Relevant bibliographies by topics / AlphaFold2

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Academic literature on the topic 'AlphaFold2'

Author: Grafiati
Published: 25 May 2024
Last updated: 7 July 2024

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Journal articles on the topic "AlphaFold2"

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Wheeler, Richard John. "A resource for improved predictions of Trypanosoma and Leishmania protein three-dimensional structure." PLOS ONE 16, no. 11 (November 11, 2021): e0259871. http://dx.doi.org/10.1371/journal.pone.0259871.

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AlphaFold2 and RoseTTAfold represent a transformative advance for predicting protein structure. They are able to make very high-quality predictions given a high-quality alignment of the protein sequence with related proteins. These predictions are now readily available via the AlphaFold database of predicted structures and AlphaFold or RoseTTAfold Colaboratory notebooks for custom predictions. However, predictions for some species tend to be lower confidence than model organisms. Problematic species include Trypanosoma cruzi and Leishmania infantum: important unicellular eukaryotic human parasites in an early-branching eukaryotic lineage. The cause appears to be due to poor sampling of this branch of life (Discoba) in the protein sequences databases used for the AlphaFold database and ColabFold. Here, by comprehensively gathering openly available protein sequence data for Discoba species, significant improvements to AlphaFold2 protein structure prediction over the AlphaFold database and ColabFold are demonstrated. This is made available as an easy-to-use tool for the parasitology community in the form of Colaboratory notebooks for generating multiple sequence alignments and AlphaFold2 predictions of protein structure for Trypanosoma, Leishmania and related species.
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Stein, Richard A., and Hassane S. Mchaourab. "SPEACH_AF: Sampling protein ensembles and conformational heterogeneity with Alphafold2." PLOS Computational Biology 18, no. 8 (August 22, 2022): e1010483. http://dx.doi.org/10.1371/journal.pcbi.1010483.

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The unprecedented performance of Deepmind’s Alphafold2 in predicting protein structure in CASP XIV and the creation of a database of structures for multiple proteomes and protein sequence repositories is reshaping structural biology. However, because this database returns a single structure, it brought into question Alphafold’s ability to capture the intrinsic conformational flexibility of proteins. Here we present a general approach to drive Alphafold2 to model alternate protein conformations through simple manipulation of the multiple sequence alignment via in silico mutagenesis. The approach is grounded in the hypothesis that the multiple sequence alignment must also encode for protein structural heterogeneity, thus its rational manipulation will enable Alphafold2 to sample alternate conformations. A systematic modeling pipeline is benchmarked against canonical examples of protein conformational flexibility and applied to interrogate the conformational landscape of membrane proteins. This work broadens the applicability of Alphafold2 by generating multiple protein conformations to be tested biologically, biochemically, biophysically, and for use in structure-based drug design.
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Gordon, Catriona H., Emily Hendrix, Yi He, and Mark C. Walker. "AlphaFold Accurately Predicts the Structure of Ribosomally Synthesized and Post-Translationally Modified Peptide Biosynthetic Enzymes." Biomolecules 13, no. 8 (August 12, 2023): 1243. http://dx.doi.org/10.3390/biom13081243.

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Ribosomally synthesized and post-translationally modified peptides (RiPPs) are a growing class of natural products biosynthesized from a genetically encoded precursor peptide. The enzymes that install the post-translational modifications on these peptides have the potential to be useful catalysts in the production of natural-product-like compounds and can install non-proteogenic amino acids in peptides and proteins. However, engineering these enzymes has been somewhat limited, due in part to limited structural information on enzymes in the same families that nonetheless exhibit different substrate selectivities. Despite AlphaFold2’s superior performance in single-chain protein structure prediction, its multimer version lacks accuracy and requires high-end GPUs, which are not typically available to most research groups. Additionally, the default parameters of AlphaFold2 may not be optimal for predicting complex structures like RiPP biosynthetic enzymes, due to their dynamic binding and substrate-modifying mechanisms. This study assessed the efficacy of the structure prediction program ColabFold (a variant of AlphaFold2) in modeling RiPP biosynthetic enzymes in both monomeric and dimeric forms. After extensive benchmarking, it was found that there were no statistically significant differences in the accuracy of the predicted structures, regardless of the various possible prediction parameters that were examined, and that with the default parameters, ColabFold was able to produce accurate models. We then generated additional structural predictions for select RiPP biosynthetic enzymes from multiple protein families and biosynthetic pathways. Our findings can serve as a reference for future enzyme engineering complemented by AlphaFold-related tools.
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Nunes-Alves, Ariane, and Kenneth Merz. "AlphaFold2 in Molecular Discovery." Journal of Chemical Information and Modeling 63, no. 19 (October 9, 2023): 5947–49. http://dx.doi.org/10.1021/acs.jcim.3c01459.

