FUNCLAN - FUNctional annotations through Conformational Landscape Analysis

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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.