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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...

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Autores principales: Bruley, Apolline, Mornon, Jean-Paul, Duprat, Elodie, Callebaut, Isabelle
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
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.
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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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