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.

导致多种构象状态的蛋白质的动态性质在许多生物过程中至关重要，从与其他蛋白质、小分子（配体）或核酸形成大分子复合物到在活性和非活性形式之间切换以实现酶活性。为了更好地了解蛋白质的功能，其三维 (3D) 结构及其构象状态的结构表征至关重要。了解不同能量有利构象状态之间的转变对于理解蛋白质结构和进化原理至关重要，并且有助于解释遗传变异的影响、设计新药物分子以及阐明分子水平的耐药性。尽管 PDB 已存档超过 165,000 个单独的结构，但基于 UniProt 登录交叉引用的数量，独特蛋白质的数量仍在增长 速度较慢，总数只有~50,000，不同序列之间的冗余率差异很大。这是因为每种蛋白质在PDB中可能有多种代表：配体结合和非结合形式；多个空间群或样本条件下的结构；与其他大分子（蛋白质或核酸）或由较小结构域或序列变体确定的结构复合。因此，PDB 中的结构为理解配体结合位点、单个蛋白质分子以及大型大分子机器的构象灵活性提供了宝贵的资源。使用可用结构的集合来了解配体结合位点、单个蛋白质分子和大分子复合物的相似性和差异可以帮助破译大分子功能的分子水平细节。不同构象状态数据的可用性也将有助于表征全细胞断层扫描中的颗粒，从而允许对不同疾病或发育状态下的全细胞进行分子表型分析。在该项目中，我们将增强GESAMT（结构比较算法），以推导配体结合位点、单个蛋白质或结构域以及大分子组装体的构象灵活性。新框架FUNCLAN将包括实现有意义的聚类所需的指标以及描述不同聚类成员之间结构相似性和差异的必要方案。每个簇将具有代表性结构，并使用 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。新功能将根据表现出特定构象状态的大分子和复合物文献中的已知示例数据集进行验证。将实施配体结合位点、单个大分子和大分子复合物的 PDB 档案范围聚类的管道。生成的数据将通过 REST API、FTP 站点以及基于网络的新型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