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Folding the unfoldable: using AlphaFold to explore spurious proteins

MOTIVATION: The release of AlphaFold 2.0 has revolutionized our ability to determine protein structures from sequences. This tool also inadvertently opens up many unanticipated opportunities. In this article, we investigate the AntiFam resource, which contains 250 protein sequence families that we b...

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Autores principales: Monzon, Vivian, Haft, Daniel H, Bateman, Alex
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9710616/
https://www.ncbi.nlm.nih.gov/pubmed/36699409
http://dx.doi.org/10.1093/bioadv/vbab043
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author Monzon, Vivian
Haft, Daniel H
Bateman, Alex
author_facet Monzon, Vivian
Haft, Daniel H
Bateman, Alex
author_sort Monzon, Vivian
collection PubMed
description MOTIVATION: The release of AlphaFold 2.0 has revolutionized our ability to determine protein structures from sequences. This tool also inadvertently opens up many unanticipated opportunities. In this article, we investigate the AntiFam resource, which contains 250 protein sequence families that we believe to be spurious protein translations. We would not expect proteins belonging to these families to fold into well-ordered globular structures. To test this hypothesis, we have attempted to computationally determine the structure of a representative sequence from all AntiFam 6.0 families. RESULTS: Although the large majority of families showed no evidence of globular structure, we have identified one example for which a globular structure is predicted. Proteins in this AntiFam entry indeed seem likely to be bona fide proteins, based on additional considerations, and thus AlphaFold provides a useful quality control for the AntiFam database. Conversely, known spurious proteins offer useful set of quality controls for AlphaFold. We have identified a trend that the mean structure prediction confidence score pLDDT is higher for shorter sequences. Of the 131 AntiFam representative sequences <100 amino acids in length, AlphaFold predicts a mean pLDDT of 80 or greater for six of them. Thus, particular care should be taken when applying AlphaFold to short protein sequences. AVAILABILITY AND IMPLEMENTATION: The AlphaFold predictions for representative sequences can be found at the following URL: https://drive.google.com/drive/folders/1u9OocRIAabGQn56GljoG1JTDAxjkY1ro. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics Advances online.
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spelling pubmed-97106162023-01-24 Folding the unfoldable: using AlphaFold to explore spurious proteins Monzon, Vivian Haft, Daniel H Bateman, Alex Bioinform Adv Discovery Note MOTIVATION: The release of AlphaFold 2.0 has revolutionized our ability to determine protein structures from sequences. This tool also inadvertently opens up many unanticipated opportunities. In this article, we investigate the AntiFam resource, which contains 250 protein sequence families that we believe to be spurious protein translations. We would not expect proteins belonging to these families to fold into well-ordered globular structures. To test this hypothesis, we have attempted to computationally determine the structure of a representative sequence from all AntiFam 6.0 families. RESULTS: Although the large majority of families showed no evidence of globular structure, we have identified one example for which a globular structure is predicted. Proteins in this AntiFam entry indeed seem likely to be bona fide proteins, based on additional considerations, and thus AlphaFold provides a useful quality control for the AntiFam database. Conversely, known spurious proteins offer useful set of quality controls for AlphaFold. We have identified a trend that the mean structure prediction confidence score pLDDT is higher for shorter sequences. Of the 131 AntiFam representative sequences <100 amino acids in length, AlphaFold predicts a mean pLDDT of 80 or greater for six of them. Thus, particular care should be taken when applying AlphaFold to short protein sequences. AVAILABILITY AND IMPLEMENTATION: The AlphaFold predictions for representative sequences can be found at the following URL: https://drive.google.com/drive/folders/1u9OocRIAabGQn56GljoG1JTDAxjkY1ro. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics Advances online. Oxford University Press 2022-01-09 /pmc/articles/PMC9710616/ /pubmed/36699409 http://dx.doi.org/10.1093/bioadv/vbab043 Text en © The Author(s) 2022. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Discovery Note
Monzon, Vivian
Haft, Daniel H
Bateman, Alex
Folding the unfoldable: using AlphaFold to explore spurious proteins
title Folding the unfoldable: using AlphaFold to explore spurious proteins
title_full Folding the unfoldable: using AlphaFold to explore spurious proteins
title_fullStr Folding the unfoldable: using AlphaFold to explore spurious proteins
title_full_unstemmed Folding the unfoldable: using AlphaFold to explore spurious proteins
title_short Folding the unfoldable: using AlphaFold to explore spurious proteins
title_sort folding the unfoldable: using alphafold to explore spurious proteins
topic Discovery Note
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9710616/
https://www.ncbi.nlm.nih.gov/pubmed/36699409
http://dx.doi.org/10.1093/bioadv/vbab043
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