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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:...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group US
2022
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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. |
format | Online Article Text |
id | pubmed-9911346 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group US |
record_format | MEDLINE/PubMed |
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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