Deepti Gupta

Protein Data Bank in Europe (PDBe) is a founding member of the worldwide Protein Data Bank (wwPDB), delivering open access to experimentally determined macromolecular structures. PDBe also delivers enriched annotations contributed by the PDBe-Knowledge Base (PDBe-KB) consortium. The macromolecular entry pages are the primary interface for millions of users who explore experimental structure data. Here, we describe the redesign of the PDBe entry pages that organize content into logical views, thereby improving usability and facilitating the use of structure models, driving fundamental and applied research, and supporting education. The new design introduces several enhancements, including integrated and central 3D visualization, sequence feature exploration, standardized molecular scenes, AI-driven residue-level annotations from text mining of the literature, and streamlined annotation access via refactored APIs. Importantly, researchers can now upload their own residue-level features and visualize them directly in structural context, displayed alongside PDBe-KB annotations. This functionality enables scientists to interpret unpublished or private data in relation to high-quality structural information, lowering barriers for those without prior expertise in structural biology. Together, these updates create a more accessible, flexible, and scalable framework for interacting with structural data, expanding the resource’s value to both domain specialists and the wider life sciences community.

We present a novel system that leverages curators in the loop to develop a dataset and model for detecting residue-level functional annotations and other protein structure features from standard publication text. Our approach involves the integration of data from multiple resources, including PDBe, EuropePMC, PubMedCentral, and PubMed, combined with annotation guidelines from UniProt, while employing LitSuggest and Huggingface models as tools in the annotation process. A team of seven annotators manually curated ten articles for named entities, which we utilized to train a starting PubmedBert model from Huggingface. Using a human-in-the-loop annotation system, we developed the best model with commendable performance metrics of 0.90 for precision, 0.92 for recall, and 0.91 for F1-measure. Our proposed system showcases a successful synergy of machine learning techniques and human expertise in curating a dataset for residue-level functional annotations and protein structure features. The results demonstrate the potential for broader applications in protein research, bridging the gap between advanced machine learning models and the indispensable insights of domain experts.

Macromolecular complexes are essential functional units in nearly all cellular processes, and their atomic-level understanding is critical for elucidating and modulating molecular mechanisms. The Protein Data Bank (PDB) serves as the global repository for experimentally determined structures of macromolecules. Structural data in the PDB offer valuable insights into the dynamics, conformation, and functional states of biological assemblies. However, the current annotation practices lack standardised naming conventions for assemblies in the PDB, complicating the identification of instances representing the same assembly. In this study, we introduce a method leveraging resources external to PDB, such as the Complex Portal, UniProt and Gene Ontology, to describe assemblies and contextualise them within their biological settings accurately. Employing the proposed approach, we assigned standard names and provided value-added annotations to over 90% of unique assemblies in the PDB. This standardisation of assembly data enhances the PDB, facilitating a deeper understanding of these cellular components. Furthermore, the data standardisation improves the PDB’s FAIR attributes, fostering more effective basic and translational research and education across scientific disciplines.