AlphaBridge: Tools for the analysis of predicted biomolecular complexes
- 1Oncode Institute and the Netherlands Cancer Institute Division of Biochemistry, Amsterdam 1066CX, the Netherlands
- 2Oncode Institute and the Netherlands Cancer Institute Division of Biochemistry, Amsterdam 1066CX, the Netherlands. Electronic address: [email protected]
Abstract
Artificial intelligence (AI)-powered protein structure prediction has transformed how scientists explore macromolecular function. AI-based prediction of macromolecular complexes is increasingly used for evaluating the likelihood of proteins forming complexes with other proteins, nucleic acids, lipids, sugars, or small-molecule ligands. Efficient tools are needed to evaluate these predicted models. We introduce an approach based on combining the confidence metrics of AlphaFold3 to enable clustering of sequence motifs participating in binary interactions and subsequently in 3D interfaces of complexes. Interaction interfaces within confidence limits are finally visualised in 2D using chord diagrams and network graphs. The analysis and visualisation are implemented in a web tool, which links them with interactive graphics and summary tables of predicted interfaces and intermolecular interactions, including confidence scores. Finally, we demonstrate real-life examples of how AlphaBridge is used for providing an efficient way to assess and validate predicted protein complexes and interfaces. The reproducible, objective and automated procedures we present provide a straightforward critical assessment of structure prediction of biomolecular complexes, that should be consulted before conducting more resource-intensive analyses.
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BibTeX
@article{salmoral2026alphabridge,
title = {{AlphaBridge: Tools for the analysis of predicted biomolecular complexes}},
author = {Daniel Álvarez Salmoral and Razvan Borza and Carmen Maiella and Bjørn P. Y. Kwee and Ren Xie and Robbie P. Joosten and Maarten L. Hekkelman and Anastassis Perrakis A},
journal = {Structure},
year = {2026},
month = sep,
doi = {10.1016/j.str.2026.08.011},
url = {https://doi.org/10.1016/j.str.2026.08.011},
note = {CC BY-NC},
}
RIS
TY - JOUR
TI - AlphaBridge: Tools for the analysis of predicted biomolecular complexes
AU - Daniel Álvarez Salmoral
AU - Razvan Borza
AU - Carmen Maiella
AU - Bjørn P. Y. Kwee
AU - Ren Xie
AU - Robbie P. Joosten
AU - Maarten L. Hekkelman
AU - Anastassis Perrakis A
PY - 2026
DA - 2026/09/01
JO - Structure
DO - 10.1016/j.str.2026.08.011
UR - https://doi.org/10.1016/j.str.2026.08.011
AB - Artificial intelligence (AI)-powered protein structure prediction has transformed how scientists explore macromolecular function. AI-based prediction of macromolecular complexes is increasingly used for evaluating the likelihood of proteins forming complexes with other proteins, nucleic acids, lipids, sugars, or small-molecule ligands. Efficient tools are needed to evaluate these predicted models. We introduce an approach based on combining the confidence metrics of AlphaFold3 to enable clustering of sequence motifs participating in binary interactions and subsequently in 3D interfaces of complexes. Interaction interfaces within confidence limits are finally visualised in 2D using chord diagrams and network graphs. The analysis and visualisation are implemented in a web tool, which links them with interactive graphics and summary tables of predicted interfaces and intermolecular interactions, including confidence scores. Finally, we demonstrate real-life examples of how AlphaBridge is used for providing an efficient way to assess and validate predicted protein complexes and interfaces. The reproducible, objective and automated procedures we present provide a straightforward critical assessment of structure prediction of biomolecular complexes, that should be consulted before conducting more resource-intensive analyses.
SN - 0969-2126
SN - 1878-4186
ER -