Preeti Choudhary

PDBe-KB Ligands provides a comprehensive resource for exploring ligands found in PDB structures, including small molecules, peptide-like antibiotics and inhibitors, and carbohydrates. Learn how to search for ligands and explore their chemical properties, structures, interactions, and functional information across the PDB.

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 .

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

Three-dimensional structures of protein-ligand complexes are essential for insights into the molecular principles that govern ligand recognition and binding. With more than 180,000 ligand-bound entries in the Protein Data Bank (PDB), representing over two million individual complexes, the volume of available structural data offers unprecedented opportunities for large-scale analysis of interaction patterns. Analysis of interaction patterns across the PDB archive can help discover similarities and differences in the binding modes of ligands, assisting in drug discovery. However, large-scale analysis of up-to-date information remains a significant challenge due to the rapid growth of data. Here, we introduce the Extended Connectivity Interaction Fingerprint (ECIFP), an interaction-based fingerprint that simplifies 3D protein-ligand contact information into a fingerprint, while retaining key molecular and chemical features of the interacting fragments. The simpler fingerprint representation of the interaction data makes comparison of millions of protein-ligand complexes tractable. Benchmarking shows that ECIFP outperforms ligand-only Extended Connectivity Fingerprints in identifying similar binding sites across identical protein sequences occupied by chemically diverse ligands. Our analysis showed that similarities calculated using ECIFP can be used to compare macromolecular complexes with similar or different ligands. In this study, we demonstrate two large-scale applications of ECIFP: (1) identification of distinct binding modes for over 9,000 ligands across the entire PDB, and (2) detection of binding-mode similarities among structurally diverse ligands within the same binding site across 48,870 binding sites from over 21,000 proteins.

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.

The Protein Data Bank (PDB) is the primary global repository for experimentally determined 3D structures of biological macromolecules and their complexes with ligands, proteins, and nucleic acids. PDB contains over 47,000 unique small molecules bound to the macromolecules. Despite the extensive data available, the complexity of small‐molecule data in the PDB necessitates specialized tools for effective analysis and visualization. PDBe has developed a number of tools, including PDBe CCDUtils (https://github.com/PDBeurope/ccdutils) for accessing and enriching ligand data, PDBe Arpeggio (https://github.com/PDBeurope/arpeggio) for analyzing interactions between ligands and macromolecules, and PDBe RelLig (https://github.com/PDBeurope/rellig) for identifying the functional roles of ligands (such as reactants, cofactors, or drug‐like molecules) within protein–ligand complexes. The enhanced ligand annotations and data generated by these tools are presented on the novel PDBe‐KB ligand pages, offering a comprehensive overview of small molecules and providing valuable insights into their biological contexts (example page for Imatinib: https://pdbe.org/chem/sti). By improving the standardization of ligand identification, adding various annotations, and offering advanced visualization capabilities, these tools help researchers navigate the complexities of small molecules and their roles in biological systems, facilitating mechanistic understanding of biological functions. The ongoing enhancements to these resources are designed to support the scientific community in gaining valuable insights into ligands and their applications across various fields, including drug discovery, molecular biology, systems biology, structural biology, and pharmacology.

The Protein Data Bank (PDB) is the primary global repository for experimentally determined 3D structures of biological macromolecules and their complexes with ligands, proteins, and nucleic acids. PDB contains over 47,000 unique small molecules bound to the macromolecules. Despite the extensive data available, the complexity of small molecule data in the PDB necessitates specialised tools for effective analysis and visualisation. PDBe has developed a number of tools, including PDBe CCDUtils ( https://github.com/PDBeurope/ccdutils ) for accessing and enriching ligand data, PDBe Arpeggio ( https://github.com/PDBeurope/arpeggio ) for analysing interactions between ligands and macromolecules, and PDBe RelLig ( https://github.com/PDBeurope/rellig ) for identifying the functional roles of ligands (such as reactants, cofactors, or drug-like molecules) within protein-ligand complexes. Furthermore, the enhanced ligand annotations and data generated by these tools are presented in a comprehensive view on the novel PDBe-KB ligand pages, providing a holistic view of small molecules that enables the establishment of their biological contexts (Example page for Imatinib: https://wwwdev.ebi.ac.uk/pdbe-srv/pdbechem/chemicalCompound/show/STI ). By improving the standardisation of ligand identification, adding various annotations, and offering advanced visualisation capabilities, these tools help researchers navigate the complexities of small molecules and their roles in biological systems, facilitating mechanistic understanding of biological functions. The ongoing enhancements to these resources are designed to support the scientific community in gaining valuable insights into ligands and their applications across various fields, including drug discovery, molecular biology, systems biology, structural biology, and pharmacology.

In January 2020, a workshop was held at EMBL-EBI (Hinxton, UK) to discuss data requirements for the deposition and validation of cryoEM structures, with a focus on single-particle analysis. The meeting was attended by 47 experts in data processing, model building and refinement, validation, and archiving of such structures. This report describes the workshop's motivation and history, the topics discussed, and the resulting consensus recommendations. Some challenges for future methods-development efforts in this area are also highlighted, as is the implementation to date of some of the recommendations.

The Protein Data Bank (PDB) archive is the global repository for public-domain 3D biomolecular structural information. The archival nature of the PDB presents certain challenges pertaining to updating or adding associated annotations from trusted external biodata resources. While each Worldwide PDB (wwPDB) partner has made best efforts to provide up-to-date external annotations, accessing and integrating information from disparate wwPDB data centers can be an involved process. To address this issue, the wwPDB has established the PDB Next Generation or NextGen Archive, developed to centralize and streamline access to enriched structural annotations from wwPDB partners and trusted external sources. At present, the archive provides mappings between experimentally determined 3D structures of proteins and UniProt amino acid sequences, together with domain annotations from Pfam, SCOP2, and CATH databases, and intra-molecular connectivity information. Since launch, the PDB NextGen Archive has seen substantial user engagement with over 3.5 million data file downloads, ensuring researchers have access to accurate, up-to-date, and easily accessible structural annotations. Database URL: http://www.wwpdb.org/ftp/pdb-nextgen-archive-site

More than 61,000 proteins have up-to-date correspondence between their amino acid sequence (UniProtKB) and their 3D structures (PDB), enabled by the Structure Integration with Function, Taxonomy and Sequences (SIFTS) resource. SIFTS incorporates residue-level annotations from many other biological resources. SIFTS data is available in various formats like XML, CSV and TSV format or also accessible via the PDBe REST API but always maintained separately from the structure data (PDBx/mmCIF file) in the PDB archive. Here, we extended the wwPDB PDBx/mmCIF data dictionary with additional categories to accommodate SIFTS data and added the UniProtKB, Pfam, SCOP2, and CATH residue-level annotations directly into the PDBx/mmCIF files from the PDB archive. With the integrated UniProtKB annotations, these files now provide consistent numbering of residues in different PDB entries allowing easy comparison of structure models. The extended dictionary yields a more consistent, standardised metadata description without altering the core PDB information. This development enables up-to-date cross-reference information at the residue level resulting in better data interoperability, supporting improved data analysis and visualisation.