ATP_mCNN:通过预训练的语言模型和多窗口神经网络预测ATP结合点
Van-The Le1, Muhammad-Shahid Malik2, Yi-Jing Lin1
1Department of Computer Science and Engineering, Yuan Ze University, Chung-Li, 32003, Taiwan.
Computers in biology and medicine
|December 9, 2024
概括
我们开发了一个新的计算模型来预测蛋白质中的腺三酸盐结合残留物. 该方法使用先进的深度学习准确识别能量结合地点,改进现有技术.
科学领域:
- 计算生物学是一种计算生物学.
- 生物信息学是一种生物信息学.
- 蛋白质结构预测 蛋白质结构预测
背景情况:
- 腺三酸盐 (ATP) 对于细胞能量和功能至关重要,与众多蛋白质相互作用.
- 识别蛋白质中的ATP结合部位对于理解生物过程至关重要.
- 识别ATP结合残留物的实验方法具有挑战性和耗时.
研究的目的:
- 开发一种新的计算方法,准确预测蛋白质序列中的腺三酸盐结合残留物.
- 利用预先训练的蛋白质语言模型和多窗口卷积神经网络架构来提高预测准确性.
主要方法:
- 使用预训练的蛋白质语言模型嵌入作为输入特征.
- 开发了一种多窗口卷积神经网络 (CNN) 架构,具有平行卷积层,用于在各种窗口大小下进行图案扫描.
- 采用最大的聚合来提取突出的特征,连接在窗口上进行多尺度表示.
- 对ATP结合部位的预测进行了残留水平分类.
主要成果:
- 开发的模型在基准数据集上实现了0.95的ROC曲线下的面积 (AUC).
- 与之前的基于序列的模型相比,显著提高了预测性能.
- 超越了现有的卷积神经网络 (CNN) 基线模型.
结论:
- 预训练的语言模型和多窗口CNN对于基于序列的ATP结合残留物的预测是有效的.
- 拟议的方法为预测结合点和从主要蛋白质序列阐明分子相互作用提供了一个有希望的新方向.
- 这种计算工具可以帮助理解蛋白质的功能和设计新的治疗方法.
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