Deborah Harrus

Structure integration with function, taxonomy and sequences (SIFTS) provides residue‐level mappings between UniProt Knowledgebase sequences and Protein Data Bank structures and has historically been generated through internal Protein Data Bank in Europe (PDBe) pipelines. Here, PDBe‐SIFTS is presented as a fully open‐source, locally deployable implementation of this mapping framework. The pipeline combines fast, scalable sequence search using MMseqs2, an improved bounded scoring scheme for ranking candidate mappings, and residue‐level mapping refinement based on backbone connectivity. PDBe‐SIFTS is distributed as a Python package with command‐line tools for (1) building a sequence search database, (2) identifying the best sequence‐structure match, (3) one‐to‐one mapping at the residue level, and (4) generating SIFTS annotations in PDBx/mmCIF format. Benchmarking on the complete Protein Data Bank archive showed that MMseqs2 reduced archive‐scale UniProtKB searches from hours with BLASTP to minutes, approximately 22–36 times faster, while curated mappings were recovered at top rank in 93.1% of cases. The remaining discrepancies mainly involved biologically ambiguous cases such as highly conserved proteins, chimeric constructs, or closely related orthologs. These results show that PDBe‐SIFTS enables fast mapping, improving structural coherence in residue‐level alignments while delivering the most up‐to‐date and accurate mappings, comparable to expert curation. Tool: https://github.com/PDBeurope/SIFTS . Quick start notebook with example: https://github.com/PDBeurope/SIFTS/tree/master/notebooks .

The PDBx/macromolecular crystallographic information file (PDBx/mmCIF) framework is the standard for representing macromolecular structure data in the Protein Data Bank (PDB) and has been mandatory for crystallographic depositions since 2019. The PDBx/mmCIF dictionary, maintained by the PDBx/mmCIF working group established by the Worldwide PDB (wwPDB) consortium, defines the schema, data types, enumerations and relationships that govern the structure and content of mmCIFs. Pre-deposition validation of mmCIFs enables researchers to identify and correct errors, ensuring both dictionary compliance and that data values are meaningful and appropriate for their dataset. This validation step can significantly streamline the deposition process in the wwPDB OneDep system, when depositing to the PDB and Electron Microscopy Data Bank archives or using the PDB-IHM deposition system for integrative/hybrid structures, reducing delays and improving data quality. We present the mmCIF Validator , a comprehensive validation tool available in two complementary implementations: a Visual Studio Code extension for real-time interactive validation during file editing; and a standalone Python script for command-line use, batch processing, and integration into automated workflows and CI/CD (continuous integration and continuous delivery/deployment) pipelines. The validator performs comprehensive checks including item-definition validation, mandatory-item presence (with category-aware checking), enumeration-value validation, data-type validation (including automatic regex-based validation extracted from OneDep deposition-specific categories in the dictionary for types like email, phone, ORCID ID and PDB ID), range constraints with distinction between strictly allowed and advisory boundary conditions, parent/child category relationships, foreign-key integrity, composite-key validation for relationships defined by multiple items, duplicate category and item detection, and complex operation-expression parsing. The tool requires Python 3.7+ and uses only the Python standard library (no pip packages required). It works out of the box with automatic dictionary downloading from the official wwPDB repository, or users can employ a custom distant or local dictionary file. The two implementations share the same validation engine, ensuring consistent results across different usage scenarios. The validator is particularly valuable for structural biologists preparing structures for deposition, biocurators ensuring data quality and software developers building automated quality-control workflows.

