M-DeepAssembly:增强的DeepAssembly基于多目标多域蛋白质构成样本采样
Xinyue Cui1, Yuhao Xia1, Minghua Hou1
1College of Information Engineering, Zhejiang University of Technology, Hangzhou, 310023, China.
BMC bioinformatics
|May 5, 2025
概括
M-DeepAssembly通过使用一种新的多目标形态采样算法来改善多域蛋白质结构预测. 这种方法提高了准确性,特别是对于具有弱进化信号或大结构的蛋白质.
科学领域:
- 结构生物学 结构生物学
- 计算生物学 计算生物学
- 生物物理学的生物物理.
背景情况:
- 蛋白质结构预测对于理解功能和药物设计至关重要.
- 深度学习已经推进了单域预测,但多域预测仍然具有挑战性.
- 软弱的进化信号和大的蛋白质大小阻碍了准确的多域结构预测.
研究的目的:
- 开发一种用于准确预测多域蛋白质结构的新型计算协议.
- 解决大型多域蛋白质和具有弱域间进化信号的蛋白质结构预测方面的局限性.
主要方法:
- 提出了M-DeepAssembly,这是一个使用多目标蛋白质构成采样算法的协议.
- 使用DeepAssembly和AlphaFold2.2提取了域间相互作用和序列距离特征.
- 构建了一个多目标能量模型,并使用抽样算法来生成构造集.
- 使用内部模型质量评估算法进行最终结构选择.
主要成果:
- 在一个由164个多域蛋白质组成的测试组中,M-DeepAssembly的TM平均得分比AlphaFold2高15.4%,比DeepAssembly高2.0%.
- 集合包含了具有明显更高准确度的模型,其性能高于基线方法高达20.3%.
- 在CASP15多域目标上表现出比AlphaFold2的性能优势.
结论:
- M-DeepAssembly为多域蛋白质组装提供了一种新的方法,克服了弱进化信号和大结构的挑战.
- 该方法通过多目标形状采样产生多样化的集合,有助于预测复杂的蛋白质结构.
- 为探索多域蛋白功能做出贡献,并提供了对具有多个构造状态的蛋白质的见解.
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