The Genome3D Consortium for Structural Annotations of Selected Model Organisms

Protein structures unravel the signatures and patterns of deep time evolution

The formulation and testing of hypotheses using 'big biology data' often lie at the interface of computational biology and structural biology. The Protein Data Bank (PDB), which was established about 50 years ago, catalogs three-dimensional (3D) shapes of organic macromolecules and showcases a structural view of biology. The comparative analysis of the structures of homologs, particularly of proteins, from different species has significantly improved the in-depth analyses of molecular and cell biological questions. In addition, computational tools that were developed to analyze the 'protein universe' are providing the means for efficient resolution of longstanding debates in cell and molecular evolution. In celebrating the golden jubilee of the PDB, much has been written about the transformative impact of PDB on a broad range of fields of scientific inquiry and how structural biology transformed the study of the fundamental processes of life. Yet, the transforming influence of PDB on one field of inquiry of fundamental interest-the reconstruction of the distant biological past-has gone almost unnoticed. Here, I discuss the recent advances to highlight how insights and tools of structural biology are bearing on the data required for the empirical resolution of vigorously debated and apparently contradicting hypotheses in evolutionary biology. Specifically, I show that evolutionary characters defined by protein structure are superior compared to conventional sequence characters for reliable, data-driven resolution of competing hypotheses about the origins of the major clades of life and evolutionary relationship among those clades. Since the better quality data unequivocally support two primary domains of life, it is imperative that the primary classification of life be revised accordingly.

Comprehensive classification of proteins based on structures that engage lipids by COMPOSEL

Structures can now be predicted for any protein using programs like AlphaFold and Rosetta, which rely on a foundation of experimentally determined structures of architecturally diverse proteins. The accuracy of such artificial intelligence and machine learning (AI/ML) approaches benefits from the specification of restraints which assist in navigating the universe of folds to converge on models most representative of a given protein's physiological structure. This is especially pertinent for membrane proteins, with structures and functions that depend on their presence in lipid bilayers. Structures of proteins in their membrane environments could conceivably be predicted from AI/ML approaches with user-specificized parameters that describe each element of the architecture of a membrane protein accompanied by its lipid environment. We propose the Classification Of Membrane Proteins based On Structures Engaging Lipids (COMPOSEL), which builds on existing nomenclature types for monotopic, bitopic, polytopic and peripheral membrane proteins as well as lipids. Functional and regulatory elements are also defined in the scripts, as shown with membrane fusing synaptotagmins, multidomain PDZD8 and Protrudin proteins that recognize phosphoinositide (PI) lipids, the intrinsically disordered MARCKS protein, caveolins, the β barrel assembly machine (BAM), an adhesion G-protein coupled receptor (aGPCR) and two lipid modifying enzymes - diacylglycerol kinase DGKε and fatty aldehyde dehydrogenase FALDH. This demonstrates how COMPOSEL communicates lipid interactivity as well as signaling mechanisms and binding of metabolites, drug molecules, polypeptides or nucleic acids to describe the operations of any protein. Moreover COMPOSEL can be scaled to express how genomes encode membrane structures and how our organs are infiltrated by pathogens such as SARS-CoV-2.

3D-Beacons: decreasing the gap between protein sequences and structures through a federated network of protein structure data resources

While scientists can often infer the biological function of proteins from their 3-dimensional quaternary structures, the gap between the number of known protein sequences and their experimentally determined structures keeps increasing. A potential solution to this problem is presented by ever more sophisticated computational protein modeling approaches. While often powerful on their own, most methods have strengths and weaknesses. Therefore, it benefits researchers to examine models from various model providers and perform comparative analysis to identify what models can best address their specific use cases. To make data from a large array of model providers more easily accessible to the broader scientific community, we established 3D-Beacons, a collaborative initiative to create a federated network with unified data access mechanisms. The 3D-Beacons Network allows researchers to collate coordinate files and metadata for experimentally determined and theoretical protein models from state-of-the-art and specialist model providers and also from the Protein Data Bank.