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1. Computer-aided engineering of a branching sucrase for the glucodiversification of a tetrasaccharide precursor of S. flexneri antigenic oligosaccharides

Author

Mounir Benkoulouche, Akli Ben Imeddourene, Louis-Antoine Barel, Dorian Lefebvre, Mathieu Fanuel, Hélène Rogniaux, David Ropartz, Sophie Barbe, David Guieysse, Laurence A. Mulard, Magali Remaud-Siméon, Claire Moulis, and Isabelle André

Subjects
Medicine, Science
Abstract

Abstract Enzyme engineering approaches have allowed to extend the collection of enzymatic tools available for synthetic purposes. However, controlling the regioselectivity of the reaction remains challenging, in particular when dealing with carbohydrates bearing numerous reactive hydroxyl groups as substrates. Here, we used a computer-aided design framework to engineer the active site of a sucrose-active $$\mathrm{\alpha }$$ α -transglucosylase for the 1,2-cis-glucosylation of a lightly protected chemically synthesized tetrasaccharide, a common precursor for the synthesis of serotype-specific S. flexneri O-antigen fragments. By targeting 27 amino acid positions of the acceptor binding subsites of a GH70 branching sucrase, we used a RosettaDesign-based approach to propose 49 mutants containing up to 15 mutations scattered over the active site. Upon experimental evaluation, these mutants were found to produce up to six distinct pentasaccharides, whereas only two were synthesized by the parental enzyme. Interestingly, we showed that by introducing specific mutations in the active site of a same enzyme scaffold, it is possible to control the regiospecificity of the 1,2-cis glucosylation of the tetrasaccharide acceptor and produce a unique diversity of pentasaccharide bricks. This work offers novel opportunities for the development of highly convergent chemo-enzymatic routes toward S. flexneri haptens.

Published
2021
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2. Protein Design with Deep Learning

Author

Marianne Defresne, Sophie Barbe, and Thomas Schiex

Subjects
computational protein design, artificial neural network, protein structure, inverse folding problem, language models, deep learning, Biology (General), QH301-705.5, Chemistry, QD1-999
Abstract

Computational Protein Design (CPD) has produced impressive results for engineering new proteins, resulting in a wide variety of applications. In the past few years, various efforts have aimed at replacing or improving existing design methods using Deep Learning technology to leverage the amount of publicly available protein data. Deep Learning (DL) is a very powerful tool to extract patterns from raw data, provided that data are formatted as mathematical objects and the architecture processing them is well suited to the targeted problem. In the case of protein data, specific representations are needed for both the amino acid sequence and the protein structure in order to capture respectively 1D and 3D information. As no consensus has been reached about the most suitable representations, this review describes the representations used so far, discusses their strengths and weaknesses, and details their associated DL architecture for design and related tasks.

Published
2021
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3. Three ParA Dimers Cooperatively Assemble on Type Ia Partition Promoters

Author

François Boudsocq, Maya Salhi, Sophie Barbe, and Jean-Yves Bouet

Subjects
bacterial DNA segregation, partition promoter organization, ParA, plasmid F, winged-HTH, Genetics, QH426-470
Abstract

Accurate DNA segregation is essential for faithful inheritance of genetic material. In bacteria, this process is mainly ensured by partition systems composed of two proteins, ParA and ParB, and a centromere site. Auto-regulation of Par operon expression is important for efficient partitioning and is primarily mediated by ParA for type Ia plasmid partition systems. For the F-plasmid, four ParAF monomers were proposed to bind to four repeated sequences in the promoter region. By contrast, using quantitative surface-plasmon-resonance, we showed that three ParAF dimers bind to this region. We uncovered that one perfect inverted repeat (IR) motif, consisting of two hexamer sequences spaced by 28-bp, constitutes the primary ParAF DNA binding site. A similar but degenerated motif overlaps the former. ParAF binding to these motifs is well supported by biochemical and modeling analyses. Molecular dynamics simulations predict that the winged-HTH domain displays high flexibility, which may favor the cooperative ParA binding to the promoter. We propose that three ParAF dimers bind cooperatively to overlapping motifs, thus covering the promoter region. A similar organization is found on closely related and distant plasmid partition systems, suggesting that such promoter organization for auto-regulated Par operons is widespread and may have evolved from a common ancestor.

