pLM4CPPs:基于蛋白质语言模型的预测器,用于细胞透
Nandan Kumar1, Zhenjiao Du1, Yonghui Li1
1Department of Grain Science and Industry, Kansas State University, Manhattan, Kansas 66506, United States.
Journal of chemical information and modeling
|January 29, 2025
概括
本研究介绍了pLM4CCPs,这是一种新的深度学习模型,使用先进的蛋白质语言模型准确预测细胞透 (CPP). 新方法显著提高了预测准确度,有助于药物输送研究.
科学领域:
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
- 酸科学 酸科学
背景情况:
- 细胞透 (CPPs) 对于药物输送和细胞内向至关重要,因为它们具有膜透性.
- 准确预测CPP对于降低实验成本和加速验证至关重要.
- 现有的预测方法需要改进,以提高可靠性和性能.
研究的目的:
- 评估各种预训练的蛋白质语言模型 (pLMs) 在表示CPP的有效性.
- 为CPP分类开发一种新的,高性能的深度学习模型.
- 建立一个可靠的计算工具来识别潜在的CPP.
主要方法:
- 评估了来自各种pLM的嵌,包括ESM变体,ProtT5和ProtBERT.
- 开发了pLM4CCPs,这是一种使用卷积神经网络 (CNN) 进行二进制CPP分类的深度学习架构.
- 从多个模型中集成预测,以创建共识分类,以提高可靠性.
主要成果:
- pLM4CCPs在准确度,MCC和灵敏度方面超过了现有的最先进的CPP预测模型.
- 在个别的pLM中,ESM-1280和ProtT5-XL BFD表现最好.
- pLM4CCPs模型显示了显著的改进:精度为4.9-5.5%,MCC为9.3-10.2%,灵敏度为14.1-19.6%.
结论:
- 开发的pLM4CCPs模型为CPP预测提供了卓越的性能.
- 从多个pLM中利用共识预测可以提高分类可靠性.
- 可访问的Web服务器和源代码促进了功能建模的更广泛的研究应用.
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