S-PLM:通过序列和结构之间的对比学习实现结构意识的蛋白质语言模型
bioRxiv : the preprint server for biology
|August 23, 2023
概括
我们开发了S-PLM,这是一种新的3D结构感知蛋白质语言模型 (PLM),它集成了蛋白质序列和结构数据. 这种方法提高了蛋白质功能预测和设计能力,超出了传统的仅序列模型.
科学领域:
- 计算生物学 计算生物学
- 结构生物信息学 结构生物信息学
- 机器学习在生物学中的应用
背景情况:
- 大型蛋白质语言模型 (PLM) 正在彻底改变蛋白质研究,有助于功能确定和蛋白质设计.
- 目前的PLM主要依赖于蛋白质序列,经常忽视关键的3D结构信息.
- 这种遗漏限制了PLM在结构依赖的生物和工程应用中的预测能力.
研究的目的:
- 介绍S-PLM,一个3D结构感知蛋白质语言模型.
- 通过整合序列和3D结构数据来增强蛋白质的表示学习.
- 通过利用全面的结构见解来改善蛋白质功能预测和设计.
主要方法:
- 开发了S-PLM,使用多视图对比学习来对准蛋白质序列和3D结构表示.
- 在AlphaFold预测结构上使用Swin-Transformer嵌入结构信息.
- 融合了ESM2的基于序列的嵌入式结构嵌入式,并提供了适应的轻量级调整工具.
主要成果:
- 与仅序列的PLM相比,S-PLM在蛋白质聚类和分类任务中表现出更高的性能.
- 该模型通过使用使用序列和结构输入的最先进方法实现了具有竞争力的性能.
- 轻量级的调工具有助于S-PLM适应各种蛋白质性质预测任务.
结论:
- 将3D结构信息集成到蛋白质语言模型中,大大提高了它们的预测能力.
- S-PLM提供了一种强大而可适应的框架,用于推进蛋白质研究和工程.
- 开发的S-PLM及其相关工具是研究界公开使用的.
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