Published
2021
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4. Guaranteed Diversity and Optimality in Cost Function Network Based Computational Protein Design Methods

Author

Manon Ruffini, Jelena Vucinic, Simon de Givry, George Katsirelos, Sophie Barbe, and Thomas Schiex

Subjects
computational protein design, graphical models, automata, cost function networks, structural biology, diversity, Industrial engineering. Management engineering, T55.4-60.8, Electronic computers. Computer science, QA75.5-76.95
Abstract

Proteins are the main active molecules of life. Although natural proteins play many roles, as enzymes or antibodies for example, there is a need to go beyond the repertoire of natural proteins to produce engineered proteins that precisely meet application requirements, in terms of function, stability, activity or other protein capacities. Computational Protein Design aims at designing new proteins from first principles, using full-atom molecular models. However, the size and complexity of proteins require approximations to make them amenable to energetic optimization queries. These approximations make the design process less reliable, and a provable optimal solution may fail. In practice, expensive libraries of solutions are therefore generated and tested. In this paper, we explore the idea of generating libraries of provably diverse low-energy solutions by extending cost function network algorithms with dedicated automaton-based diversity constraints on a large set of realistic full protein redesign problems. We observe that it is possible to generate provably diverse libraries in reasonable time and that the produced libraries do enhance the Native Sequence Recovery, a traditional measure of design methods reliability.

Published
2021
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5. A Comparative Study to Decipher the Structural and Dynamics Determinants Underlying the Activity and Thermal Stability of GH-11 Xylanases

Author

Jelena Vucinic, Gleb Novikov, Cédric Y. Montanier, Claire Dumon, Thomas Schiex, and Sophie Barbe

Subjects
GH-11 xylanase, molecular dynamics simulations, enzyme thermostability, enzyme activity, dynamic cross correlation, free-energy landscape, Biology (General), QH301-705.5, Chemistry, QD1-999
Abstract

With the growing need for renewable sources of energy, the interest for enzymes capable of biomass degradation has been increasing. In this paper, we consider two different xylanases from the GH-11 family: the particularly active GH-11 xylanase from Neocallimastix patriciarum, NpXyn11A, and the hyper-thermostable mutant of the environmentally isolated GH-11 xylanase, EvXyn11TS. Our aim is to identify the molecular determinants underlying the enhanced capacities of these two enzymes to ultimately graft the abilities of one on the other. Molecular dynamics simulations of the respective free-enzymes and enzyme–xylohexaose complexes were carried out at temperatures of 300, 340, and 500 K. An in-depth analysis of these MD simulations showed how differences in dynamics influence the activity and stability of these two enzymes and allowed us to study and understand in greater depth the molecular and structural basis of these two systems. In light of the results presented in this paper, the thumb region and the larger substrate binding cleft of NpXyn11A seem to play a major role on the activity of this enzyme. Its lower thermal stability may instead be caused by the higher flexibility of certain regions located further from the active site. Regions such as the N-ter, the loops located in the fingers region, the palm loop, and the helix loop seem to be less stable than in the hyper-thermostable EvXyn11TS. By identifying molecular regions that are critical for the stability of these enzymes, this study allowed us to identify promising targets for engineering GH-11 xylanases. Eventually, we identify NpXyn11A as the ideal host for grafting the thermostabilizing traits of EvXyn11TS.

