病毒3D:一个全面的病毒蛋白结构预测数据库
Ulad Litvin1, Spyros Lytras1,2, Alexander Jack1
1MRC-University of Glasgow Centre for Virus Research, Glasgow, UK.
Molecular systems biology
|September 16, 2025
概括
机器学习预测了超过85,000个病毒蛋白质结构,大大扩大了病毒圈.
科学领域:
- 分子生物学分子生物学
- 病毒学 病毒学
- 结构生物学 结构生物学
- 生物信息学是一种生物信息学.
背景情况:
- 病毒遗传序列因突变和宿主适应而迅速分离,使进化和功能研究复杂化.
- 蛋白质结构比序列更保守,为植物遗传学分析和功能注释提供了更稳定的基础.
- 现有的蛋白质结构数据库显著低于病毒蛋白质组.
研究的目的:
- 利用机器学习进行病毒蛋白结构的大规模预测.
- 为研究界创建病毒蛋白结构的综合数据库.
- 用预测的结构数据分析病毒的演变和功能.
主要方法:
- 使用AlphaFold2-ColabFold和ESMFold进行结构预测.
- 从4400多种病毒中生成了超过85,000种蛋白质的结构模型.
- 与实验确定结构相比,病毒蛋白结构覆盖范围扩大了30倍.
主要成果:
- 创建了Viro3D数据库,显著提高了病毒蛋白结构表示.
- 在人类和动物病毒中映射蛋白质的形式和功能.
- 研究了病毒I类融合糖蛋白的进化史,包括冠状病毒尖端蛋白的潜在起源.
结论:
- 机器学习驱动的结构预测彻底改变了病毒蛋白质组分析.
- 维罗3D数据库为分子病毒学研究提供了宝贵的资源.
- 基于结构的洞察力有助于设计新型抗病毒疗法和疫苗.
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