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Tourlet, Sébastien, Ragousandirane Radjasandirane, Julien Diharce, and Alexandre G. de Brevern. "AlphaFold2 Update and Perspectives." BioMedInformatics 3, no. 2 (May 9, 2023): 378–90. http://dx.doi.org/10.3390/biomedinformatics3020025.

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Access to the three-dimensional (3D) structural information of macromolecules is of major interest in both fundamental and applied research. Obtaining this experimental data can be complex, time consuming, and costly. Therefore, in silico computational approaches are an alternative of interest, and sometimes present a unique option. In this context, the Protein Structure Prediction method AlphaFold2 represented a revolutionary advance in structural bioinformatics. Named method of the year in 2021, and widely distributed by DeepMind and EBI, it was thought at this time that protein-folding issues had been resolved. However, the reality is slightly more complex. Due to a lack of input experimental data, related to crystallographic challenges, some targets have remained highly challenging or not feasible. This perspective exercise, dedicated to a non-expert audience, discusses and correctly places AlphaFold2 methodology in its context and, above all, highlights its use, limitations, and opportunities. After a review of the interest in the 3D structure and of the previous methods used in the field, AF2 is brought into its historical context. Its spatial interests are detailed before presenting precise quantifications showing some limitations of this approach and finishing with the perspectives in the field.
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Bollinger, Terry. "Why AlphaFold is Not Like AlphaGo." Terry's Archive Online 2021, no. 02 (April 12, 2021): 0206. http://dx.doi.org/10.48034/20210206.

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AlphaFold2 is the second major iteration of a protein structure predictor by Google-owned DeepMind Lab. DeepMind is famous for creating AlphaGo Zero, the first game-playing system to transcend the rules taught by human trainers. When AlphaFold2 made a significant leap in protein prediction accuracy in the fourteenth annual CASP competition, even reserved publications like Nature were noticeably breathless in their praise of the results. It was not just the impressive and well-proven leap in prediction accuracy that made AlphaFold2 notable, but also its association with the DeepMind brand and implicitly with the beyond-human learning successes of AlphaGo Zero. But is this latter component of its notoriety and acclaim justified? That is, beyond superficial name similarities, is the design of AlphaFold2 sufficiently like that of AlphaGo Zero to enable a similar leap ahead of human knowledge and expertise? An analysis of the underlying designs says no. In contrast to the fully virtualized, faster-than-human learning speeds of AlphaGo Zero, the learning speed of AlphaFold2 remains firmly attached to and limited by human experimental time. AlphFold2 thus is inherently incapable of the trans-human leaps in learning speed demonstrated by AlphaGo Zero.
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Ng, Tsz Kin, Jie Ji, Qingping Liu, Yao Yao, Wen-Ying Wang, Yingjie Cao, Chong-Bo Chen, et al. "Evaluation of Myocilin Variant Protein Structures Modeled by AlphaFold2." Biomolecules 14, no. 1 (December 21, 2023): 14. http://dx.doi.org/10.3390/biom14010014.

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Deep neural network-based programs can be applied to protein structure modeling by inputting amino acid sequences. Here, we aimed to evaluate the AlphaFold2-modeled myocilin wild-type and variant protein structures and compare to the experimentally determined protein structures. Molecular dynamic and ligand binding properties of the experimentally determined and AlphaFold2-modeled protein structures were also analyzed. AlphaFold2-modeled myocilin variant protein structures showed high similarities in overall structure to the experimentally determined mutant protein structures, but the orientations and geometries of amino acid side chains were slightly different. The olfactomedin-like domain of the modeled missense variant protein structures showed fewer folding changes than the nonsense variant when compared to the predicted wild-type protein structure. Differences were also observed in molecular dynamics and ligand binding sites between the AlphaFold2-modeled and experimentally determined structures as well as between the wild-type and variant structures. In summary, the folding of the AlphaFold2-modeled MYOC variant protein structures could be similar to that determined by the experiments but with differences in amino acid side chain orientations and geometries. Careful comparisons with experimentally determined structures are needed before the applications of the in silico modeled variant protein structures.
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Wilson, Carter J., Wing-Yiu Choy, and Mikko Karttunen. "AlphaFold2: A Role for Disordered Protein/Region Prediction?" International Journal of Molecular Sciences 23, no. 9 (April 21, 2022): 4591. http://dx.doi.org/10.3390/ijms23094591.

