Academic Journal
A Perspective on the prospective use of AI in protein structure prediction ; Perspective sur l'utilisation prospective de l'IA dans la prédiction de la structure protéique
| العنوان: | A Perspective on the prospective use of AI in protein structure prediction ; Perspective sur l'utilisation prospective de l'IA dans la prédiction de la structure protéique |
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| المؤلفون: | Versini, Raphaelle, Sritharan, Sujith, Aykaç Fas, Burcu, Tubiana, Thibault, Aimeur, Sana Zineb, Henri, Julien, Erard, Marie, Nüsse, Oliver, Jessica, Andreani, Baaden, Marc, Fuchs, Patrick, Galochkina, Tatiana, Chatzigoulas, Alexios, Cournia, Zoe, Santuz, Hubert, Sacquin-Mora, Sophie, Taly, Antoine |
| المساهمون: | Laboratoire de biochimie théorique Paris (LBT (UPR_9080)), Institut de biologie physico-chimique (IBPC (FR_550)), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut de Chimie - CNRS Chimie (INC-CNRS)-Centre National de la Recherche Scientifique (CNRS), Institut de Biologie Intégrative de la Cellule (I2BC), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Institut de Chimie Physique (ICP), Institut de Chimie - CNRS Chimie (INC-CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Institut de Biologie Paris Seine (IBPS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Laboratoire des biomolécules (LBM UMR 7203), Chimie Moléculaire de Paris Centre (FR 2769), École normale supérieure - Paris (ENS-PSL), Université Paris Sciences et Lettres (PSL)-Université Paris Sciences et Lettres (PSL)-Ecole Nationale Supérieure de Chimie de Paris - Chimie ParisTech-PSL (ENSCP), Université Paris Sciences et Lettres (PSL)-Ecole Superieure de Physique et de Chimie Industrielles de la Ville de Paris (ESPCI Paris), Université Paris Sciences et Lettres (PSL)-Institut de Chimie - CNRS Chimie (INC-CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL), Université Paris Sciences et Lettres (PSL)-Institut de Chimie - CNRS Chimie (INC-CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Département de Chimie - ENS-PSL, Université Paris Sciences et Lettres (PSL)-Université Paris Sciences et Lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Biologie Intégrée du Globule Rouge (BIGR (UMR_S_1134 / U1134)), Institut National de la Transfusion Sanguine Paris (INTS)-Université de La Réunion (UR)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pointe-à-Pitre / Abymes Guadeloupe -Université des Antilles (UA)-Université Paris Cité (UPCité), National and Kapodistrian University of Athens (NKUA), ANR-19-CE11-0018,MITOFUSION,Structure, assemblage et propriétés biophysiques des mitofusines(2019), ANR-21-CE13-0016,DIVCON,Diviser et connecter: mise en place de la communication intercellulaire pendant la division cellulaire(2021), ANR-21-CE29-0013,SuperET,Production de superoxyde par transferts d'électrons transmembranaires(2021), ANR-21-CE45-0014,PIRATE,Pharmacophore Interactif grace à la Réalité AugmenTéE(2021), European Project: 956314,ALLODD |
| المصدر: | ISSN: 1549-9596. |
| بيانات النشر: | CCSD American Chemical Society |
| سنة النشر: | 2023 |
| مصطلحات موضوعية: | Alphafold2, Structure prediction, Machin Learning, [CHIM.THEO]Chemical Sciences/Theoretical and/or physical chemistry |
| الوصف: | International audience ; AlphaFold2 (AF2) and RoseTTaFold (RF) have revolutionized structural biology, serving as highly reliable and effective methods for predicting protein structures. This article explores their impact and limitations, focusing on their integration into experimental pipelines and their application in diverse protein classes, including membrane proteins, intrinsically disordered proteins (IDPs), and oligomers.In experimental pipelines, AF2 models aid X-ray crystallography in resolving the phase problem, while complementarity with Mass Spectrometry and NMR data enhances structure determination and protein flexibility prediction. Predicting the structure of membrane proteins remains challenging for both AF2 and RF due to difficulties in capturing conformational ensembles and interactions with the membrane. Improvements in incorporating membrane-specific features and predicting the structural effect of mutations are crucial. For Intrinsically Disordered Proteins, AF2's confidence score (pLDDT) serves as a competitive disorder predictor, but integrative approaches with molecular dynamics simulations or hydrophobic cluster analyses are advocated for accurate dynamics representation. AF2 and RF show promising results for oligomeric models, outperforming traditional docking methods, with AlphaFold-Multimer showing improved performance, however, somes caveats remain in particular for membrane proteins. Real-life examples demonstrate AF2's predictive capabilities in unknown protein structures, but models should be evaluated for their agreement with experimental data. Furthermore, combining AF2 models with molecular dynamics simulations can be used complementarily. In this perspective we propose a "wish list" for improving deep learning-based protein folding prediction models, including using experimental data as constraints and modifying models with binding partners or post-translational modifications. Additionally, a meta-tool for ranking and suggesting composite models is suggested, driving future ... |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| Relation: | info:eu-repo/grantAgreement//956314/EU/Allostery in Drug Discovery/ALLODD |
| DOI: | 10.1021/acs.jcim.3c01361 |
| الاتاحة: | https://hal.science/hal-04306436 https://hal.science/hal-04306436v1/document https://hal.science/hal-04306436v1/file/JCIM_Perspective_prospective%20%281%29.pdf https://doi.org/10.1021/acs.jcim.3c01361 |
| Rights: | info:eu-repo/semantics/OpenAccess |
| رقم الانضمام: | edsbas.6377DA21 |
| قاعدة البيانات: | BASE |
| DOI: | 10.1021/acs.jcim.3c01361 |
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