整合预训练的蛋白质语言模型和多窗口扫描深度学习网络,以准确识别膜蛋白中的二次活性载体
Muhammad Shahid Malik1, Yu-Yen Ou2
1Department of Computer Science and Engineering, Yuan Ze University, Chung-Li 32003, Taiwan; Department of Computer Sciences, Karakoram International University, Gilgit-Baltistan, Pakistan.
Methods (San Diego, Calif.)
|October 23, 2023
概括
我们开发了一种使用深度学习的计算方法来识别二次活性运输体,这对于细胞运输至关重要,并且与癌症有关. 这种方法可以从序列中准确地识别这些蛋白质,有助于膜蛋白研究.
科学领域:
- 生物化学 生物化学
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
背景情况:
- 二次活性运输体对于细胞运输和疾病调节至关重要.
- 对这些载体的生物化学研究在实验上具有挑战性.
- 精确识别载体对于了解细胞功能和疾病机制至关重要.
研究的目的:
- 开发一种计算方法,从蛋白质序列中识别二次活性载体.
- 为了提高准确性,利用预先训练的语言模型和深度学习.
- 为推进膜蛋白研究和功能分析提供一个工具.
主要方法:
- 利用了来自UniProt.的290个二次活性载体和5,420种其他膜蛋白的数据集.
- 提取的功能包括一次性编码,位置特定的评分矩阵配置文件和ProtTrans语言模型嵌入.
- 采用多窗口卷积神经网络架构来分析多个尺度上的序列模式.
主要成果:
- 在识别二次活性载体方面,获得了86%的灵敏度,99%的特异性和98%的整体准确性.
- 拟议的模型表现优于传统的机器学习方法.
- 证明了将ProtTrans嵌入式与多窗口CNN集成的有效性.
结论:
- 计算方法有效地识别了使用深度学习和预训练的语言模型的二次活性传递器.
- 这种方法为膜蛋白的准确功能分析提供了一个有前途的工具.
- 计算识别方面的进步可以显著有利于膜蛋白研究.
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