MCNN-AAPT:使用蛋白质语言模型和多窗口深度学习,准确地分类和功能预测二次活性载体中的氨基酸和载体
Muhammad Shahid Malik1,2, Van The Le1, Syed Muazzam Ali Shah3
1Department of Computer Science and Engineering, Yuan Ze University, Chung-Li, Taiwan.
Journal of biomolecular structure & dynamics
|November 22, 2024
概括
本研究介绍了一种使用先进人工智能的计算方法,以准确分类氨基酸和载体,这对于了解细胞运输和疾病至关重要. 这种方法有效地预测了溶解物载体蛋白协会,有助于药物开发.
科学领域:
- 生物化学和分子生物学
- 计算生物学 计算生物学
- 基因组学和蛋白质组学
背景情况:
- 二次活性载体 (SATs) 对于细胞功能至关重要,可以将分子移动穿过膜.
- 精确分类SATs,特别是氨基酸和载体,是理解生理过程和癌症等疾病的关键.
研究的目的:
- 开发一个计算框架,在SAT家族中对氨基酸和载体进行分类.
- 预测这些载体与溶液载体蛋白 (SLC) 的功能关联.
主要方法:
- 利用了来自UniProt和TCDB的448个SAT和36个SLC蛋白的数据集.
- 综合预训练的蛋白质语言模型 (ProtTrans,ESM-1b,ESM-2) 与深度学习.
- 采用多窗口扫描技术进行序列模式分析.
主要成果:
- 在分类氨基酸/载体方面,ProtTrans实现了98.21%的准确性,87.32%的灵敏性和99.76%的特异性.
- 该模型预测了SLC蛋白协会的准确率为88.89%,MCC为0.7750.
结论:
- 蛋白质语言模型和深度学习的整合为SAT功能分类提供了一个强大的方法.
- 这些发现支持药物开发,疾病研究和细胞运输理解的进步.
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