在具有距离矩阵的蛋白质中寻找 (II) 结合位点
Vincenzo Laveglia1, Milana Bazayeva1,2, Claudia Andreini1,2,3
1Department of Chemistry, University of Florence, Sesto Fiorentino 50019, Italy.
Bioinformatics (Oxford, England)
|October 25, 2023
概括
我们开发了Master of Metals (MOM),这是一个生物信息学工具,可以预测蛋白质结构中的 (II) 结合位点. MOM加速功能注释,在酵母蛋白质组分析中达到76%的精度.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 结构生物学 结构生物学
背景情况:
- 高通量测序产生了大量的基因组数据,但蛋白质功能注释仍然是一个瓶.
- 蛋白质功能注释的实验方法通常是低通量和昂贵的.
- (II) 离子是5%-10%的蛋白质的关键共因子,对它们的生理功能至关重要.
研究的目的:
- 开发一种用于预测蛋白质3D结构中的 (II) 结合位点的计算工具.
- 通过识别重要的金属结合点来加速蛋白质的功能注释.
- 用预测的蛋白质结构为整个蛋白质组注释提供可扩展的解决方案.
主要方法:
- 利用神经网络来预测潜在的 (II) 结合部位.
- 基于已知结合位点的局部结构性质实施过步骤.
- 开发了金属大师 (MOM) 工具,比较Cα和Cβ原子的距离矩阵.
- 集成的MOM与AlphaFold生成的蛋白质组结构模型进行大规模分析.
主要成果:
- 金属大师 (MOM) 工具已成功开发,用于预测 (II) 结合位点.
- 将MOM应用于酵母蛋白质组,证明了其对整个生物体的能力.
- 在基于同类蛋白质数据的基础上,在预测酵母中的 (II) 结合位点方面获得了大约76%的精度.
结论:
- MOM提供了一种快速有效的生物信息学方法来对蛋白质功能注释,特别是对于 (II) 结合部位.
- 该工具可用于使用预测的蛋白质结构进行大规模蛋白质组分析.
- 作为一个开源的Python实现,MOM是可用的,这有助于更广泛的研究使用.
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