ColabFold - Making protein folding accessible to all

Mirdita, Milot; Schütze, Konstantin; Moriwaki, Yoshitaka; Heo, Lim; Овчинников, С. Г.; Steinegger, Martin

doi:10.1101/2021.08.15.456425

Cited by 741 publications

(817 citation statements)

References 41 publications

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“…Despite the varying quality of the new structures, SWISS-MODEL 10 has already installed the code from AlphaFold to complement its existing structure prediction pipelines, while other groups have added the models to their databases of protein information, for example UniProt 11 and PDBsum 12 . ColabFold 13 provides tools for modeling multi-chain homo-and hetero-complexes using the AlphaFold and also RoseTTAFold models 14 . Another use of the models is in the interpretation of low-resolution electron microscopy data, especially where the protein shows flexibility between domains.…”

Section: A New Structure Prediction Pipelinementioning

confidence: 99%

AlphaFold heralds a data-driven revolution in biology and medicine

Thornton

Laskowski

Borkakoti

2021

Nat Med

142

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Section: A New Structure Prediction Pipelinementioning

confidence: 99%

AlphaFold heralds a data-driven revolution in biology and medicine

Thornton

Laskowski

Borkakoti

2021

Nat Med

142

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“…As this manuscript is being completed rapid advances are being made in adapting and extending AlphaFold2 to allow it to model homomeric and heteromeric protein complexes (e.g. [35][36][37]). It seems likely, therefore, that in the near future the same strategy reported here might be used to identify ligand binding sites that lie at the interface between separate polypeptide chains.…”

Section: Discussionmentioning

confidence: 99%

Identification of Iron-Sulfur (Fe-S) and Zn-binding Sites Within Proteomes Predicted by DeepMind’s AlphaFold2 Program Dramatically Expands the Metalloproteome

Wehrspan

McDonnell

Elcock

2021

Preprint

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DeepMind′s AlphaFold2 software has ushered in a revolution in high quality, 3D protein structure prediction. In very recent work by the DeepMind team, structure predictions have been made for entire proteomes of twenty-one organisms, with >360,000 structures made available for download. Here we show that thousands of novel binding sites for iron-sulfur (Fe-S) clusters and zinc ions can be identified within these predicted structures by exhaustive enumeration of all potential ligand-binding orientations. We demonstrate that AlphaFold2 routinely makes highly specific predictions of ligand binding sites: for example, binding sites that are comprised exclusively of four cysteine sidechains fall into three clusters, representing binding sites for 4Fe-4S clusters, 2Fe-2S clusters, or individual Zn ions. We show further: (a) that the majority of known Fe-S cluster and Zn-binding sites documented in UniProt are recovered by the AlphaFold2 structures, (b) that there are occasional disputes between AlphaFold2 and UniProt with AlphaFold2 predicting highly plausible alternative binding sites, (c) that the Fe-S cluster binding sites that we identify in E. coli agree well with previous bioinformatics predictions, (d) that cysteines predicted here to be part of Fe-S cluster or Zn-binding sites show little overlap with those shown via chemoproteomics techniques to be highly reactive, and (e) that AlphaFold2 occasionally appears to build erroneous disulfide bonds between cysteines that should instead coordinate a ligand. These results suggest that AlphaFold2 could be an important tool for the functional annotation of proteomes, and the methodology presented here is likely to be useful for predicting other ligand-binding sites.

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“…All AlphaFold models were obtained from the EMBL EBI database (https://alphafold.ebi.ac.uk/) for each available model organism. For any searches where existing models were not available from the PDB, these were generated using AlphaFold hosted through ColabFold 29 . A regex search of PDB metadata for ‘uncharacterised’ was used to curate a subset of uncharacterised or unknown proteins in the human foldome.…”

Section: Methodsmentioning

confidence: 99%

Mining folded proteomes in the era of accurate structure prediction

Bayly-Jones

Whisstock

2021

Preprint

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Protein structure fundamentally underpins the function and processes of numerous biological systems. Fold recognition algorithms offer a sensitive and robust tool to detect structural, and thereby functional, similarities between distantly related homologs. In the era of accurate structure prediction owing to advances in machine learning techniques, previously curated sequence databases have become a rich source of biological information. Here, we use bioinformatic fold recognition algorithms to scan the entire AlphaFold structure database to identify novel protein family members, infer function and group predicted protein structures. As an example of the utility of this approach, we identify novel, previously unknown members of various pore-forming protein families, including MACPFs, GSDMs and aerolysin-like proteins. Further, we explore the use of structure-based mining for functional inference.

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ColabFold - Making protein folding accessible to all

Cited by 741 publications

References 41 publications

AlphaFold heralds a data-driven revolution in biology and medicine

AlphaFold heralds a data-driven revolution in biology and medicine

Identification of Iron-Sulfur (Fe-S) and Zn-binding Sites Within Proteomes Predicted by DeepMind’s AlphaFold2 Program Dramatically Expands the Metalloproteome

Mining folded proteomes in the era of accurate structure prediction

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