Ellaway Joseph I J, Anyango Stephen, Nair Sreenath, Zaki Hossam A, Nadzirin Nurul, Powell Harold R, Gutmanas Aleksandras, Varadi Mihaly, Velankar Sameer
Protein Data Bank in Europe, European Bioinformatics Institute, Hinxton, United Kingdom.
The Warren Alpert Medical School of Brown University, Providence, Rhode Island 02903, USA.
Struct Dyn. 2024 May 17;11(3):034701. doi: 10.1063/4.0000251. eCollection 2024 May.
Studying protein dynamics and conformational heterogeneity is crucial for understanding biomolecular systems and treating disease. Despite the deposition of over 215 000 macromolecular structures in the Protein Data Bank and the advent of AI-based structure prediction tools such as AlphaFold2, RoseTTAFold, and ESMFold, static representations are typically produced, which fail to fully capture macromolecular motion. Here, we discuss the importance of integrating experimental structures with computational clustering to explore the conformational landscapes that manifest protein function. We describe the method developed by the Protein Data Bank in Europe - Knowledge Base to identify distinct conformational states, demonstrate the resource's primary use cases, through examples, and discuss the need for further efforts to annotate protein conformations with functional information. Such initiatives will be crucial in unlocking the potential of protein dynamics data, expediting drug discovery research, and deepening our understanding of macromolecular mechanisms.
研究蛋白质动力学和构象异质性对于理解生物分子系统和治疗疾病至关重要。尽管蛋白质数据库中已存入超过215,000个大分子结构，且诸如AlphaFold2、RoseTTAFold和ESMFold等基于人工智能的结构预测工具也已出现，但通常生成的是静态表示，无法完全捕捉大分子运动。在此，我们讨论将实验结构与计算聚类相结合以探索体现蛋白质功能的构象景观的重要性。我们描述了欧洲蛋白质数据库知识库开发的用于识别不同构象状态的方法，通过实例展示了该资源的主要用例，并讨论了进一步努力用功能信息注释蛋白质构象的必要性。此类举措对于释放蛋白质动力学数据的潜力、加速药物发现研究以及加深我们对大分子机制的理解至关重要。
