Structural Bioinformatics is a foundational scientific domain of ELIXIR CZ, providing essential analytical methods, curated datasets, and computational tools for understanding the structure, dynamics, and function of biomacromolecules. As structural biology rapidly evolves—from atomic-resolution experiments to AI-based prediction and large-scale ensemble modelling—this area underpins a wide range of research fields including protein engineering, drug discovery, molecular simulations, and systems-level structural analysis.
The strategic relevance of Structural Bioinformatics is amplified by recent breakthroughs such as AlphaFold, RoseTTAFold, and generative modelling, which have transformed access to predicted structures but simultaneously introduced new demands for validation, refinement, visualization, and high-quality reference datasets. At the European level, the field is central to the ELIXIR Platforms (particularly Data, Compute, and Interoperability) and benefits from close links with the 3D-Beacons initiative, PDBe, and communities focusing on protein engineering, enzymology, and AI/ML in structural biology.
ELIXIR CZ is uniquely positioned to contribute to these European activities by combining long-standing expertise in structural analysis, advanced visualization tools, large curated datasets, and the development of predictive methods for protein stability, solubility, and interactions. Structural Bioinformatics remains one of the strongest scientific pillars of ELIXIR CZ, with internationally recognised tools and on scientific roadmap for the 2026–2030 period.
Current Strengths
ELIXIR CZ has a well-established track record in the development of structural bioinformatics tools, databases, and algorithms that are widely used across Europe and globally. Tools such as MOL*, MOLE, MOLEonline, Caver, CaverDock, AtomicChargeCalculator, PrankWeb, and DNATCO form a robust ecosystem for analysing channels, charges, pockets, protein–ligand interactions, nucleic acid conformations, and biomolecular geometry. These tools are complemented by high-quality curated datasets, including DATMOS, nucleic acid reference sets, AlphaCharges, PDBCharges, curated partial atomic charges, ChannelsDB, curated channels in proteins and datasets used for training and validating AI/ML models – e.g. AHOJ-DB for apo/holo structures.
ELIXIR CZ is a leader in protein engineering, with methods such as FireProt, FireProt-ASR, HotSpot Wizard, SoluProt, AggreProt, LoopGrafter, and BenchStab enabling rational design of protein stability, solubility, aggregation propensity, and smart mutational libraries. These methods are supported by deep expertise in molecular simulations, large-scale analysis of structural ensembles, and data-driven prediction of molecular properties tested in experimental validation.
A strong additional asset is the development of advanced visualization platforms, particularly the contributions to Mol* and 2DProts, enabling representation of large biomacromolecules, ensembles, and complex molecular assemblies. These strengths position the ELIXIR CZ as an important European actor in structural data analysis, tool development, and the interpretation of large-scale AI-generated structural resources in ELIXIR core resources – PDBe, PDBe-KB, AlphafoldDB, and CATH.
New Directions & Challenges
The coming years bring an unprecedented transformation in structural bioinformatics, driven by the explosion of AI-generated models, rapid advances in cryo-electron microscopy, and the increasing integration of structural data with multi-omics, chemical biology, and molecular dynamics. These developments introduce opportunities that require new technical capabilities, new workflows, and new standards.
- A major challenge is the validation and refinement of AI-predicted structures. While tools like AlphaFold and RoseTTAFold provide accurate global folds, they often generate local artefacts in side-chain placement, loop geometry, nucleic acid conformations, and ligand stereochemistry. ELIXIR CZ is planning therefore to develop new pipelines for structural quality assessment, error detection, automated correction, and local refinement to ensure that predicted structures can be used reliably in downstream modelling, AI training, and experimental design.
- Another key challenge is the increasing scale and complexity of structural datasets. Cryo-EM tomography, integrative modelling, and simulation-based ensemble approaches now produce structures at the scale of organelles, cells, and tissues. These datasets demand next-generation visualization and analysis tools capable of interactive navigation through terabyte-scale structural landscapes, integration of structural ensembles, and exploration of dynamic properties. This represents a frontier area where only few ELIXIR nodes have established solutions, positioning ELIXIR CZ to take a pioneering role.
- A further challenge is the creation of high-quality, AI-ready reference datasets. The performance of emerging AI systems relies critically on curated training data with well-defined ground truth. These include partial atomic charges, protonation states, pockets, tunnels, nucleic-acid geometries, mutational effects, and dynamical parameters derived from simulations. The curation of such datasets requires sustained expert effort and careful standardisation, yet represents a strategic asset of increasing importance for the European structural bioinformatics community.
- Searching, clustering, and indexing growing collections of structural models—including full proteomes, conformational ensembles, molecular dynamics and generative-AI structural libraries—pose additional computational challenges. ELIXIR CZ thrives to develop methods for efficient navigation of ultra-large structural spaces, including fast similarity search, fragment-based structural comparison, and functional annotation using AI.
- Finally, Structural Bioinformatics must respond to emerging integration challenges, where structural data increasingly intersect with genomics, chemical biology, human data, and large-scale simulations. This requires interoperability with ELIXIR platforms, standardized metadata schemas, and workflows that link structure, sequence, variant effects, chemical properties, and functional annotations. Ensuring interoperability between ELIXIR CZ tools and the European 3D-Beacons network, PDBe, and Galaxy and Nextflow workflows will be essential for long-term sustainability.
These new directions—AI validation, ensemble-scale visualization, curated AI-training datasets, structural searchability, and cross-domain integration—define the scientific frontier where ELIXIR CZ can offer significant European leadership in the 2026–2030 period.
