基于Mamba和三角形几何约束的链间接触预测
Shujia Wei1, Wei Yang1, Lei Zhang1
1Henan Key Laboratory of Big Data Analysis and Processing, Henan Engineering Laboratory of Spatial Information Processing, School of Computer and Information Engineering, Henan University, Kaifeng, 475004, Henan, China.
Computational biology and chemistry
|October 23, 2025
概括
对于长序列来说,很难预测蛋白质接触. MaMCon使用Mamba和几何约束来改善远程残留相互作用,在基准数据集上表现优于现有的方法.
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
- 在蛋白质科学中的机器学习
背景情况:
- 预测链间残留-残留接触对于建模长蛋白序列至关重要.
- 使用剩余卷积网络的现有方法在长距离相互作用方面遇到了困难.
研究的目的:
- 开发一种有效的框架来预测蛋白质链间残留物与残留物之间的接触.
- 解决当前模型在捕获远程依赖方面的局限性.
主要方法:
- 提出MaMCon,一个新的框架,整合了Mamba (状态空间模型) 和三角形几何约束.
- 使用Mamba捕获远程残留物相互作用.
- 采用三角形几何约束来完善配对的残留物表示.
主要成果:
- 马姆康在蛋白质链间残留物与残留物接触预测方面表现出有效性.
- 在TEST300,TEST218和CASP-CAPRI基准数据集上的实验验证实了这一方法.
- 与现有技术相比,拟议的方法显示了更好的性能.
结论:
- MaMCon为准确的蛋白质接触预测提供了一个有希望的解决方案,特别是对于长序列.
- 曼巴和几何约束的集成增强了复杂的链间相互作用的建模.
- 该框架为结构生物信息学研究提供了有价值的工具.
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