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The development of AlphaFold2 marked a paradigm-shift in the structural biology community. Herein, we assess the ability of AlphaFold2 to predict disordered regions against traditional sequence-based disorder predictors. We find that AlphaFold2 performs well at discriminating disordered regions, but also note that the disorder predictor one constructs from an AlphaFold2 structure determines accuracy. In particular, a naïve, but non-trivial assumption that residues assigned to helices, strands, and H-bond stabilized turns are likely ordered and all other residues are disordered results in a dramatic overestimation in disorder; conversely, the predicted local distance difference test (pLDDT) provides an excellent measure of residue-wise disorder. Furthermore, by employing molecular dynamics (MD) simulations, we note an interesting relationship between the pLDDT and secondary structure, that may explain our observations and suggests a broader application of the pLDDT for characterizing the local dynamics of intrinsically disordered proteins and regions (IDPs/IDRs).
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伏信, 進矢. "【用語解説】AlphaFold2." Bulletin of Applied Glycoscience 13, no. 2 (August 20, 2023): 136. http://dx.doi.org/10.5458/bag.13.2_136.

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Boland, Devon J., and Nicola M. Ayres. "Cracking AlphaFold2: Leveraging the power of artificial intelligence in undergraduate biochemistry curriculums." PLOS Computational Biology 20, no. 6 (June 27, 2024): e1012123. http://dx.doi.org/10.1371/journal.pcbi.1012123.

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AlphaFold2 is an Artificial Intelligence-based program developed to predict the 3D structure of proteins given only their amino acid sequence at atomic resolution. Due to the accuracy and efficiency at which AlphaFold2 can generate 3D structure predictions and its widespread adoption into various aspects of biochemical research, the technique of protein structure prediction should be considered for incorporation into the undergraduate biochemistry curriculum. A module for introducing AlphaFold2 into a senior-level biochemistry laboratory classroom was developed. The module’s focus was to have students predict the structures of proteins from the MPOX 22 global outbreak virus isolate genome, which had no structures elucidated at that time. The goal of this study was to both determine the impact the module had on students and to develop a framework for introducing AlphaFold2 into the undergraduate curriculum so that instructors for biochemistry courses, regardless of their background in bioinformatics, could adapt the module into their classrooms.
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Dissertations / Theses on the topic "AlphaFold2"

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Launay, Romain. "Computational characterization and understanding of protein assemblies : the case of the Escherichia coli Ubi metabolon involved in ubiquinone biosynthesis." Electronic Thesis or Diss., Toulouse, INSA, 2023. http://www.theses.fr/2023ISAT0055.

