Automated Pipeline for Comparing Protein Conformational States in the PDB to AlphaFold2 Predictions
Automated Pipeline for Comparing Protein Conformational States in the PDB to AlphaFold2 Predictions
复制标题
用于将 PDB 中的蛋白质构象状态与 AlphaFold2 预测进行比较的自动化流程
DOI：
10.1101/2023.07.13.545008
复制
发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Ellaway J
中科院分区：
文献类型：
--
作者：
Ellaway J
蛋白质作为分子机器，必然是执行基本细胞功能的动态大分子。认识它们的稳定构象对于理解疾病的分子机制很重要。虽然基于人工智能的计算方法已经能够预测蛋白质结构，但蛋白质动力学的预测仍然是一个挑战。在这里，我们提出了一个确定性管道，它将实验确定的蛋白质结构聚集在一起，以全面识别蛋白质数据库中的构象状态。我们的方法基于全局构象 (GLOCON) 差异评分对蛋白质链进行聚类，该评分是根据成对的 C-alpha 距离计算得出的。通过叠加簇状结构，可以观察构象状态的差异和相似之处。此外，我们还为用户提供将 AlphaFold 数据库中的预测模型叠加到 PDB 结构集群的能力。这种聚类流程显着提高了研究人员探索 PDB 内构象景观的能力。所有聚类和叠加模型都可以在 PDBe 知识库网站上的 Mol* 中查看，或者通过我们的 GraphAPI 和 FTP 服务器作为原始注释进行访问。该集群包作为 Apache-2.0 许可证下的开源 Python3 包提供。
Proteins, as molecular machines, are necessarily dynamic macromolecules that carry out essential cellular functions. Recognising their stable conformations is important for understanding the molecular mechanisms of disease. While AI-based computational methods have enabled protein structure prediction, the prediction of protein dynamics remains a challenge. Here, we present a deterministic pipeline that clusters experimentally determined protein structures to comprehensively recognise conformational states across the Protein Data Bank. Our approach clusters protein chains based on a GLObal CONformation (GLOCON) difference score, which is computed from pairwise C-alpha distances. By superposing the clustered structures, differences and similarities in conformational states can be observed. Additionally, we offer users the ability to superpose predicted models from the AlphaFold Database to the clusters of PDB structures. This clustering pipeline significantly advances researchers' ability to explore the conformational landscape within the PDB. All clustered and superposed models can be viewed in Mol* on the PDBe Knowledge Base website, or accessed in as raw annotations via our GraphAPI and FTP server. The clustering package is made available as an open-source Python3 package under the Apache-2.0 license.
影响因子：
4.4
作者：
Hsu, Darren J.;Leshchev, Denis;Chen, Lin X.
通讯作者：
Chen, Lin X.