蛋白质大语言模型的比较评估,用于酶委员会数量预测
João Capela1, Maria Zimmermann-Kogadeeva2, Aalt D J van Dijk3,4
1Centre of Biological Engineering, University of Minho, Braga, 4710-057, Portugal. joao.capela@ceb.uminho.pt.
BMC bioinformatics
|February 27, 2025
概括
大型语言模型 (LLM) 显示了酶功能的预测的前景,补充了BLASTp用于困难的情况下. 虽然还没有超过BLASTp,但LLM提供了宝贵的见解,特别是对于低序列身份的酶.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 酶工程是什么? 酶工程是什么?
背景情况:
- 蛋白质大语言模型 (LLM) 提取酶序列表示用于功能预测 (EC 数).
- 缺乏对LLM用于酶功能预测的全面比较.
- 作为单个预测指标,LLM和序列对齐方法 (例如BLASTp) 尚未被广泛比较.
研究的目的:
- 评估ESM2,ESM1b和ProtBERT语言模型在预测酶委员会 (EC) 数量的性能.
- 将LLM与BLASTp和基于一次热编码的模型进行比较.
- 评估LLM和序列对齐方法的互补潜力.
主要方法:
- 评估了ESM2,ESM1b和ProtBERT语言模型用于EC数字预测.
- 与BLASTp和使用一热氨基酸序列编码的模型进行LLM性能比较.
- 评估了与LLMs结合的深度学习模型与传统方法相比.
主要成果:
- 与完全连接的神经网络相结合的LLM优于单热编码模型.
- BLASTp的整体表现略有改善,但LLMs和BLASTp的结果是互补的.
- 在经过测试的LLM中,ESM2表现最好,在难以注释和没有同类酶的酶方面表现出色.
结论:
- 在常规酶注释中,LLMs需要进一步改进以取代BLASTp.
- 对于具有挑战性的酶注释,LLM提供了有价值的预测,特别是在<25%的序列相同性.
- 结合BLASTp和LLM模型可以提高整体预测的有效性.
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