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Les interactions protéine-protéine (PPIs) et les assemblages supramoléculaires sont essentiels pour les fonctions des cellules vivantes. Ils jouent un rôle important dans un certain nombre de fonctions biologiques, comme la transduction de signaux, la communication entre cellules, la transcription, la réplication ou le transport membranaire. La détermination et la caractérisation de telles interfaces restent un défi en biologie structurale. Cependant, les progrès dans le développement de méthodes computationnelles et la puissance des ressources informatiques disponibles de nos jours ont permis une amélioration considérable de la précision des prédictions in silico des modèles tri-dimensionnels d’assemblages protéiques.Dans le cadre de cette thèse, l’objectif était de prédire la structure d’un assemblage supramoléculaire, appelé métabolon Ubi, impliqué dans la voie de biosynthèse de l’ubiquinone (UQ8) dans Escherichia coli. L’ubiquinone est un prénol possédant des propriétés oxydo-réductrices, localisé dans les membranes, et très conservé à travers l’évolution mais également dans les différentes cellules des organismes. Elle est composée de deux parties principales, un groupe aromatique aux propriétés oxydo-réductrices, appelé quinone ou tête polaire, et une queue polyisoprénoide qui est de nature hydrophobe. Dans le cadre de cette étude, ce sont les dernières étapes de la voie de biosynthèse, notamment les modifications (méthylations et hydroxylations) de la tête polaire, qui nous intéressent. Ces réactions ont lieu au sein du métabolon Ubi. Ce dernier est constitué de sept protéines différentes (UbiE, UbiG, UbiF, UbiH, UbiI, UbiJ, UbiK) catalysant six réactions enzymatiques consécutives.Dans ce travail, nous avons cherché à prédire la structure du métabolon et nous avons ainsi été capable de proposer un sous-ensemble protéique que nous avons nommé "sous-unité centrale". Cette sous-unité comprend l’ensemble des partenaires et pourrait être biologiquement fonctionnelle. En parallèle, une étude a été menée sur l’hétérotrimère UbiJ-UbiK2, une brique moléculaire essentielle du métabolon Ubi. Un modèle tri-dimensionnel de UbiJ-UbiK2 a été proposé. A l’aide d’une étude par modélisation multi-échelles, il a pu être montré qu’il pouvait être impliqué dans le relargage de l’ubiquinone au sein des membranes. Enfin, la dernière partie de ce travail a porté sur l’étude du comportement d’une famille particulière d’enzymes, les mono-oxygénases à flavine de classe A, à laquelle appartiennent UbiF, UbiH et UbiI. Une étude comparative entre une enzyme modèle de cette famille enzymatique, appelée PHBH, et UbiI a été réalisée et concluant à la nécessité d’interactions avec des partenaires, permettant de stabiliser ces protéines au sein du métabolon Ubi.L’ensemble de ces travaux, et des hypothèses proposées, permet d’apporter un regard nouveau sur l’organisation supramoléculaire du métabolon Ubi, tant au niveau structural que fonctionnel. Ainsi, nos résultats ouvrent de nouvelles perspectives pour leur étude expérimentale
Protein-protein interactions (PPIs) and supramolecular assemblies are essential for the functions of living cells. They play an important role in various biological functions, such as signal transduction, cell-cell communication, transcription, replication and membrane transport. Determining and characterizing such interfaces remains a challenge in structural biology. However, advances in the development of computational methods and the power of the computing resources available today have led to a considerable improvement in the accuracy of in silico predictions of three-dimensional models of protein assemblies.In this thesis, the aim was to predict the structure of a supramolecular assembly, called the Ubi metabolon, involved in the ubiquinone (UQ8) biosynthesis pathway in Escherichia coli. Ubiquinone is a prenol with oxido-reducing properties, localized in membranes, and highly conserved throughout evolution but also in different cells of organisms. It is composed of two main parts, an aromatic group with oxido-reducing properties, known as quinone or polar head, and a polyisoprenoid tail which is hydrophobic in nature. In this study, we are interested in the final stages of the biosynthetic pathway, in particular the modifications (methylations and hydroxylations) of the polar head. These reactions take place within the Ubi metabolon. The latter is made up of seven different proteins (UbiE, UbiG, UbiF, UbiH, UbiI, UbiJ, UbiK) catalysing six consecutive enzymatic reactions.In this work, we sought to predict the structure of the metabolon and were thus able to propose a protein subset that we called the 'core subunit'. This sub-unit includes all the partners and could be biologically functional. In parallel, a study was carried out on the UbiJ-UbiK2 heterotrimer, an essential molecular brick of the Ubi metabolon. A three-dimensional model of UbiJ-UbiK2 was proposed. Using a multi-scale modelling study, it was shown that it could be involved in the release of ubiquinone from membranes. Finally, the last part of this work focused on studying the behavior of a particular family of enzymes, the class A flavin mono-oxygenases, to which UbiF, UbiH and UbiI belong. A comparative study between a representative enzyme from this family, called PHBH, and UbiI was carried out, concluding that interactions with partners were necessary to stabilize these proteins within the Ubi metabolon.Taken together, this work and the proposed hypotheses provide a new insight into the supramolecular organization of the Ubi metabolon, both structurally and functionally. Our results open up new prospects for their experimental study
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Bruley, Apolline. "Exploitation de signatures des repliements protéiques pour décrire le continuum ordre/désordre au sein des protéomes." Electronic Thesis or Diss., Sorbonne université, 2022. http://www.theses.fr/2022SORUS474.

