PubReading [257] - AlphaFill- enriching AlphaFold models with ligands and cofactors - M. Hekkelman, A. Perrakis et al.

<p>Artificial intelligence-based <strong>protein structure prediction</strong> approaches have had a transformative effect on biomolecular sciences. The predicted protein models in the <strong>AlphaFold</strong> 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 <strong>AlphaFill</strong>, 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 <strong>algorithm</strong> 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 <a href="http://alphafill.eu">alphafill.eu</a> databank, a resource to help scientists make new hypotheses and design targeted experiments.</p><p><a href="https://doi.org/10.1038/s41592-022-01685-y"><em>https://doi.org/10.1038/s41592-022-01685-y</em></a><em> - 2022</em></p>