DeepProSite:使用ESMFold和预训练语言模型进行结构感知蛋白质结合部位预测
Yitian Fang1,2, Yi Jiang3, Leyi Wei4
1State Key Laboratory of Microbial Metabolism, Shanghai-Islamabad-Belgrade Joint Innovation Center on Antibacterial Resistances, Joint International Research Laboratory of Metabolic & Developmental Sciences and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200040, China.
DeepProSite使用结构和序列数据准确地识别了蛋白质结合部位. 这一新框架改进了现有方法,并证明了它在药物设计和生物研究中具有广泛的适用性.
科学领域:
- 计算生物学是一种计算生物学.
- 结构生物信息学 结构生物信息学
- 药物发现 药物发现
背景情况:
- 识别蛋白质结合点对于理解生物过程和开发新药物至关重要.
- 目前基于序列的方法缺乏准确性,原因是考虑的特征有限,缺乏结构信息.
研究的目的:
- 引入DeepProSite,这是一个用于增强蛋白质结合位点识别的新框架.
- 利用蛋白质结构和序列数据来提高预测准确度.
主要方法:
- DeepProSite集成了ESMFold中的蛋白质结构和语言模型中的序列表示.
- 使用图形转换器架构将站点预测绑定为图形节点分类.
- 根据最先进的基于序列和结构的方法评估性能.
主要成果:
- 在预测蛋白质-蛋白质和蛋白质-结位点方面,DeepProSite的性能优于现有的方法.
- 在不受约束的结构上表现出强的性能,与竞争方法不同.
- 通过成功预测核酸和其他配体的结合位,显示了概括能力.
结论:
- 在蛋白质结合点预测准确性和可靠性方面,DeepProSite提供了显著的进步.
- 该框架的多功能性扩展到各种连接体类型,突出显示其广泛的实用性.
- 在线服务器可用于DeepProSite的实际应用.
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