PLMFit:比较转移学习与蛋白质语言模型用于蛋白质工程.
Thomas Bikias1,2, Evangelos Stamkopoulos1,2, Sai T Reddy1,2
1Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.
Briefings in bioinformatics
|July 30, 2025
概括
蛋白质语言模型 (PLM) 与转移学习 (TL) 结合,提供了强大的蛋白质工程工具. 我们的研究对不同的TL方法进行了基准测试,发现微调 (FT) 对于有限的数据和概括需求是最好的.
科学领域:
- 计算生物学是一种计算生物学.
- 蛋白质工程是一种蛋白质工程.
- 在生物信息学中的机器学习.
背景情况:
- 蛋白质语言模型 (PLM) 对蛋白质工程非常有价值.
- 转移学习 (TL) 通过特征提取或微调 (FT) 来提高PLM性能.
- 缺乏比较分析阻碍了PLM的最佳TL策略选择.
研究的目的:
- 对应用到最先进的蛋白质语言模型 (PLM) 的转移学习 (TL) 方法进行基准测试.
- 在蛋白质工程任务中确定知识转移的最佳策略.
- 为特定蛋白质工程应用确定最合适的TL方法.
主要方法:
- 结合了三个最先进的PLM (ESM2,ProGen2,ProteinBert) 和三个TL方法 (特征提取,低级适应,瓶适应器).
- 进行了>3150个in silico实验,不同的PLM大小,层次,TL超参数和培训程序.
- 在五个不同的蛋白质工程数据集中评估了性能.
主要成果:
- 使用TL的部分PLM不会显著影响业绩.
- 特性提取 (FE) 和微调 (FT) 之间的选择取决于数据量和多样性.
- 用有限的数据进行概括时,FT最有效.
结论:
- PLMFit为PLM中的TL提供了一个全面的基准.
- 该研究为选择蛋白质工程的最佳FE或FT策略提供了指导.
- PLMFit是一个开源资源,旨在促进PLM在科学界的应用.
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