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Une fraction significative des protéomes reste non annotée, laissant inaccessible une partie du répertoire fonctionnel de la vie, incluant des innovations moléculaires ayant une valeur thérapeutique ou environnementale. Le manque d'annotation fonctionnelle est en partie dû aux limites des approches actuelles pour la détection de relations cachées, ou à des caractéristiques spécifiques telles que le désordre. L'objectif de ma thèse a été de développer des approches méthodologiques reposant sur les signatures structurales des domaines repliés, afin de caractériser plus avant les séquences protéiques dont la fonction est inconnue, même en l'absence d'informations évolutives. Tout d'abord, j'ai développé un score permettant d'estimer le potentiel de repliement d'une séquence d'acides aminés, basé sur sa densité en amas hydrophobes, correspondant principalement aux structures secondaires régulières. J'ai décrit le continuum entre l'ordre et le désordre, couvrant différents états allant des conformations étendues aux globules fondus et ai caractérisé des cas d'ordre conditionnel. Ensuite, j'ai combiné ce score avec les prédictions de structure 3D d'AlphaFold2 (AF2) disponibles pour 21 protéomes de référence. Une grande fraction des acides aminés des modèles AF2 associés à un très faible index de confiance est incluse dans des segments non repliables, soutenant la qualité d'AF2 comme prédicteur du désordre. Cependant, dans chaque protéome, de longs segments repliables avec des prédictions AF2 de faible confiance présentent également des caractéristiques de domaines solubles et repliés. Cela suggère un ordre caché (conditionnel ou inconditionnel), qui n'est pas détecté par AF2 en raison du manque d'informations évolutives, ou des motifs de repliement non répertoriés. Enfin, à l'aide de ces outils, j'ai effectué une exploration préliminaire de protéines ou de régions non annotées, identifiées via le développement et l'application d'une nouvelle procédure d'annotation. Bien que ces séquences soient enrichies en désordre, une part importante d'entre elles présente des caractéristiques de type globulaire soluble. Ces séquences constituent de bons candidats pour de futures validations et caractérisations expérimentales. De plus, l'analyse de gènes de novo validés expérimentalement m'a permis de contribuer au débat encore ouvert sur les caractéristiques structurales des protéines codées par ces gènes, qui présentent un enrichissement en désordre et une grande diversité d'états structuraux
A significant fraction of the proteomes remains unannotated, leaving inaccessible a part of the functional repertoire of life, including molecular innovations with therapeutic or environmental value. Lack of functional annotation is partly due to the limitations of the current approaches in detecting hidden relationships, or to specific features such as disorder. The aim of my PhD thesis was to develop methodological approaches based on the structural signatures of folded domains, in order to further characterize the protein sequences with unknown function even in absence of evolutionary information. First, I developed a scoring system in order to estimate the foldability potential of an amino acid sequence, based on its density in hydrophobic clusters, which mainly correspond to regular secondary structures. I disentangled the continuum between order and disorder, covering various states from extended conformations (random coils) to molten globules and characterize cases of conditional order. Next, I combined this scoring system with the AlphaFold2 (AF2) 3D structure predictions available for 21 reference proteomes. A large fraction of the amino acids with very low AF2 model confidence are included in non-foldable segments, supporting the quality of AF2 as a predictor of disorder. However, within each proteome, long segments with very low AF2 model confidence also exhibit characteristics of soluble, folded domains. This suggests hidden order (conditional or unconditional), which is undetected by AF2 due to lack of evolutionary information, or unrecorded folding patterns. Finally, using these tools, I made a preliminary exploration of unannotated proteins or regions, identified through the development and application of a new annotation workflow. Even though these sequences are enriched in disorder, an important part of them showcases soluble globular-like characteristics. These would make good candidates for further experimental validation and characterization. Moreover, the analysis of experimentally validated de novo genes allowed me to contribute to the still-open debate on the structural features of proteins encoded by these genes, enriched in disorder and displaying a great diversity of structura
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Gosset, Simon. "Annotation du proteome d'A. thaliana via l'analyse et la prédiction de son interactome." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASL004.

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Le fonctionnement des cellules vivantes est assuré par un ensemble d'interactions entre des molécules que l'on appelle protéines. Identifier les couples de protéines en interaction, impliqués dans un processus biologique d'intérêt, permet donc de mieux comprendre son fonctionnement. Pour cela, il existe un ensemble de méthodes expérimentales, mais qui sont trop coûteuses pour explorer l'ensemble des interactions mises en jeu ou pas passez fiables. Pour pallier à ce problème, des méthodes informatiques ont été développées. L'objectif de ma thèse a ainsi été de mettre en place une méthode permettant de construire un réseau d'interactions protéine-protéine (PPI) impliquant des protéines d'intérêt, puis d'y rechercher toutes les protéines impliquées dans un même processus biologique qui constituent des sous-réseaux de protéines fortement in-terconnectées. Le résultat dépendant de la qualité du réseau, j'ai tenté ensuite de l'affiner en y ajoutant des PPI prédites. Pour cela, j'ai mis en place une méthode de prédiction de PPI utilisant Alphafold-multimer, une méthode innovante de prédiction de structure de complexe protéique. En parallèle, j'ai produit un jeu de données décrivant les caractéristiques physico-chimiques, l'énergie de liaison et la propension à l'interaction des surfaces et des interfaces d'un grand nombre de protéines afin de comprendre ce qui distingue les PPI correctement prédites de celles ratées par la méthode que j'ai mise en place
Living organisms function thanks to a set of interactions between molecules called proteins. Identifying the pairs of proteins involved in these interactions provides a better understanding of how they function. There are several experimental methods for identifying these proteins, but they are either too expensive or not reliable enough to explore all the protein interactions that take place in living organisms. Computational methods have been developed to predict these interactions to meet this need. During my thesis, I set up a method for predicting protein-protein interactions using Alphafold-multimer, an innovative structure prediction method. At the same time, I produced a dataset describing the physico-chemical characteristics, binding energy and interaction propensity of the surfaces and interfaces of a large number of proteins to understand what distinguishes correctly predicted PPIs from incorrectly predicted PPI by the method I have set up
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Aimeur, Sana. "Exploration de l'interaction p22phox/p47phox : une clé pour comprendre l'assemblage du complexe NADPH oxydase." Electronic Thesis or Diss., université Paris-Saclay, 2023. http://www.theses.fr/2023UPASF099.

