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Digging into the 3D Structure Predictions of AlphaFold2 with Low Confidence: Disorder and Beyond
AlphaFold2 (AF2) has created a breakthrough in biology by providing three-dimensional structure models for whole-proteome sequences, with unprecedented levels of accuracy. In addition, the AF2 pLDDT score, related to the model confidence, has been shown to provide a good measure of residue-wise diso...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9599455/ https://www.ncbi.nlm.nih.gov/pubmed/36291675 http://dx.doi.org/10.3390/biom12101467 |
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author | Bruley, Apolline Mornon, Jean-Paul Duprat, Elodie Callebaut, Isabelle |
author_facet | Bruley, Apolline Mornon, Jean-Paul Duprat, Elodie Callebaut, Isabelle |
author_sort | Bruley, Apolline |
collection | PubMed |
description | AlphaFold2 (AF2) has created a breakthrough in biology by providing three-dimensional structure models for whole-proteome sequences, with unprecedented levels of accuracy. In addition, the AF2 pLDDT score, related to the model confidence, has been shown to provide a good measure of residue-wise disorder. Here, we combined AF2 predictions with pyHCA, a tool we previously developed to identify foldable segments and estimate their order/disorder ratio, from a single protein sequence. We focused our analysis on the AF2 predictions available for 21 reference proteomes (AFDB v1), in particular on their long foldable segments (>30 amino acids) that exhibit characteristics of soluble domains, as estimated by pyHCA. Among these segments, we provided a global analysis of those with very low pLDDT values along their entire length and compared their characteristics to those of segments with very high pLDDT values. We highlighted cases containing conditional order, as well as cases that could form well-folded structures but escape the AF2 prediction due to a shallow multiple sequence alignment and/or undocumented structure or fold. AF2 and pyHCA can therefore be advantageously combined to unravel cryptic structural features in whole proteomes and to refine predictions for different flavors of disorder. |
format | Online Article Text |
id | pubmed-9599455 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-95994552022-10-27 Digging into the 3D Structure Predictions of AlphaFold2 with Low Confidence: Disorder and Beyond Bruley, Apolline Mornon, Jean-Paul Duprat, Elodie Callebaut, Isabelle Biomolecules Article AlphaFold2 (AF2) has created a breakthrough in biology by providing three-dimensional structure models for whole-proteome sequences, with unprecedented levels of accuracy. In addition, the AF2 pLDDT score, related to the model confidence, has been shown to provide a good measure of residue-wise disorder. Here, we combined AF2 predictions with pyHCA, a tool we previously developed to identify foldable segments and estimate their order/disorder ratio, from a single protein sequence. We focused our analysis on the AF2 predictions available for 21 reference proteomes (AFDB v1), in particular on their long foldable segments (>30 amino acids) that exhibit characteristics of soluble domains, as estimated by pyHCA. Among these segments, we provided a global analysis of those with very low pLDDT values along their entire length and compared their characteristics to those of segments with very high pLDDT values. We highlighted cases containing conditional order, as well as cases that could form well-folded structures but escape the AF2 prediction due to a shallow multiple sequence alignment and/or undocumented structure or fold. AF2 and pyHCA can therefore be advantageously combined to unravel cryptic structural features in whole proteomes and to refine predictions for different flavors of disorder. MDPI 2022-10-13 /pmc/articles/PMC9599455/ /pubmed/36291675 http://dx.doi.org/10.3390/biom12101467 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Bruley, Apolline Mornon, Jean-Paul Duprat, Elodie Callebaut, Isabelle Digging into the 3D Structure Predictions of AlphaFold2 with Low Confidence: Disorder and Beyond |
title | Digging into the 3D Structure Predictions of AlphaFold2 with Low Confidence: Disorder and Beyond |
title_full | Digging into the 3D Structure Predictions of AlphaFold2 with Low Confidence: Disorder and Beyond |
title_fullStr | Digging into the 3D Structure Predictions of AlphaFold2 with Low Confidence: Disorder and Beyond |
title_full_unstemmed | Digging into the 3D Structure Predictions of AlphaFold2 with Low Confidence: Disorder and Beyond |
title_short | Digging into the 3D Structure Predictions of AlphaFold2 with Low Confidence: Disorder and Beyond |
title_sort | digging into the 3d structure predictions of alphafold2 with low confidence: disorder and beyond |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9599455/ https://www.ncbi.nlm.nih.gov/pubmed/36291675 http://dx.doi.org/10.3390/biom12101467 |
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