Structural Biology’s Data Format Crisis: Why PDB/mmCIF Can’t Keep Pace with the AI Era

The structural biology field faces an inflection point where its foundational data formats—designed for punch cards in 1972—cannot adequately represent AI-generated ensembles, petabyte-scale cryo-ET datasets, or dynamic molecular behavior. The 99,999 atom limit, parser fragmentation across dozens of incompatible implementations, and inability to encode conformational heterogeneity are blocking breakthrough applications. Meanwhile, other fields have solved analogous problems: genomics achieved interoperability through HTSlib and GA4GH governance, while microscopy embraced cloud-native OME-Zarr. Structural biology must adopt similar strategies—chunked cloud formats, ensemble-centric representations, and coordinated community governance—or risk becoming an isolated island in the multi-omics landscape.

The legacy burden: why everyone writes their own parser

The PDB format’s 80-column punch-card layout imposes hard constraints that break modern workflows. The maximum 99,999 atoms (5-character field) means large cryo-EM structures display asterisks (*****) that crash parsers. The 62-chain limit (single-character identifier) forces “split entries” where ribosomes were historically divided across multiple PDB IDs. Residue numbers cap at 9,999 per chain. These aren’t theoretical concerns—MDAnalysis GitHub issues document parsing failures when atom numbers overflow, and the BeEM converter paper notes that BioPython, cif-tools, and Atomium handle only single-character chain IDs.

Even mmCIF, designed to solve these limitations, creates new problems. The format contains two competing numbering systems—auth_seq_id (author-provided) versus label_seq_id (PDB-assigned)—that cause constant confusion. Chimera mailing lists document users puzzled when the same structure shows different residue numbers depending on which field the viewer reads. PyMOL defaults to auth_* fields while other tools use label_*, creating incompatible coordinate references for the same molecule.

Parser fragmentation is severe. The project-gemmi/mmcif-benchmark reveals dramatic performance differences: reading a 230MB file takes 7.8 seconds in Gemmi but causes out-of-memory crashes in BioPython and iotbx-pdb. BioPython itself maintains two separate parsers—MMCIFParser and FastMMCIFParser—because the “correct” parser is too slow while the fast one “doesn’t aim to parse mmCIF correctly.” GitHub issues across BioPython (#775, #481, #778, #1206), MDAnalysis (#446, #2422, #3473), Gemmi (#24, #118, #178), and Boltz (#451) document parsing failures ranging from missing atom serial numbers to crashes on alternate conformations to files generated by one tool being unreadable by another.

CryoET and AlphaFold strain every assumption

The data volume explosion is staggering. EMPIAR exceeds 2 petabytes with individual datasets reaching 70+ terabytes—taking weeks to transfer even with high-bandwidth connections. The Chan Zuckerberg CryoET Data Portal contains over 16,000 annotated tomograms, but annotations exist in “a wide variety of formats with varying forms and completeness of metadata,” making algorithm development and data reuse prohibitively difficult. A joint EBI/CZ Imaging Institute working group formed in April 2024 specifically to address this standardization gap.

AlphaFold’s 214 million predicted structures introduced new data types that don’t fit existing formats. The pLDDT confidence score gets repurposed into the B-factor field—but unlike B-factors, higher pLDDT is better, causing confusion in molecular replacement workflows. The Predicted Aligned Error (PAE) matrix, critical for multi-domain interpretation, requires separate JSON files with custom parsing. Multiple sequence alignments (.a3m files) essential for reproducibility exist outside the coordinate format entirely.

XFEL serial crystallography generates terabytes per hour, scaling to petabytes per day at next-generation facilities. European XFEL’s 1.1 MHz repetition rate produces hundreds of thousands of diffraction patterns hourly from crystals in random unknown orientations. Multi-panel detector geometries require complex non-standardized geometry files. Real-time processing must occur during collection—current formats weren’t designed for streaming data.

