使用生成变压器识别蛋白质化部位,并使用具有深层囊网络的二维表示方法确定蛋白质化部位
Shahid Akbar1,2, Ali Raza3,4,5, Wajdi Alghamdi6
1Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China.
iScience
|December 24, 2025
概括
我们开发了iSucc-SnCNs,这是一种智能模型,用于预测蛋白质化位点. 这种计算工具增强了对蛋白质功能的理解,并通过实现高精度来帮助药物发现.
科学领域:
- 生物化学 生物化学
- 计算生物学 计算生物学
- 蛋白质组学是指蛋白质组学.
背景情况:
- 蛋白质化是一种关键的翻译后修饰,调节细胞功能.
- 精确识别化部位对于理解蛋白质作用和开发向疗法至关重要.
研究的目的:
- 开发一个智能计算模型,iSucc-SnCNs,用于准确预测蛋白质化部位.
- 通过改进化部位识别来增强蛋白质功能分析和药物发现.
主要方法:
- 使用ProtGPT2进行蛋白质序列编码.
- 使用SMR和PSSM矩阵 (SMR-HOG,SMR-DCT,PSSM-DWT) 提取的结构特征.
- 采用BTGA+KNN用于特征选择和自我规范化囊神经网络 (Sn-CapsNet) 进行预测.
主要成果:
- 通过iSucc-SnCNs获得了92.92%的准确性和0.96 AUC.
- 在预测准确度方面,传统模型的表现优于17%.
- 在独立数据集 (Ind-I和Ind-II) 上展示了强大的概括,显著提高了性能.
结论:
- iSucc-SnCNs提供了一个强大的和高效的框架,用于大规模的化部位预测.
- 该模型在药物发现的背景下显著推进了蛋白质功能分析.
- 突出了高级计算模型在翻译后修改研究中的潜力.
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