The Epigenetic Dimension of Protein Structure Is an Intrinsic Weakness of the AlphaFold Program

One of the most important lessons we have learned from sequencing the human genome is that not all proteins have a 3D structure. In fact, a large part of the human proteome is made up of intrinsically disordered proteins (IDPs) which can adopt multiple structures, and therefore, multiple functions,...

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Autores principales: Azzaz, Fodil, Yahi, Nouara, Chahinian, Henri, Fantini, Jacques
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9599222/
https://www.ncbi.nlm.nih.gov/pubmed/36291736
http://dx.doi.org/10.3390/biom12101527
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author Azzaz, Fodil
Yahi, Nouara
Chahinian, Henri
Fantini, Jacques
author_facet Azzaz, Fodil
Yahi, Nouara
Chahinian, Henri
Fantini, Jacques
author_sort Azzaz, Fodil
collection PubMed
description One of the most important lessons we have learned from sequencing the human genome is that not all proteins have a 3D structure. In fact, a large part of the human proteome is made up of intrinsically disordered proteins (IDPs) which can adopt multiple structures, and therefore, multiple functions, depending on the ligands with which they interact. Under these conditions, one can wonder about the value of algorithms developed for predicting the structure of proteins, in particular AlphaFold, an AI which claims to have solved the problem of protein structure. In a recent study, we highlighted a particular weakness of AlphaFold for membrane proteins. Based on this observation, we have proposed a paradigm, referred to as “Epigenetic Dimension of Protein Structure” (EDPS), which takes into account all environmental parameters that control the structure of a protein beyond the amino acid sequence (hence “epigenetic”). In this new study, we compare the reliability of the AlphaFold and Robetta algorithms’ predictions for a new set of membrane proteins involved in human pathologies. We found that Robetta was generally more accurate than AlphaFold for ascribing a membrane-compatible topology. Raft lipids (e.g., gangliosides), which control the structural dynamics of membrane protein structure through chaperone effects, were identified as major actors of the EDPS paradigm. We conclude that the epigenetic dimension of a protein structure is an intrinsic weakness of AI-based protein structure prediction, especially AlphaFold, which warrants further development.
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spelling pubmed-95992222022-10-27 The Epigenetic Dimension of Protein Structure Is an Intrinsic Weakness of the AlphaFold Program Azzaz, Fodil Yahi, Nouara Chahinian, Henri Fantini, Jacques Biomolecules Article One of the most important lessons we have learned from sequencing the human genome is that not all proteins have a 3D structure. In fact, a large part of the human proteome is made up of intrinsically disordered proteins (IDPs) which can adopt multiple structures, and therefore, multiple functions, depending on the ligands with which they interact. Under these conditions, one can wonder about the value of algorithms developed for predicting the structure of proteins, in particular AlphaFold, an AI which claims to have solved the problem of protein structure. In a recent study, we highlighted a particular weakness of AlphaFold for membrane proteins. Based on this observation, we have proposed a paradigm, referred to as “Epigenetic Dimension of Protein Structure” (EDPS), which takes into account all environmental parameters that control the structure of a protein beyond the amino acid sequence (hence “epigenetic”). In this new study, we compare the reliability of the AlphaFold and Robetta algorithms’ predictions for a new set of membrane proteins involved in human pathologies. We found that Robetta was generally more accurate than AlphaFold for ascribing a membrane-compatible topology. Raft lipids (e.g., gangliosides), which control the structural dynamics of membrane protein structure through chaperone effects, were identified as major actors of the EDPS paradigm. We conclude that the epigenetic dimension of a protein structure is an intrinsic weakness of AI-based protein structure prediction, especially AlphaFold, which warrants further development. MDPI 2022-10-20 /pmc/articles/PMC9599222/ /pubmed/36291736 http://dx.doi.org/10.3390/biom12101527 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
Azzaz, Fodil
Yahi, Nouara
Chahinian, Henri
Fantini, Jacques
The Epigenetic Dimension of Protein Structure Is an Intrinsic Weakness of the AlphaFold Program
title The Epigenetic Dimension of Protein Structure Is an Intrinsic Weakness of the AlphaFold Program
title_full The Epigenetic Dimension of Protein Structure Is an Intrinsic Weakness of the AlphaFold Program
title_fullStr The Epigenetic Dimension of Protein Structure Is an Intrinsic Weakness of the AlphaFold Program
title_full_unstemmed The Epigenetic Dimension of Protein Structure Is an Intrinsic Weakness of the AlphaFold Program
title_short The Epigenetic Dimension of Protein Structure Is an Intrinsic Weakness of the AlphaFold Program
title_sort epigenetic dimension of protein structure is an intrinsic weakness of the alphafold program
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9599222/
https://www.ncbi.nlm.nih.gov/pubmed/36291736
http://dx.doi.org/10.3390/biom12101527
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