AlphaFill: enriching AlphaFold models with ligands and cofactors

Artificial intelligence-based protein structure prediction approaches have had a transformative effect on biomolecular sciences. The predicted protein models in the AlphaFold protein structure database, however, all lack coordinates for small molecules, essential for molecular structure or function: hemoglobin lacks bound heme; zinc-finger motifs lack zinc ions essential for structural integrity and metalloproteases lack metal ions needed for catalysis. Ligands important for biological function are absent too; no ADP or ATP is bound to any of the ATPases or kinases. Here we present AlphaFill, an algorithm that uses sequence and structure similarity to 'transplant' such 'missing' small molecules and ions from experimentally determined structures to predicted protein models. The algorithm was successfully validated against experimental structures. A total of 12,029,789 transplants were performed on 995,411 AlphaFold models and are available together with associated validation metrics in the alphafill.eu databank, a resource to help scientists make new hypotheses and design targeted experiments. The AlphaFill algorithm transplants missing small molecules and ions from experimentally determined structures to predicted protein models in the AlphaFold protein structure database. All AlphaFill entries are available for visual inspection and download through the AlphaFill website.

Automated summary

Artificial intelligence-based protein structure prediction has greatly impacted biomolecular sciences. However, the predicted protein models in the AlphaFold database lack coordinates for small molecules and ions necessary for structure and function. The AlphaFill algorithm addresses this issue by transplanting missing small molecules and ions from experimentally determined structures to predicted protein models.