FUNCLAN - FUNctional annotations through Conformational Landscape Analysis

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是至关重要的，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酶活性的活性和非活性形式之间切换。为了更好地了解蛋白质功能的机制，对其三维（3D）结构及其构象状态进行结构表征至关重要。不同的能量有利的构象状态之间的转变的知识是理解蛋白质结构和进化原理的基础，可以帮助解释遗传变异的影响，设计新的药物分子和阐明分子水平上的耐药性。虽然PDB已经存档了超过165，000个单独的结构，基于UniProt登录交叉引用的数量的独特蛋白质的数量以较慢的速度增长，并且总数仅为~ 50，000，在不同序列之间冗余率有相当大的变化。这是因为每种蛋白质在PDB中可能具有多种代表性：配体结合和未结合形式;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）复合或由较小结构域或序列变体确定的结构。因此，PDB中的结构为理解配体结合位点、单个蛋白质分子以及大分子机器的构象灵活性提供了有价值的资源。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，使用现有结构的合奏可以帮助破译大分子功能的分子水平的细节。不同构象状态数据的可用性也将有助于表征全细胞断层图像中的颗粒，从而对不同疾病或发育状态的全细胞进行分子表型分析。在本项目中，我们将增强GESAMT（结构比较算法），以推导配体的构象灵活性结合位点、单个蛋白质或结构域以及大分子组装体。新的框架，FUNCLAN，将包括必要的指标，实现有意义的集群和必要的计划，以描述不同集群的成员之间的结构相似性和差异。每个簇将具有代表性的结构，并且使用来自PDBe-KB的结构和功能注释，我们将对每个簇进行分类并提供生物学背景。新的功能将验证对已知的例子，从文献中的大分子和复合物表现出特定的构象状态的数据集。将实施PDB档案范围内配体结合位点、单个大分子和大分子复合物聚类的管道。生成的数据将通过REST API、FTP站点以及一个新颖的基于Web的应用程序以编程方式提供。

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.