FUNCLAN - FUNctional annotations through Conformational Landscape Analysis

FUNCLAN - 通过构象景观分析进行功能注释

• 批准号: BB/V016113/1
• 负责人: Sameer Velankar
• 金额: $ 51.33万
• 依托单位: European Bioinformatics Institute
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FV016113%2F1
• 关键词: FUNCLAN; FUNctional; annotations; through; Conformational

The dynamic nature of proteins leading to multiple conformational states is critical in many biological processes from forming macromolecular complexes with other proteins, small molecules (ligands) or nucleic acids to switching between active and inactive forms for enzymatic activity. To gain improved mechanistic insights into the function of proteins, structural characterisation of their three-dimensional (3D) structures and their conformational states is critical. Knowledge of the transition between different energetically favoured conformational states is fundamental to the understanding of the principles of protein structure and evolution and can help in explaining the effects of genetic variants, in designing new drug molecules and in elucidating drug resistance at the molecular level.Although the PDB has archived more than 165,000 individual structures, the number of unique proteins based on the number of UniProt accession cross-references grows at a slower pace and totals only ~50,000, with a considerable variation in the redundancy rate amongst different sequences. This is because each protein may have multiple representatives in the PDB: ligand-bound and unbound forms; structures in multiple space groups or sample conditions; in complex with other macromolecules (proteins or nucleic acid) or structures determined of smaller domains or sequence variants. Thus, the structures in the PDB provide a valuable resource for understanding the conformational flexibility of ligand binding sites, individual protein molecules as well as large macromolecular machines. Understanding the similarities and differences in ligand binding sites, individual protein molecules and the large macromolecular complexes using the ensemble of available structures can assist in deciphering the molecular level details of macromolecular function. The availability of data on distinct conformational states will also assist in characterising the particles in whole-cell tomograms, thus allowing molecular phenotyping of whole cells in different disease or development states.In this project we will enhance GESAMT, the structure comparison algorithm, to derive conformational flexibility of ligand binding sites, individual proteins or domains and macromolecular assemblies. The new framework, FUNCLAN, will include the necessary metrics to realise meaningful clustering and the necessary scheme to describe the structural similarities and differences between members of different clusters. Each cluster will have a representative structure and using the structural and functional annotations from PDBe-KB, we will characterise each cluster and provide biological context. The new functionality will be validated against a dataset of known examples from the literature of macromolecules and complexes exhibiting specific conformational states. A pipeline for a PDB archive-wide clustering of ligand binding sites, individual macromolecules and macromolecular complexes will be implemented. The resulting data will be made available programmatically via a REST API, an FTP site, and also via a novel web-based application.

在许多生物过程中，从与其他蛋白质、小分子(配体)或核酸形成大分子复合体，到在酶活性的活性和非活性形式之间切换，蛋白质的动态性质导致多种构象状态的变化是至关重要的。为了更好地了解蛋白质的功能，对其三维(3D)结构和构象状态的结构特征至关重要。了解不同能量有利的构象状态之间的转换是理解蛋白质结构和进化原理的基础，有助于解释遗传变异的影响，设计新的药物分子，并在分子水平上阐明耐药性。尽管PDB已经存档了超过165,000个个体结构，但基于UniProt登录交叉引用的数量的独特蛋白质的数量增长速度较慢，总共只有50,000个左右，不同序列之间的冗余率有相当大的差异。这是因为每个蛋白质在PDB中可能有多个代表：配体结合和非结合形式；在多个空间群或样品条件下的结构；与其他大分子(蛋白质或核酸)的复合体或由较小结构域或序列变体确定的结构。因此，PDB中的结构为了解配体结合位点、单个蛋白质分子以及大分子机器的构象灵活性提供了宝贵的资源。利用可用结构的集合来理解配体结合部位、单个蛋白质分子和大分子复合体的异同，有助于破译大分子功能的分子水平细节。关于不同构象状态的数据的可用性也将有助于表征全细胞断层图像中的颗粒，从而允许在不同疾病或发育状态下的整个细胞的分子表型。在这个项目中，我们将增强结构比较算法Gesamt，以获得配体结合位置、单个蛋白质或结构域和大分子组装的构象灵活性。新的框架Funclan将包括实现有意义的集群所需的指标，以及描述不同集群成员之间结构相似和差异的必要方案。每个簇都有一个代表性的结构，使用PDBe-KB的结构和功能注释，我们将描述每个簇的特征并提供生物学背景。新的功能将根据文献中显示特定构象状态的大分子和络合物的已知示例的数据集进行验证。将实施一条PDB档案范围内配体结合位点、单个大分子和大分子络合物聚集的管道。生成的数据将通过REST API、一个ftp站点以及一个新的基于Web的应用程序以编程方式提供。

项目成果

PDBe and PDBe-KB: Providing high-quality, up-to-date and integrated resources of macromolecular structures to support basic and applied research and education.

• DOI: 10.1002/pro.4439
• 发表时间: 2022-10
• 期刊: PROTEIN SCIENCE
• 影响因子: 8
• 作者: Varadi, Mihaly;Anyango, Stephen;Appasamy, Sri Devan;Armstrong, David;Bage, Marcus;Berrisford, John;Choudhary, Preeti;Bertoni, Damian;Deshpande, Mandar;Leines, Grisell Diaz;Ellaway, Joseph;Evans, Genevieve;Gaborova, Romana;Gupta, Deepti;Gutmanas, Aleksandras;Harrus, Deborah;Kleywegt, Gerard J.;Bueno, Weslley Morellato;Nadzirin, Nurul;Nair, Sreenath;Pravda, Lukas;Afonso, Marcelo Querino Lima;Sehnal, David;Tanweer, Ahsan;Tolchard, James;Abrams, Charlotte;Dunlop, Roisin;Velankar, Sameer
• 通讯作者: Velankar, Sameer

Automated Pipeline for Comparing Protein Conformational States in the PDB to AlphaFold2 Predictions

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
• 发表时间: 2023
• 作者: Ellaway J