Recent breakthroughs in artificial intelligence have transformed protein structure biology from a data-limited discipline into a data-driven frontier of biomolecular science. AI models such as AlphaFold, RoseTTAFold, ESMFold, and their generative successors such as RFdiffusion, ProteinMPNN, Foldseek, BioEmu, etc, now enable near-experimental accuracy in faster prediction of protein structures, elucidating complex assemblies, and designing entirely novel folds and complexes.

This Research Topic emphasizes the latest advancements in the area of structural biology – studies on biomolecular structures, functions, evolution, interactions, and self-assembly encompassing AI-driven approaches, as well as classical methods, while exploring future directions in the field. Contributions explore not only the application of AI to structure prediction, but also its role in functional annotation, protein–ligand interaction modelling, and de novo therapeutic design.

While advanced methods are continuously emerging in the field, protein dynamics, conformation changes, highly flexible regions predictions, and structural characteristics of IDPs are certain areas that the present state of the art techniques still face difficulty in handling and hence limit accurate structure/assembly and affinity predictions. This article collection aims to capture the rapidly evolving landscape where computational intelligence and experimental innovation converge to accelerate discovery in structural and synthetic biology.

In this Research Topic, we seek original and novel findings in the annotation and functional interpretation of protein structures and their complexes with biomolecules, including ligands, nucleic acids, and other proteins and peptides. We welcome studies on binder and affinity prediction, binding interface characterization, and structure–function relationships, with an emphasis on emerging technologies, particularly AI-driven methods such as deep learning, generative modelling, and integrative structural approaches. Both experimental and computational research are encouraged. Resource articles presenting curated datasets, benchmark tools, and open-source software are also invited. The aim is to highlight innovative approaches that accelerate discovery and engineering in protein science.

Potential themes include, but are not limited to:

• Modeling bio-macromolecules – proteins, nucleic acids, and their complexes

• Dynamics of bio-macromolecules

• Protein evolution

• Protein engineering

• Structural bioinformatics

• Drug discovery and design

• Structural systems biology

• Protein phase separation and conformation

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Advances in Protein Structure Biology – Use of AI and Beyond