DARKIN:使用蛋白质语言模型为酸-暗酶关联的零射击基准
Emine Ayşe Sunar1, Zeynep Işık1, Mert Pekey1
1Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul 34956, Türkiye.
Bioinformatics (Oxford, England)
|October 29, 2025
概括
蛋白质语言模型 (pLMs) 对识别未经研究的激酶有希望. DARKIN基准有效地评估了为这些暗基因酶分配酸的pLM,突出显示ESM,ProtT5-XL和SaProt作为表现最佳的基因.
科学领域:
- 计算生物学是一种计算生物学.
- 生物信息学是一种生物信息学.
- 蛋白质组学是指蛋白质组学.
背景情况:
- 蛋白质语言模型 (pLM) 是分析蛋白质序列的先进工具.
- 在各种生物任务中评估pLM性能需要强大的基准.
- 确定负责特定酸化事件的激酶是细胞信号传递的一个重大挑战.
研究的目的:
- 介绍DARKIN,一个新的零射击分类基准.
- 评估各种pLMs在将酸分配到研究不足的激酶 (暗激酶) 的有效性.
- 为探索暗酶提供一个生物学相关的试验台.
主要方法:
- 开发了一个零射击分类基准 (DARKIN) 与分层数据分割.
- 使用k-NN和双线分类器评估多个蛋白质语言模型.
- 保证的数据分割尊重了零射击性质和激酶分组.
主要成果:
- ESM,ProtT5-XL和SaProt在酸分配到暗酶方面表现出卓越的表现.
- 开发的零射击分类器有效评估了plm的能力.
- 达尔金作为一个具有挑战性的pLM评估的基准.
结论:
- DARKIN为评估未被充分研究的激酶上的pLMs提供了宝贵的资源.
- 该基准有助于对暗酶功能进行更深入的研究.
- pLMs显示了促进酸盐-激酶分配的显著潜力.
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