Published
2021
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16. A primer on predictive techniques for food and bioresources transformation processes

Author

Jason Sicard, Sophie Barbe, Rachel Boutrou, Laurent Bouvier, Guillaume Delaplace, Gwenaëlle Lashermes, Laëtitia Théron, Olivier Vitrac, Alberto Tonda, Qualité des Produits Animaux (QuaPA), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Toulouse Biotechnology Institute (TBI), Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Science et Technologie du Lait et de l'Oeuf (STLO), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro Rennes Angers, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Unité Matériaux et Transformations - UMR 8207 (UMET), Centrale Lille-Institut de Chimie du CNRS (INC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Fractionnement des AgroRessources et Environnement (FARE), Université de Reims Champagne-Ardenne (URCA)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Paris-Saclay Food and Bioproduct Engineering (SayFood), AgroParisTech-Université Paris-Saclay-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), and Mathématiques et Informatique Appliquées (MIA Paris-Saclay)

Subjects
FOOD, bioresource, General Chemical Engineering, [SDV.IDA]Life Sciences [q-bio]/Food engineering, modeling, [SPI.GPROC]Engineering Sciences [physics]/Chemical and Process Engineering, prediction, guideline, Food Science, process development
Abstract

International audience; To meet current societal demand for more sustainable transformation processes and bioresources, these processes must be optimized and new ones developed. The evolution of various systems (raw material, food, or process attributes) can be predicted to optimize the uses of biomass for better quality, safety, economic benefit, and sustainability. Predictive modeling can guide the necessary changes and influence industrials, governmental policies and consumers decision-making. However, achieving good predictive capability requires reflection on the models and model validation, which can be difficult. This review aims to help scientists begin to predict by presenting the techniques currently used in predictive science for food and related bioproducts. First, a guideline helps readers initiate a prediction process along with final tips and a warning about the risks involved, with a particular focus on the crucial validation step. Threebroad categories of techniques are then presented: empirical, mechanistic, and artificial intelligence (or “data-driven”). For each category, the advantages and limitations of current techniques for prediction are explained in light of their current domains of applications, illustrated with literature studies and a detailed example. Thus this article provides engineering researchers information about predictive modeling which is a recent relevant development in optimization of both food and nonfood bioresources processes.Practical applications Predictive modeling is a recent development of much relevance in the optimizationof both food and nonfood bioresources processes. The goal of this article is to guide those in research or industry who would like to start predicting. Therefore, the article is intended as a primer on prediction concepts and predictive techniques for food and non-food bioresources processing. Three categories of techniques commonly used in these fields are illustrated by various examples of current applications and amore detailed example helps to understand the implementation process. An increased ability of the global scientific body to predict the outcome of various decisions, often linked or sequential, will open new avenues for designing food products with circularity in mind: maintaining value and not creating waste in the process.

Published
2023

21. Probing the determinants of the transglycosylation/hydrolysis partition in a retaining α-l-arabinofuranosidase

Author

Leila Lo Leggio, Tobias Tandrup, Michael J. O’Donohue, Régis Fauré, Jens-Christian N. Poulsen, Sophie Barbe, Bastien Bissaro, Jiao Zhao, Isabelle André, Claire Dumon, Toulouse Biotechnology Institute (TBI), Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Department of Chemistry [Copenhagen], Faculty of Science [Copenhagen], University of Copenhagen = Københavns Universitet (KU)-University of Copenhagen = Københavns Universitet (KU), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), and University of Copenhagen = Københavns Universitet (UCPH)-University of Copenhagen = Københavns Universitet (UCPH)

Subjects
Models, Molecular, 0106 biological sciences, Glycosylation, [SDV.BIO]Life Sciences [q-bio]/Biotechnology, Glycoside Hydrolases, Stereochemistry, Mutant, Carbohydrate synthesis, Bioengineering, Context (language use), Crystallography, X-Ray, 01 natural sciences, 03 medical and health sciences, Hydrolysis, Residue (chemistry), 010608 biotechnology, Molecular interactions, Glycoside hydrolase, [SDV.BBM.BC]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Biochemistry [q-bio.BM], Molecular Biology, 030304 developmental biology, 0303 health sciences, biology, Chemistry, Engineered transglycosylases, Active site, General Medicine, Acceptor, Mutation, Biocatalysis, biology.protein, Flexibility, Biotechnology
Abstract

