蛋白质预训练语言模型是否有助于预测蛋白质 - 连接体相互作用?
Weihong Zhang1, Fan Hu2, Wang Li2
1Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China.
Methods (San Diego, Calif.)
|September 10, 2023
概括
蛋白质预训练语言模型 (PLMs) 显著提高了蛋白质-连接体相互作用 (PLI) 预测的准确性和效率. 这项研究量化了PLM的可转移性,为特定的PLI任务确定了最佳模型,并改善了特征空间的可区分性.
科学领域:
- 计算生物学是一种计算生物学.
- 药物发现 药物发现
- 生物信息学是一种生物信息学.
背景情况:
- 蛋白质 - 连接体相互作用 (PLI) 的预测对于药物发现至关重要.
- 蛋白质预训练语言模型 (PLM) 是有前途的,但具有特定任务的不确定性.
- 在PLM和PLI任务之间存在异质性.
研究的目的:
- 量化评估蛋白质PLM在PLI预测中的重要性.
- 分析不同PLM在各种PLI任务上的性能和可转移性.
- 为改善药物发现工具提供PLM机制的见解.
主要方法:
- 在三个PLI任务 (PDBbind,Kinase,DUD-E) 中评估了三种蛋白质PLM (TAPE,ESM-1b,ProtTrans).
- 量化评估了跨任务的PLM可转移性.
- 分析了PLM对特征空间分布和可歧视性的影响.
主要成果:
- 预先训练的模型始终提高了PLI预测的准确性,并降低了时间成本.
- 在没有进行广泛的实验的情况下,为特定的PLI任务确定了最佳的PLM.
- PLM 增强了功能空间的可分辨性,有助于预测.
结论:
- 蛋白质PLM提高了PLI预测的准确性和效率.
- 量化可转移性评估指导了PLI任务的最佳PLM选择.
- 这些发现有助于开发更易于解释和更准确的PLM用于药物发现.
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