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Le complexe NADPH oxydase phagocytaire (NOX2), occupe une place cruciale dans la génération d'espèces réactives de l'oxygène, jouant un rôle essentiel dans l'immunité innée, ainsi que dans divers processus physiologiques, quant à sa dérégulation participe aux processus de stress oxydatif, du vieillissement ou de l'inflammation. Ce complexe enzymatique complexe est constitué de six sous-unités, dont deux sous unités membranaires, NOX2 et p22phox, tandis que les quatre autres sont des protéines cytosoliques (p47phox, p67phox, p40phox et Rac). NOX2 et p22phox, codés respectivement par les gènes CYBB et CYBA, forment le cœur catalytique du complexe NADPH oxydase des phagocytes. Ce cœur, également appelé flavocytochrome b558 (Cytb558), contient tous les intermédiaires rédox permettant les transferts d'électrons nécessaire à la réduction de l'oxygène en anions superoxyde. Malgré les défis persistants tel que la flexibilité du complexe, la compréhension de la structure et des fonctions de ces sous-unités est un domaine de recherche en croissance. La première étape de l'activation de l'enzyme nécessite l'interaction entre la protéine p47phox et la sous-unité membranaire p22phox. L’exploration de ces deux protéines, et qui plus est celle de leur interaction, sont rendues difficiles en raison des leurs propriétés intrinsèques (régions désordonnées, domaines membranaires, organisation en domaine d’une grande flexibilité). Bien que plusieurs études aient menées, Les mécanismes moléculaires mis en jeu restent à clarifier. Cette thèse se concentre sur plusieurs aspects de cette interaction en abordant cette problématique en combinant des techniques biochimies des protéines solubles et membranaires) avec des études par dichroïsme circulaire et par résonance magnétique nucléaire (RMN), des mesures de diffusion des rayons X aux petits angles (SAXS) et des prédictions de structure par intelligence artificielle (AlphaFold). Ces approches complémentaires ont permis de proposer un scénario montrant les changements conformationnels de p47phox de son état inactif vers des états actifs ont pu être démontrés. Ces changements de structure de p47phox ont par ailleurs été explorés dans le cadre de son interaction avec p22phox. Cela a révélé des aspects structuraux de cette interaction jusque-là méconnu. Parallèlement, l’intelligence artificielle au travers de l'outil AlphaFold a permis d’apporter pour la première fois des modèles structuraux du complexe enzymatique assemblé. Cela a permis d’explorer les interactions protéine-protéine et protéine-membrane lipidique qui devraient encourager des études futures plus approfondies. Enfin, la mise au point de la production et la purification de la protéine membranaire p22phox recombinante, notamment en nanodisques natifs, apporte à ce travail mixant approches expérimentales et théoriques des perspectives nouvelles
The phagocytic NADPH oxidase complex (NOX2) plays a pivotal role in generating reactive oxygen species, crucial for innate immunity and various physiological processes. Its dysregulation contributes to oxidative stress, aging, and inflammation. This complex enzyme comprises six subunits, including two membrane subunits, NOX2 and p22phox, and four cytosolic proteins (p47phox, p67phox, p40phox, and Rac). NOX2 and p22phox, encoded by the CYBB and CYBA genes, respectively, form the catalytic core of the phagocyte NADPH oxidase complex. This core, also known as flavocytochrome b558 (Cytb558), contains all redox intermediates for electron transfers needed to reduce oxygen to superoxide anions. Despite ongoing challenges like the complex's flexibility, understanding the structure and function of these subunits is an expanding research area. The enzyme's activation first requires the interaction between the p47phox protein and the p22phox membrane subunit. Investigating these proteins, particularly their interaction, is challenging due to their intrinsic properties, such as disordered regions, membrane domains, and highly flexible domain organization. Although several studies have been conducted, the molecular mechanisms involved remain to be clarified. This thesis focuses on several aspects of this interaction, addressing the issue by combining biochemical techniques (for both soluble and membrane proteins) with circular dichroism studies, nuclear magnetic resonance (NMR), small-angle X-ray scattering (SAXS), and structure predictions by artificial intelligence (AlphaFold). These complementary approaches have enabled the proposal of a scenario demonstrating the conformational changes of p47phox from its inactive state to active states. These structural changes of p47phox have also been explored in the context of its interaction with p22phox, revealing previously unknown structural aspects of this interaction. Concurrently, artificial intelligence through AlphaFold has provided the first structural models of the assembled enzyme complex, allowing exploration of protein-protein and protein-lipid membrane interactions, which should encourage more in-depth future studies. Finally, the development of production and purification methods for recombinant membrane protein p22phox, particularly in native nanodiscs, adds new perspectives to this work that blends experimental and theoretical approaches
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Book chapters on the topic "AlphaFold2"

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Laurent, Nussaume, Desnos Thierry, Jinsheng Zhu, David Pascale, Kumiko Miwa, and Kanno Satomi. "Analysis and Comparison of Alphafold-Structure Predictions between Pi-Uptake Transporters Recovering Phosphate in Natural Environments." In Plant Phosphorus Nutrition, 129–50. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003440079-9.

