ProtTrans和多窗口扫描卷积神经网络用于预测蛋白质-交互位点
Van-The Le1, Zi-Jun Zhan1, Thi-Thu-Phuong Vu2
1Department of Computer Science and Engineering, Yuan Ze University, Chung-Li, 32003, Taiwan.
Journal of molecular graphics & modelling
|April 20, 2024
概括
这项研究使用先进的机器学习增强了蛋白质-相互作用预测,实现了药物发现和癌症治疗应用的高精度.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 机器学习 机器学习
背景情况:
- 准确预测蛋白质-相互作用对于理解生物过程至关重要.
- 预测这些相互作用的现有方法在性能和范围上有局限性.
研究的目的:
- 开发和评估用于预测蛋白质-相互作用的先进机器学习模型.
- 为了比较不同机器学习方法的性能,包括基于序列的模型,卷积神经网络 (CNN) 和传统分类器.
- 探索这些模型在癌症治疗和药物发现中的应用.
主要方法:
- 使用预先训练的语言模型 (例如,ProtTrans) 在广泛的蛋白质序列数据上进行训练.
- 实现了多视图窗口扫描CNN,以增强功能提取.
- 将集成模型与标准CNN和传统分类方法进行比较.
主要成果:
- 在超过21亿个蛋白质序列上训练的ProtTrans模型显示出显著的性能改善.
- 综合模型获得了高的曲线下面积 (AUC) 分数:PepBCL Set_1上的0.856和PepBCL Set_2.2.上的0.823.
- 获得了0.564 (PepBCL Set 1) 和0.527 (PepBCL Set 2) 的精度,超过了以前的方法.
结论:
- 先进的机器学习,特别是利用大型语言模型和CNN,显著提高了蛋白质-的相互作用预测.
- 开发的模型为加速药物发现和治疗开发提供了强大的工具.
- 潜在的应用包括在癌症治疗中选择性向癌细胞.
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