International audience; The use of retaining glycoside hydrolases as synthetic tools for glycochemistry is highly topical and the focus of considerable research. However, due to the incomplete identification of the molecular determinants of the transglycosylation/hydrolysis partition (t/h), rational engineering of retaining glycoside hydrolases to create transglycosylases remains challenging. Therefore, to understand better the factors that underpin transglycosylation in a GH51 retaining α-l-arabinofuranosidase from Thermobacillus xylanilyticus, the investigation of this enzyme’s active site was pursued. Specifically, the properties of two mutants, F26L and L352M, located in the vicinity of the active site are described, using kinetic and 3D structural analyses and molecular dynamics simulations. The results reveal that the presence of L352M in the context of a triple mutant (also containing R69H and N216W) generates changes both in the donor and acceptor subsites, the latter being the result of a domino-like effect. Overall, the mutant R69H-N216W-L352M displays excellent transglycosylation activity (70 % yield, 78 % transfer rate and reduced secondary hydrolysis of the product). In the course of this study, the central role played by the conserved R69 residue was also reaffirmed. The mutation R69H affects both the catalytic nucleophile and the acid/base, including their flexibility, and has a determinant effect on the t/h partition. Finally, the results reveal that increased loop flexibility in the acceptor subsites creates new interactions with the acceptor, in particular with a hydrophobic binding platform composed of N216W, W248 and W302.

Published
2021

22. An integrated approach reveals how lipo‐chitooligosaccharides interact with the lysin motif receptor‐like kinase <scp>MtLYR3</scp>

Author

Younes Bouchiba, Jérémy Esque, Ludovic Cottret, Maude Maréchaux, Mégane Gaston, Virginie Gasciolli, Jean Keller, Nico Nouwen, Djamel Gully, Jean‐François Arrighi, Clare Gough, Benoit Lefebvre, Sophie Barbe, Jean‐Jacques Bono, Toulouse Biotechnology Institute (TBI), Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Laboratoire des Interactions Plantes Microbes Environnement (LIPME), Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Laboratoire de Recherche en Sciences Végétales (LRSV), Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT), Laboratoire des symbioses tropicales et méditerranéennes (UMR LSTM), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro Montpellier, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Université de Montpellier (UM), ANR-16-CE20-0025,WHEATSYM,ROLES DES SIGNAUX MICROBIENS LCO/CO ET DE LEURS RECEPTEURS DE PLANTES DANS DES INTERACTIONS BENEFIQUES ENTRE MONOCOTYLEDONES ET MICROORGANISMES DU SOL(2016), ANR-14-CE18-0008,NICE CROPS,Bio-stimulateurs chitiniques naturels pour une agriculture durable(2014), ANR-20-CE20-0017,DUALITY,Récepteurs et contrôle de l'état redox à l'interface symbiose et immunité(2020), ANR-10-LABX-0041,TULIP,Towards a Unified theory of biotic Interactions: the roLe of environmental(2010), and ANR-18-EURE-0019,TULIP-GSR,The Toulouse-Perpignan(2018)

Subjects
Chitosan, lysin motif receptor-like receptor, Oligosaccharides, lipo-chitooligosaccharide, Chitin, Lupinus angustifolius, [SDV.BV.BOT]Life Sciences [q-bio]/Vegetal Biology/Botanics, Biochemistry, biosis, Molecular Docking Simulation, free energy landscape, plant endosym, receptor ligand binding, docking, [SDV.BC.IC]Life Sciences [q-bio]/Cellular Biology/Cell Behavior [q-bio.CB], Medicago truncatula, Aeschynomene spp, Tyrosine, [SDV.BBM]Life Sciences [q-bio]/Biochemistry, Molecular Biology, steered molecular dynamics, [INFO.INFO-BT]Computer Science [cs]/Biotechnology, Molecular Biology
Abstract