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Mubarak, Malad, Aastha Senapati, Purva Bankar, Nirmitee Dolas, Jyoti Srivastava, Shankar Mukundrao Khade, and Krishna Kant Pandey. "Introduction to AI in Biotechnology and Biomedical Engineering." In Future of AI in Biomedicine and Biotechnology, 1–17. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-3629-8.ch001.

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Artificial intelligence (AI) is a field of computer science that works towards using machines/computer programs to perform tasks that would normally require human intelligence. Some of these tasks may include prediction, problem-solving, reasoning, and inferring. Some the most notable uses are protein structure prediction (AlphaFold2) and the prediction of disease-causing genetic mutations in primates (PrimateAI-3D). Machine learning models have also found use in diagnosis. A study showed that it is possible to get accurate diagnosis from limited amounts of data if the data is handled and processed properly. Overall, AI can be applied in a wide range of fields, given that accurate and sufficient data is provided, and this is just what is known. There are many more applications that might come up over time. This chapter aims to give a basic overview of machine learning algorithms used to train models and shed some light on how it is being used in the fields of biotechnology and biomedical engineering.
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Moriarty, David. "Alphafold 2." In Designing and Managing Complex Systems, 263–66. Elsevier, 2023. http://dx.doi.org/10.1016/b978-0-323-91609-7.00026-3.

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Mariano, Diego. "AlphaFold e a busca pelo Santo Graal da Biologia Molecular." In BIOINFO #02 - Revista Brasileira de Bioinformática e Biologia Computacional, 162–67. 2nd ed. Alfahelix, 2022. http://dx.doi.org/10.51780/978-65-992753-5-7-10.

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Recentemente, o uso de técnicas de inteligência artificial tem proporcionado um grande avanço para a solução de um problema da biologia estrutural estudado há mais de 50 anos, trazendo novas perspectivas para a compreensão de doenças e para a descoberta de novos medicamentos.
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Blaney, Jeff, and Andrew M. Davis. "Structure-based Design for Medicinal Chemists." In The Handbook of Medicinal Chemistry, 137–87. The Royal Society of Chemistry, 2023. http://dx.doi.org/10.1039/9781788018982-00137.

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Structure-based design is an essential part of medicinal chemistry. The availability of experimental structures for many drug discovery targets and improvements in molecular modeling software makes it practical for medicinal chemists to do their own modeling and design. We will focus on how we've seen structure-based design performed during many medicinal chemistry projects in industry. Structure-based design relies most critically on a solid understanding of physical organic chemistry, especially conformational analysis and intra- and intermolecular interactions, and these aspects are covered in depth together with illustrative case studies. The amazing rapid advances in cryo-EM over the last several years are the most exciting and dramatic advance for structure-based design since the previous edition of this book. Those advances have enabled structure determination of membrane proteins and large protein complexes. The breakthroughs in protein structure prediction made by AlphaFold and RoseTTAFold are also considered. Future directions for application of structure-based drug design are considered including use of machine learning and large-scale virtual screening, and PROTAC and molecular glue design.
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Jones, David T., and Janet M. Thornton. "AlphaFold — The End of the Protein Folding Problem or the Start of Something Bigger?" In Artificial Intelligence for Science, 67–80. WORLD SCIENTIFIC, 2023. http://dx.doi.org/10.1142/9789811265679_0005.

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Ganguly, Rik, Shashi Kumar Yadav, Prosperwell Ingty, Angneh Ngoruh, and Atanu Bhattacharjee. "LEVERAGING THE STRENGTH OF ARTIFICIAL INTELLIGENCE IN SOLVING PROTEIN STRUCTURES BY ALPHAFOLD-2- A MODERN APPROACH TO UNDERSTAND PROTEIN DYNAMICS." In Futuristic Trends in Biotechnology Volume 2 Book 27, 234–45. Iterative International Publishers, Selfypage Developers Pvt Ltd, 2023. http://dx.doi.org/10.58532/v2bs27p3ch1.

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Proteins are unique macromolecules made up of a long chain of amino acids and are classified based on their function, structure, shape, chemical composition and solubility in different solvents. A wide variety of proteins are prone to misfold and create intracellular or extracellular aggregates that cause severe cellular malfunction. The importance of the protein folding problem was recognized and put forward 50 years back by distinguished scientists. Understanding the dynamics of protein folding is crucial and this can help us predict the ultimate configuration of functional protein. Many of the life-threatening diseases are caused by the misfolding of proteins. The reason for the misfolding can be point mutations since the three-dimensional structure of proteins depends on the primary sequence of its amino acid. Despite fifty years of research, we still need to fill the knowledge gap and accelerate our understanding, particularly in computational biology for the accurate prediction of protein structure. Homology modeling is utilized to predict protein structure in absence of experimental structure. Artificial intelligence, machine learning, and deep learning are being used extensively by researchers to computationally estimate a protein's structure based only on its amino acid sequence. AlphaFold which is in a second iteration tool has changed the perception about protein folding by solving the unsolved structures
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Muhammed, Muhammed Tilahun, and Esin Aki-Yalcin. "Up-to-Date Developments in Homology Modeling." In Applied Computer-Aided Drug Design: Models and Methods, 116–35. BENTHAM SCIENCE PUBLISHERS, 2023. http://dx.doi.org/10.2174/9789815179934123010006.