Structure Integration with Function, Taxonomy and Sequences (SIFTS) provides residue-level mappings between UniProt Knowledgebase sequences and Protein Data Bank structures and has historically been generated through internal Protein Data Bank in Europe (PDBe) pipelines. Here, PDBe-SIFTS is presented as a fully open-source, locally deployable implementation of this mapping framework. The pipeline combines fast, scalable sequence search using MMseqs2, an improved bounded scoring scheme for ranking candidate mappings, and residue-level mapping refinement based on backbone connectivity. PDBe-SIFTS is distributed as a Python package with command-line tools for 1) building a sequence search database, 2) identifying the best sequence-structure match, 3) one-to-one mapping at the residue level, and 4) generating SIFTS annotations in PDBx/mmCIF format. Benchmarking on the complete Protein Data Bank archive showed that MMseqs2 reduced archive-scale UniProtKB searches from hours with BLASTP to minutes, approximately 22-36 times faster, while curated mappings were recovered at top rank in 93.1% of cases. The remaining discrepancies mainly involved biologically ambiguous cases such as highly conserved proteins, chimeric constructs, or closely related orthologs. These results show that PDBe-SIFTS enables fast mapping, improving structural coherence in residue-level alignments while delivering the most up-to-date and accurate mappings, comparable to expert curation. Tool: https://github.com/PDBeurope/SIFTS Quick start notebook with example: https://github.com/PDBeurope/SIFTS/tree/master/notebooks Broader audience statement: Matching protein sequences to their three-dimensional structures, and mapping annotations across both, is essential for understanding protein function, interactions, and molecular mechanisms. This integrated view enables richer interpretation of biological data and underpins advances in drug discovery, disease research, and protein engineering. PDBe-SIFTS provides an open and functional framework for structure–sequence mapping, allowing researchers and databases to run, inspect, and extend these mappings locally, while benefiting from faster searches, transparent scoring, and structurally informed residue-level alignments. Graphical

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.

Biomolecular structure analysis from experimental NMR studies generally relies on restraints derived from a combination of experimental and knowledge-based data. A challenge for the structural biology community has been a lack of standards for representing these restraints, preventing the establishment of uniform methods of model-vs-data structure validation against restraints and limiting interoperability between restraint-based structure modeling programs. The NMR exchange (NEF) and NMR-STAR formats provide a standardized approach for representing commonly used NMR restraints. Using these restraint formats, a standardized validation system for assessing structural models of biopolymers against restraints has been developed and implemented in the wwPDB OneDep data deposition-validation-biocuration system. The resulting wwPDB Restraint Violation Report provides a model vs. data assessment of biomolecule structures determined using distance and dihedral restraints, with extensions to other restraint types currently being implemented. These tools are useful for assessing NMR models, as well as for assessing biomolecular structure predictions based on distance restraints. Highlights PDB Structure Validation Report expanded to include Restraint Analysis NMR Exchange Format (NEF) and NMR-STAR for distance restraint representation Standard distance and dihedral restraint formats for model vs. restraint assessment Standardized restraint formats provide interoperability between modeling programs

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.

Significance: Many environmental pollutants act as endocrine disruptors that interfere with normal endocrine regulation and promote adverse effects in humans. As a major target of xenobiotics, the pregnane X receptor (PXR) is known to play opposite roles by both facilitating their clearance and mediating their toxic effects. Here, we use structural and functional approaches to describe two converging mechanisms leading to a robust synergistic stimulation of the PXR pathway by mixtures of three chemicals exhibiting very low efficacy when administered separately. This “cocktail effect” relies on two cooperative binding processes that enhance both ligand binding affinity and recruitment of transcriptional coactivators. Our findings show how chemical mixtures may alter physiology and homeostasis at concentrations where individual components are considered safe.

Sialic acid residues found as terminal monosaccharides in various types of glycan chains in cell surface glycoproteins and glycolipids have been identified as important contributors of cell-cell interactions in normal vs. abnormal cellular behavior and are pivotal in diseases such as cancers. In vertebrates, sialic acids are attached to glycan chains by a conserved subset of sialyltransferases with different enzymatic and substrate specificities. ST6Gal I is a sialyltransferase using activated CMP-sialic acids as donor substrates to catalyze the formation of a α2,6-glycosidic bond between the sialic acid residue and the acceptor disaccharide LacNAc. Understanding sialyltransferases at the molecular and structural level shed light into the function. We present here two human ST6Gal I structures, which show for the first time the enzyme in the unliganded state and with the full donor substrate CMP-Neu5Ac bound. Comparison of these structures reveal flexibility of the catalytic loop, since in the unliganded structure Tyr354 adopts a conformation seen also as an alternate conformation in the substrate bound structure. CMP-Neu5Ac is bound with the side chain at C-5 of the sugar residue directed towards empty space at the surface of the protein. Furthermore, the exact binding mode of the sialic acid moiety of the substrate directly involves sialylmotifs L, S and III and positions the sialylmotif VS in the immediate vicinity. PROTEIN DATA BANK ACCESSION CODES Atomic coordinates and structure factors of the human wild-type unliganded and CMP-Neu5Ac bound ST6Gal I have been deposited with the PDB with accession codes 6QVS and 6QVT, respectively.