International audience; N-acetylglucosamine containing compounds acting as pathogenic or symbiotic signals are perceived by plant-specific Lysin Motif Receptor-Like Kinases (LysM-RLKs). The molecular mechanisms of this perception are not fully understood, notably those of lipo-chitooligosaccharides (LCOs) produced during root endosymbioses with nitrogen-fixing bacteria or arbuscular mycorrhizal fungi. In Medicago truncatula, we previously identified the LysM-RLK LYR3 (MtLYR3) as a specific LCO-binding protein. We also showed that the absence of LCO binding to LYR3 of the non-mycorrhizal Lupinus angustifolius, (LanLYR3), was related to LysM3, which differs from that of MtLYR3 by several amino acids and, particularly, by a critical tyrosine residue absent in LanLYR3. Here, we aimed to define the LCO binding site of MtLYR3 by using molecular modelling and simulation approaches, combined with site-directed mutagenesis and LCO binding experiments. 3D models of MtLYR3 and LanLYR3 ectodomains were built, and homology modelling and molecular dynamics (MD) simulations were performed. Molecular docking and MD simulation on the LysM3 identified potential key residues for LCO binding. We highlighted by steered MD simulations that in addition to the critical tyrosine, two other residues were important for LCO binding in MtLYR3. Substitution of these residues in LanLYR3-LysM3 by those of MtLYR3-LysM3 allowed the recovery of high-affinity LCO binding in experimental radioligand-binding assays. An analysis of selective constraints revealed that the critical tyrosine has experienced positive selection pressure and is absent in some LYR3 proteins. These findings now pave the way to uncover the functional significance of this specific evolutionary pattern.

Published
2022

23. De l’idée du créatif à la production par le collectif

Author

Camille de Bovis, Caroline Hussler, and Anne-Sophie Barbe

Subjects
Economics and Econometrics, Strategy and Management, 0502 economics and business, 05 social sciences, 050211 marketing, Business and International Management, 050203 business & management
Abstract

Comment le concepteur d’un artefact peut-il faire converger l’expression d’une routine vers la représentation qu’il a tenté d’y matérialiser ? L’étude ethnographique de la production de la première d’un opéra, met à jour trois mécanismes par lesquels il aligne les pratiques effectives des utilisateurs de son artefact et l’abstraction qu’il y encode. Les auteurs complètent ainsi notre connaissance des organisations créatives, en montrant comment le régisseur de scène, concepteur de la conduite d’opéra, révèle l’esprit d’une œuvre imaginée par un metteur en scène, tout en enrichissant le rôle des artefacts et de leurs concepteurs dans la dynamique des routines organisationnelles.

Published
2021

24. The Power Implications of the Shift to Customer Reviews: A field perspective on jobbing platforms operating in France

Author

Anne-Sophie Barbe, Jean-Pascal Gond, Caroline Hussler, Department of Management Studies, University of London, Université de Lyon, Aalto-yliopisto, and Aalto University

Subjects
Organizational Behavior and Human Resource Management, Management of Technology and Innovation, Strategy and Management
Abstract

Avattu SAGE-sopimuksella, OA-tieto ei näy vielä julkaisussa Customer reviews are a new type of third-party evaluation that has transformed how power operates over evaluated producers, and in so doing has attracted scholarly attention. However, this literature rarely addresses the power relationships operating between platforms collecting and aggregating these reviews. We address this blind spot by relying on Pierre Bourdieu’s theory of fields, which we use to highlight that the reaction of producers to traditional third-party evaluations depend on, and reproduce, the domination of an evaluation intermediary over its competitors. Our qualitative study of the field of jobbing platforms in France reveals that producers react to customer reviews only on platforms accumulating relatively better stocks of reviews, in a self-reinforcing manner. Managers of platforms with smaller stocks of reviews resist by sheltering jobbers from reviews. Our study re-introduces field-level power dynamics between platforms to research exploring the forms of power that operate on producers subjected to reviews. It adds to studies of evaluation intermediaries by specifying that the accumulation of reviews underpins the power relationships between intermediaries in the customer review era and by identifying sheltering as a new form of resistance. Finally, it updates Bourdieu’s theory for the digital age by explaining that individuals’ accumulation of capital online relates to inter-organisational power dynamics.