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Homology modeling is used to predict protein 3D structure from its amino acid sequence. It is the most accurate computational approach to estimate 3D structures. It has straightforward steps that save time and labor. There are several homology modeling tools under use. There is no sole tool that is superior in every aspect. Hence, the user should select the most appropriate one carefully. It is also a common practice to use two or more tools at a time and choose the best model among the resulting models. Homology modeling has various applications in the drug design and development process. Such applications need high-quality 3D structures. It is widely used in combination with other computational methods including molecular docking and molecular dynamics simulation. Like the other computational methods, it has been influenced by the involvement of artificial intelligence. In this regard, homology modeling tools, like AlphaFold, have been introduced. This type of method is expected to contribute to filling the gap between protein sequence release and 3D structure determination. This chapter sheds light on the history, relatively popular tools and steps of homology modeling. A detailed explanation of MODELLER is also given as a case study protocol. Furthermore, homology modeling’s application in drug discovery is explained by exemplifying its role in the fight against the novel Coronavirus. Considering the new advances in the area, better tools and thus high-quality models are expected. These, in turn, pave the way for more applications of it.
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Conference papers on the topic "AlphaFold2"

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Alshammari, Maytha, Jing He, and Willy Wriggers. "AlphaFold2 Model Refinement Using Structure Decoys." In BCB '23: 14th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3584371.3612952.

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han, Chenzi. "AlphaFold2 protein structure prediction based on computer vision." In International Conference on Biological Engineering and Medical Science (ICBIOMed2022), edited by Gary Royle and Steven M. Lipkin. SPIE, 2023. http://dx.doi.org/10.1117/12.2669377.

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Abbas, Usman L., Jin Chen, and Qing Shao. "Assessing Fairness of AlphaFold2 Prediction of Protein 3D Structures." In BCB '23: 14th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3584371.3612943.

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"Использование нейронной сети AlphaFold2 для улучшения результатов белок-белкового докинга." In Теория систем, алгебраическая биология, искусственный интеллект: математические основы и приложения. Рос. акад. наук; Нац. акад. наук Беларуси; Нац. акад. наук Респ. Казахстан; Акад. наук Респ. Узбекистан., 2023. http://dx.doi.org/10.18699/sblai2023-26.

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Oyama, Yosuke, Akihiro Tabuchi, and Atsushi Tokuhisa. "Accelerating AlphaFold2 Inference of Protein Three-Dimensional Structure on the Supercomputer Fugaku." In HPDC '23: The 32nd International Symposium on High-Performance Parallel and Distributed Computing. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3589013.3596674.

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Manshour, Negin, Yang Yu, Wenyuan Qin, Fei He, Duolin Wang, and Dong Xu. "Evaluating template-based and template-free protein-peptide complex structure prediction using AlphaFold2." In 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2022. http://dx.doi.org/10.1109/bibm55620.2022.9994883.

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Ueki, Takafumi, and Masahito Ohue. "Antibody Complementarity-Determining Region Sequence Design Using AlphaFold2 and Binding Affinity Prediction Model." In 2023 Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE). IEEE, 2023. http://dx.doi.org/10.1109/csce60160.2023.00350.

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Liu, Zhe, Weihao Pan, Xuyang Zhen, Jisheng Liang, Wenxiang Cai, Kai Yuan, and Guan Ning Lin. "Will AlphaFold2 Be Helpful in Improving the Accuracy of Single-sequence PPI Site Prediction?" In 2022 10th International Conference on Bioinformatics and Computational Biology (ICBCB). IEEE, 2022. http://dx.doi.org/10.1109/icbcb55259.2022.9802490.

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Alshammari, Maytha, Jing He, and Willy Wriggers. "Refinement of AlphaFold2 Models against Experimental Cryo-EM Density Maps at 4-6Å Resolution." In 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2022. http://dx.doi.org/10.1109/bibm55620.2022.9995676.

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Liang, Chengrui. "An analysis of the mechanism and prospect of AlphaFold2 model based on machine learning and neural network." In International Conference on Biological Engineering and Medical Science (ICBIOMed2022), edited by Gary Royle and Steven M. Lipkin. SPIE, 2023. http://dx.doi.org/10.1117/12.2669676.

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