LocPro:基于深度学习的蛋白质亚细胞定位预测,以促进多方向的药物研究
Yintao Zhang1,2, Lingyan Zheng1,2, Nanxin You2
1Department of Pharmacy, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, China.
LocPro是一个新的深度学习框架,使用LLM和专家特征的组合,准确地预测蛋白质的亚细胞定位. 这种工具通过提高目标识别和治疗设计来帮助药物开发.
科学领域:
- 计算生物学
- 生物信息学
- 药物发现
背景情况:
- 蛋白质亚细胞局部化对于药物开发至关重要,影响标识别和治疗策略.
- 精确预测蛋白质定位有助于设计有效的药物输送系统.
研究的目的:
- 介绍LocPro,一个用于预测蛋白质亚细胞定位的深度学习框架.
- 提高药物开发应用中的蛋白质定位预测的准确性和细度.
主要方法:
- 开发了LocPro,集成来自ESM2和PROFEAT的大型语言模型 (LLM) 的蛋白质表示.
- 实现混合深度神经网络架构,结合CNN,FC层和BiLSTM块.
- 使用多标签框架和基于同质性的数据集进行培训和验证.
主要成果:
- 与现有方法相比,LocPro在基于序列的多标签蛋白质亚细胞定位预测方面表现出卓越的表现.
- 案例研究证实了该框架在药物标亚细胞局部化分析中的实用性.
- 开发的框架提供了多颗粒度预测能力.
结论:
- LocPro在蛋白质定位预测方面提供了宝贵的进步, 补充了现有的工具.
- 这一框架对加速药物发现和开发过程具有重要意义.
- 通过Web服务器访问LocPro以供公众使用.
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