The biological context chasm between structure and function

Structural data lacks biological meaning. A critical finding: over 60% of proteins assigned enzymatic function in SwissProt have no active site residues identified in structural databases. The Catalytic Site Atlas covers only one reference structure per curated function and isn’t regularly updated. Mapping a variant from ClinVar to its structural context requires navigating multiple databases with incompatible identifiers.

Biological assembly annotations are surprisingly unreliable. Estimates suggest 10-15% of PDB entries contain incorrect or ambiguous biological assembly annotations. PISA algorithms often fail to correctly score heteromeric assemblies, particularly with small subunits. Crystal packing contacts get confused with biological interfaces. There’s no standardized naming convention, complicating computational analysis.

The wwPDB launched a major PTM remediation project (October 2024 – Spring 2025) acknowledging that post-translational modifications were historically handled inconsistently. A new pdbx_modification_feature category will provide instance-level PTM annotation, but this represents catching up rather than leading. Subcellular localization isn’t captured at all. Pathway context requires external database navigation. Proteoform-specific annotations are largely absent.

SIFTS (Structure Integration with Function, Taxonomy and Sequences) provides the crucial bridge, mapping PDB residues to UniProtKB sequences weekly. Recent advances embedded SIFTS annotations directly into mmCIF files and expanded coverage 40-fold to 1.8+ million UniProtKB sequences through UniRef90. The PDBe knowledge graph contains over 1 billion nodes and 1.5 billion edges integrating 30+ partner resources. But these remain external layers rather than intrinsic to the data format.

How genomics and imaging solved similar problems

HTSlib’s success offers a template. Downloaded over 1 million times and used by 900+ GitHub projects, this reference implementation made BAM/CRAM/VCF formats practical. Key design decisions: simple human-readable text format (SAM) paired with efficient binary (BAM), strong bundled reference implementation, indexability for random region access, extensibility via optional tags, and MIT/BSD licensing enabling commercial adoption. The format evolved—CRAM achieved 40-60% smaller files through reference-based compression while maintaining backward compatibility.

The GA4GH governance model provides organizational structure. Eight Work Streams and 24 Driver Projects engage over 1,000 individuals from 90+ countries and 650+ organizations. Standards emerge from real implementer needs through Study Groups, get formalized in Work Streams, and pilot through the Global Implementers Forum. This creates buy-in and ensures practical utility.

OME-Zarr demonstrates cloud-native design done right. Built for object storage from inception, it uses chunked N-dimensional arrays where each chunk is an independent file. Multi-resolution pyramids are built in. Hierarchical JSON metadata sits at each level. Implementations exist in Python, Java, JavaScript, C++, Rust, and Julia. The sharding innovation in Zarr v3 groups multiple chunks per object to handle filesystem limits while maintaining parallelism. Latency advantages are fundamental: monolithic formats require multiple round-trips while Zarr chunks are independent.

The critical lesson: reference implementation quality determines adoption. Formats without good libraries don’t get used regardless of theoretical elegance. Community governance through neutral international consortia builds trust. Cloud-native chunked formats are essential for object storage. Extensibility via optional fields enables evolution without breaking existing tools.

What would transform the field: ensemble-native, AI-ready infrastructure

A 2024 paper in Acta Crystallographica D states the core problem directly: “Although new tools are available to detect conformational and compositional heterogeneity within these ensembles, the legacy PDB data structure does not robustly encapsulate this complexity.” Current formats encode single conformational states despite biomolecules existing in dynamic ensembles. Intrinsically disordered proteins populate conformational ranges best described by heterogeneous ensembles—features “notoriously difficult to characterize” because they’re “lost by ensemble methods of structural characterization.”

