iMFP-LG:使用蛋白质语言模型和基于图形的深度学习识别新型多功能
Jiawei Luo1, Kejuan Zhao2, Junjie Chen1
1School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen 518055, China.
Genomics, proteomics & bioinformatics
|November 25, 2024
概括
我们开发了iMFP-LG,这是一种使用蛋白质语言模型和图形注意力网络的新方法,用于识别多功能. 这种工具成功地选了数百万种,发现了具有抗微生物和抗癌特性的候选物.
科学领域:
- 生物化学和生物信息学
- 酸科学 酸科学
- 药物发现 药物发现 药物发现
背景情况:
- 功能性,简短的氨基酸序列,提供各种生物益处.
- 研究已经从单功能转向多功能,但探索仍然有限.
- 准确的识别方法对于发现和理解多功能来说至关重要.
研究的目的:
- 介绍iMFP-LG,一种用于识别多功能的新型计算方法.
- 评估iMFP-LG的性能与现有的最先进的方法相比.
- 应用iMFP-LG用于发现具有联合抗微生物和抗癌功能的新.
主要方法:
- 使用蛋白质语言模型 (pLMs) 和图形注意网络 (GATs) 进行分析.
- 开发了iMFP-LG计算框架,用于多功能标识.
- 进行了比较分析,将iMFP-LG与其他方法进行比较.
主要成果:
- iMFP-LG在识别多功能生物活性和治疗性上表现出卓越的表现.
- 通过对注意力模式的可视化,证实了该方法的可解释性.
- 查发现了8种具有潜在抗微生物和抗癌活性的候选.
- 一个经过验证的候选药物同时表现出抗菌和抗癌的特性.
结论:
- iMFP-LG是识别多功能的有效工具.
- 该方法有助于发现具有联合治疗功能的.
- iMFP-LG可以显著促进类药物设计和发现的进步.
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