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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:...

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Autores principales: Hekkelman, Maarten L., de Vries, Ida, Joosten, Robbie P., Perrakis, Anastassis
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group US 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9911346/
https://www.ncbi.nlm.nih.gov/pubmed/36424442
http://dx.doi.org/10.1038/s41592-022-01685-y
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author Hekkelman, Maarten L.
de Vries, Ida
Joosten, Robbie P.
Perrakis, Anastassis
author_facet Hekkelman, Maarten L.
de Vries, Ida
Joosten, Robbie P.
Perrakis, Anastassis
author_sort Hekkelman, Maarten L.
collection PubMed
description 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.
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spelling pubmed-99113462023-02-11 AlphaFill: enriching AlphaFold models with ligands and cofactors Hekkelman, Maarten L. de Vries, Ida Joosten, Robbie P. Perrakis, Anastassis Nat Methods Resource 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. Nature Publishing Group US 2022-11-24 2023 /pmc/articles/PMC9911346/ /pubmed/36424442 http://dx.doi.org/10.1038/s41592-022-01685-y Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Resource
Hekkelman, Maarten L.
de Vries, Ida
Joosten, Robbie P.
Perrakis, Anastassis
AlphaFill: enriching AlphaFold models with ligands and cofactors
title AlphaFill: enriching AlphaFold models with ligands and cofactors
title_full AlphaFill: enriching AlphaFold models with ligands and cofactors
title_fullStr AlphaFill: enriching AlphaFold models with ligands and cofactors
title_full_unstemmed AlphaFill: enriching AlphaFold models with ligands and cofactors
title_short AlphaFill: enriching AlphaFold models with ligands and cofactors
title_sort alphafill: enriching alphafold models with ligands and cofactors
topic Resource
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9911346/
https://www.ncbi.nlm.nih.gov/pubmed/36424442
http://dx.doi.org/10.1038/s41592-022-01685-y
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