通过面具语言建模对配相互作用的蛋白序列
Umberto Lupo1,2, Damiano Sgarbossa1,2, Anne-Florence Bitbol1,2
1Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne CH-1015, Switzerland.
概括
我们使用基于对齐的语言模型 (DiffPALM) 开发了差异配对,以预测相互作用的蛋白质序列. DiffPALM利用蛋白质语言模型,优于现有的方法,改善了蛋白质复杂结构预测.
科学领域:
- 计算生物学是一种计算生物学.
- 蛋白质的生物信息学
- 结构生物学中的机器学习
背景情况:
- 从氨基酸序列预测蛋白质-蛋白质相互作用对于理解生物功能至关重要.
- 现有的方法经常与浅的多重序列对齐 (MSAs) 斗争.
- 相互作用蛋白序列的准确配对对于预测蛋白质复杂结构至关重要.
研究的目的:
- 开发一种使用蛋白质语言模型预测相互作用蛋白质序列的新方法.
- 以一种可差分的方式制定蛋白质配对问题.
- 为了提高蛋白质复杂结构预测的准确性.
主要方法:
- 开发了使用基于对齐的语言模型 (DiffPALM) 的可差配对.
- 利用MSA转换器,一种在MSA上训练的蛋白质语言模型,来预测交互伙伴.
- 利用了MSA变压器在链间相互作用中推断同进化信号的能力.
- 以可分化的方式制定了来自两个蛋白质家族的对应物对的配对.
主要成果:
- 在具有浅层MSA的具有挑战性的基准指标上,DiffPALM的表现优于现有的基于共同进化的配对方法.
- DiffPALM超越了使用单个序列训练蛋白质语言模型的方法.
- 使用DiffPALM配对序列显著提高了使用AlphaFold-Multimer的真核蛋白质复合体的结构预测.
- 与基于orthology的配对相比,获得了竞争性表现.
结论:
- DiffPALM提供了一种强大的新方法来预测相互作用蛋白序列,特别是在有限的MSA数据上.
- 该方法证明了基于MSA的语言模型在捕捉链际共进化的实用性.
- DiffPALM生成的序列对是基于深度学习的蛋白质结构预测工具的宝贵输入.
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