RCSB蛋白质数据库:可视化实验确定PDB结构的组与蛋白质的计算结构模型一起
Joan Segura1, Yana Rose1, Chunxiao Bi1
1Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California San Diego, San Diego, CA, United States.
Frontiers in bioinformatics
|December 19, 2023
概括
人工智能工具现在可以预测蛋白质的3D结构,从而创造出大量的新数据. RCSB PDB 门户提供了新的工具来管理,搜索和可视化这个不断增长的蛋白质结构集合.
科学领域:
- 结构生物学是结构生物学.
- 生物信息学是一种生物信息学.
- 计算生物学是一种计算生物学.
背景情况:
- 像AlphaFold,RosettaFold和ESMFold这样的人工智能 (AI) 和机器学习 (ML) 模型可以准确地从氨基酸序列中预测蛋白质的3D结构.
- 这些人工智能工具从蛋白质组和元基因组研究中产生了前所未有的结构数据.
- 现有的数据管理和可视化工具难以处理生物分子结构信息的指数级增长.
研究的目的:
- 介绍一套用于管理,搜索,组织和可视化3D蛋白质结构大集合的工具.
- 为满足对实验和计算预测蛋白质结构数据的有效处理的需求.
- 为研究人员提供先进的可视化功能,用于探索多个序列和结构对齐.
主要方法:
- 在RCSB PDB以研究为重点的网络门户网站 (RCSB.org) 上开发和部署一套新的工具.
- 整合管理,搜索,聚类和可视化大型3D宏分子结构集的能力.
- 实现总结和可视化元数据,生物/生化注释和结构特征的功能.
- 包括先进的可视化技术,以交互探索序列和结构对齐.
主要成果:
- 现在在RCSB.org上为研究人员提供了一套全面的工具.
- 这些工具有助于有效管理和探索实验和预测的蛋白质结构数据.
- 增强的可视化功能允许对多个序列和结构对齐进行交互式分析.
- 该平台支持正在勃发展的3D宏分子结构数据集.
结论:
- 新的RCSB PDB工具对于处理大规模蛋白质结构数据的研究人员来说至关重要.
- 这些工具能够有效地处理数据,搜索和可视化,加速科学发现.
- 该平台支持多种结构数据的集成和分析,包括人工智能预测模型.
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