The rapid growth of experimentally determined and AI-predicted macromolecular structures has shifted a central challenge in structural biology from data generation to scalable, standardised annotation, validation, and integration. PDBe-KB provides an important foundation for this work by aggregating expert annotations and placing structural data in biological context.

Within an ongoing ELIXIR Implementation Study spanning the 3D-BioInfo community, the Nucleic Acid Structure Annotation & Prediction Hackathon 2026 was held in Prague on 27–29 May 2026. The event brought together domain experts, software developers, data-resource contributors, and early-career researchers to work on practical, reusable outputs for nucleic-acid structural bioinformatics.

The hackathon focused on two connected areas: interoperable workflows for nucleic-acid structure annotation and validation, and improved representation and visualization of Watson–Crick and non-Watson–Crick base-pair interactions. Participants adapted existing tools into reusable workflow components, tested shared benchmark cases, compared annotation routes, and developed documentation, specifications, and prototype implementations. Outputs included a modular Nextflow-based workflow structure for annotation and validation from mmCIF input, routes for standardised base-pairing outputs and validation reports, exploratory graph-based downstream representations, and linked 2D/3D visualization prototypes connecting base-pair notation, secondary-structure diagrams, and molecular structures.

The resulting outputs were structured with future PDBe-compatible dissemination and integration in mind, using standardised and provenance-aware formats where possible, including mmCIF-based representations. While further refinement and validation are required before production use, the hackathon produced concrete reusable components, examples, and follow-up directions for the wider community.

This work illustrates how focused, domain-specific hackathons can strengthen cross-community collaboration within ELIXIR and provide a practical model for transforming distributed expertise into sustainable, reusable infrastructure for nucleic-acid structure annotation, validation, and visualization.

The ELIXIR IMPACT project addresses key challenges limiting the effective use of biological and health data in biomedical research, personalised medicine, and public health. Despite rapid advances in artificial intelligence (AI), scientific progress is often hindered by fragmented data resources, limited interoperability, and restricted access to sensitive clinical information. Building on the complementary expertise of ELIXIR Czech Republic and ELIXIR Switzerland, the project establishes a strategic collaboration to strengthen the integration, accessibility, and secure use of biological and health-related data across Europe.
The project focuses on five main objectives: (i) improving interoperability of molecular, lipid, metabolite, and pathogen data through semantic integration and AI-powered knowledge graphs; (ii) developing innovative AI-based tools and natural language interfaces to enhance data discovery and usability; (iii) creating secure and scalable infrastructures for cross-border sharing and analysis of sensitive omics and clinical data in compliance with European regulatory frameworks; (iv) expanding research capacities through targeted training, workshops, and educational resources; and (v) reinforcing long-term Czech–Swiss scientific cooperation within the ELIXIR ecosystem by leveraging complementary expertise and infrastructure.
By combining internationally recognised data resources, advanced bioinformatics expertise, and state-of-the-art AI methodologies, ELIXIR IMPACT will accelerate biological discoveries and support data-driven healthcare innovation. The project is aligned with European initiatives such as EOSC and will contribute to strengthening Europe’s leadership in biological data management, AI-enabled knowledge integration, and secure data sharing. Its outcomes will benefit thousands of researchers, clinicians, and data scientists, ultimately improving disease research, public health preparedness, and personalised healthcare.