MAKA-Map:通过混合神经框架对蛋白质折叠机制进行实值距离预测,集成Mamba和Kolmogorov-Arnold网络
Benzhi Dong1, Yumeng Hua1, Chang Hou1
1College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China.
Biomolecules
|February 27, 2026
概括
这项研究引入了使用Mamba和Kolmogorov-Arnold网络 (KAN) 预测蛋白质结构的新框架. 该方法提高了真实值距离预测的准确性,这对于蛋白质折叠和药物发现至关重要.
科学领域:
- 计算生物学是一种计算生物学.
- 结构生物信息学 结构生物信息学
- 结构生物学中的机器学习
背景情况:
- 实值的残留间距离图对于蛋白质结构预测和功能研究至关重要.
- 当前的方法在建模远程相互作用和保持拓一致性方面面临着挑战.
研究的目的:
- 为准确的实值间残留距离预测开发一种新的框架.
- 改进全球相互作用和蛋白质中的非线性结构表示的建模.
主要方法:
- 整合Mamba架构用于全球相互作用的选择性状态空间建模.
- 纳入Kolmogorov-Arnold网络 (KAN) 进行增强的非线性结构表示.
- 在CASP13,CASP14和CASP15基准数据集上进行验证.
主要成果:
- 预测准确度达到了86.53% (CASP13),85.44% (CASP14) 和82.77% (CASP15).
- 与现有最先进的方法相比,表现出优越的性能.
- 实值距离预测的准确度显著提高.
结论:
- 拟议的Mamba-KAN框架大大提高了实值距离预测的准确性.
- 这种方法为推进蛋白质折叠研究,功能注释和药物发现提供了有前途的工具.
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