AI-native format requirements are now well-understood: - E(3)/SE(3) equivariance preservation under geometric transformations - Native graph representations with k-nearest neighbor graphs, edge type annotations, and node features for GNNs like GearNet and DeepRank-GNN - Multi-resolution tokenization (AlphaFold 3 uses flexible schemes: standard residues as single tokens, modified residues as atoms, ligands as individual atoms) - Pre-computed MSA embeddings as feature channels - Confidence metrics (pLDDT, PAE) as first-class data alongside coordinates - Versioned model parameters linking predictions to the networks that generated them

Real-time streaming for time-resolved experiments requires time-indexed 4D coordinate trajectories, native support for incomplete/sparse datasets, and streaming formats compatible with Apache Kafka/Spark. BioCARS achieves time resolutions from 100 picoseconds to seconds; mix-and-inject serial crystallography captures enzyme catalysis with 2-7 millisecond temporal resolution. These generate data requiring storage paradigms beyond static files.

Ensemble representation extensions should include conformer population weights as first-class metadata, state transition matrices linking related conformations, per-coordinate uncertainty quantification, and links between experimental observables and ensemble statistics. The Protein Ensemble Database (PED) demonstrates what structured IDP ensemble metadata looks like—conformer counts, modeling resolution levels, validation status—but this remains isolated from mainstream structural formats.

The multi-omics integration imperative

The disconnect is stark: each omics type has distinct data structures, distributions, and batch effects. There are no standardized preprocessing protocols across omics layers. Programs require non-standard inputs and output incompatible formats, forcing researchers to write conversion scripts for every integration.

Tools like GLUE use graph variational autoencoders to anchor features using prior biological knowledge. MOFA+ and iCluster map multi-omics onto shared representations. But structural data sits outside these frameworks. A complete structural-omics pipeline—raw cryo-EM through single-particle analysis, subunit fitting, mass spectrometry validation, multi-omics annotation, integrative modeling via IMP or HADDOCK, to validated assembly—currently requires crossing multiple format boundaries with manual intervention at each step.

The PDBe knowledge graph represents the most ambitious current integration, but accessing biological context still requires navigating multiple APIs and external resources. For structural data to participate in systems biology, format-level integration points must exist: knowledge graphs linking structural data to genomic variants (AlphaMissense), protein modifications, interaction networks, and pathway annotations as native data channels rather than external lookups.

Toward next-generation structural data infrastructure

Near-term priorities (1-5 years) should include mandatory PDBx/mmCIF adoption before four-character PDB IDs exhaust around 2028, standardized cryo-EM interchange eliminating the cryoSPARC/RELION conversion mess, ensemble mmCIF extensions implementing proposed conformational heterogeneity categories, and GraphML/JSON-Graph exports providing ML-ready graph representations from coordinate files.

Medium-term developments (5-15 years) require streaming data standards with time-indexed 4D structural formats, multi-omics structural schemas linking to genomic/proteomic annotations, federated structural databases enabling distributed queries across PDB/EMDB/BMRB/PED, and AI model versioning tracking which networks produced which predictions.

Cloud infrastructure exists but needs structural biology adaptation. SBGrid’s SBCloud provides Slurm-based clusters with 620+ curated structural biology applications through Open OnDemand browser interfaces. Provenance tracking exists in specialized systems like PDB-REDO Cloud with detailed records documenting input versioning and program versions. The technical foundations are present—PDBx/mmCIF extensibility, cloud infrastructure, graph neural network frameworks, FAIR principles. The challenge is coordinated community adoption.

Conclusion

The wwPDB PDBx/mmCIF Working Group faces a forcing function: the 2028 extended PDB ID transition will require universal format migration regardless. This creates an opportunity to implement ensemble representation extensions, AI-native features, and cloud-optimized chunked storage simultaneously rather than incrementally. The alternative—continuing with formats designed for punch cards while the rest of biology moves to cloud-native, AI-ready infrastructure—risks structural biology becoming isolated from the integrated multi-omics future. Other fields have demonstrated the path: strong reference implementations, neutral governance bodies, extensible formats, and cloud-native architecture. The question is whether structural biology will follow before the format limitations become the binding constraint on discovery.