注 (HA) 站点的新方法.
- 将已识别的HA位点与特定的生物功能和蛋白质家族联系起来.
- 为了增强功能注释和蛋白质的活性位点预测.
主要方法:
- 使用进化规模建模 (ESM) 框架.
- 分析PLM中层的注意力模式,以确定关键的残留物.
- 开发一种识别高度关注 (HA) 站点的方法.
主要成果:
- 鉴定出HA部位是关键的残留物,有助于蛋白质家族的分类.
- 在HA部位和生物功能之间展示了可解释的联系.
- 使用HA位点对未注释的蛋白质进行了改进的活性位点预测.
结论:
- 拟议的方法为蛋白质分类和功能注释提供了一个广泛适用的方法.
- HA 站点提供了PLM表示的生物解释性.
- HA位点提高了蛋白质的功能预测准确性.
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