We welcome studies using X-ray crystallography, cryo-electron microscopy, NMR spectroscopy, SAXS, and integrative or hybrid modeling.

Interdisciplinary research connecting structure to mechanism, dynamics, and biological function is strongly encouraged.

Manuscripts with clear methodology, reproducible data practices, and transparent validation receive priority for efficient review.

Research that links structural insights to drug discovery, protein engineering, or molecular medicine is especially valued.

We encourage submissions on membrane proteins, macromolecular complexes, ribosomes, viral structures, and large assemblies.

Computational modeling, AI driven structure prediction, and molecular dynamics are welcome when methods are rigorous and validated.

We value studies that integrate experimental data with computational interpretation to explain biological mechanisms.

Reports that clarify conformational changes, ligand binding, or catalytic mechanisms are highly relevant to the journal scope.

Case studies that improve structure determination pipelines or data processing workflows are encouraged.

Replication or benchmarking studies that refine structural methods are considered when methods are rigorous.

All submissions should include clear reporting of datasets, validation metrics, and repository identifiers for reproducibility.

We welcome submissions on protein and nucleic acid structure determination.

Studies on cryo-EM data processing, map refinement, and validation are within scope.

Research on X-ray crystallography, phasing methods, and refinement workflows is encouraged.

NMR structure determination, restraints analysis, and dynamics studies are a strong fit.

Submissions on membrane proteins and large macromolecular complexes are welcome.

We encourage integrative and hybrid modeling that combines multiple data types.

Structure based drug design and ligand binding studies are prioritized.

Computational modeling and AI driven structure prediction are considered with validation.

Case studies that improve data pipelines, automation, or reproducibility are encouraged.

Method papers that benchmark validation metrics are welcome.

Laboratory studies should include calibration details and reproducible protocols.

Systematic reviews and meta analyses on structural methods are welcomed with clear protocols.

We invite work on structural genomics and high throughput pipelines.