Published
2023

26. Computational Design of Miniprotein Binders

Author

Younes Bouchiba, Manon Ruffini, Thomas Schiex, Sophie Barbe, Toulouse Biotechnology Institute (TBI), Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), and ANR-19-P3IA-0004,ANITI,Artificial and Natural Intelligence Toulouse Institute(2019)

Subjects
Protein–protein interaction, Binding affinity, Multistate protein design, [SDV.BIO]Life Sciences [q-bio]/Biotechnology, SARS-CoV-2, Miniprotein binders, Computational protein design, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], [INFO.INFO-BT]Computer Science [cs]/Biotechnology, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract

International audience; Miniprotein binders hold a great interest as a class of drugs that bridges the gap between monoclonal antibodies and small molecule drugs. Like monoclonal antibodies, they can be designed to bind to therapeutic targets with high affinity, but they are more stable and easier to produce and to administer. In this chapter, we present a structure-based computational generic approach for miniprotein inhibitor design. Specifically, we describe step-by-step the implementation of the approach for the design of miniprotein binders against the SARS-CoV-2 coronavirus, using available structural data on the SARS-CoV-2 spike receptor binding domain (RBD) in interaction with its native target, the human receptor ACE2. Structural data being increasingly accessible around many protein-protein interaction systems, this method might be applied to the design of miniprotein binders against numerous therapeutic targets. The computational pipeline exploits provable and deterministic artificial intelligence-based protein design methods, with some recent additions in terms of binding energy estimation, multistate design and diverse library generation.

Published
2022

27. Computational Design of Miniprotein Binders

Author

Younes, Bouchiba, Manon, Ruffini, Thomas, Schiex, and Sophie, Barbe

Subjects
Protein Domains, Artificial Intelligence, SARS-CoV-2, Spike Glycoprotein, Coronavirus, Humans, Computer Simulation, Protein Binding
Abstract

Miniprotein binders hold a great interest as a class of drugs that bridges the gap between monoclonal antibodies and small molecule drugs. Like monoclonal antibodies, they can be designed to bind to therapeutic targets with high affinity, but they are more stable and easier to produce and to administer. In this chapter, we present a structure-based computational generic approach for miniprotein inhibitor design. Specifically, we describe step-by-step the implementation of the approach for the design of miniprotein binders against the SARS-CoV-2 coronavirus, using available structural data on the SARS-CoV-2 spike receptor binding domain (RBD) in interaction with its native target, the human receptor ACE2. Structural data being increasingly accessible around many protein-protein interaction systems, this method might be applied to the design of miniprotein binders against numerous therapeutic targets. The computational pipeline exploits provable and deterministic artificial intelligence-based protein design methods, with some recent additions in terms of binding energy estimation, multistate design and diverse library generation.

Published
2022

29. An engineered PET depolymerase to break down and recycle plastic bottles

Author

B. David, Michel Chateau, Christopher M. Topham, M. Cot, E. Guémard, Vincent Tournier, E. Kamionka, A. Gilles, Gianluca Cioci, M. Dalibey, Elisabeth Moya-Leclair, C. Folgoas, Sophie Barbe, Alain Marty, Marie-Laure Desrousseaux, S. Gavalda, J. Nomme, Sophie Duquesne, Isabelle André, Helene Texier, Toulouse Biotechnology Institute (TBI), Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), CRITT Bio-Industrie, Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Ecole d'Ingénieurs de Purpan (INPT - EI Purpan), Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Biopôle Clermont-Limagne, Carbios, Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), and Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)

Subjects
Models, Molecular, 0301 basic medicine, [SDV.BIO]Life Sciences [q-bio]/Biotechnology, Hydrolases, Phthalic Acids, 02 engineering and technology, Protein Engineering, medicine.disease_cause, Polymerization, 12. Responsible consumption, 03 medical and health sciences, Fusarium, Enzyme Stability, medicine, Recycling, Disulfides, Burkholderiales, Enzyme Assays, Multidisciplinary, Polyethylene Terephthalates, Depolymerization, Chemistry, 021001 nanoscience & nanotechnology, Pulp and paper industry, Thermobifida, Actinobacteria, Polyester, 030104 developmental biology, 13. Climate action, Ideonella sakaiensis, 0210 nano-technology, Carboxylic Ester Hydrolases, Plastics
Abstract

Present estimates suggest that of the 359 million tons of plastics produced annually worldwide1, 150–200 million tons accumulate in landfill or in the natural environment2. Poly(ethylene terephthalate) (PET) is the most abundant polyester plastic, with almost 70 million tons manufactured annually worldwide for use in textiles and packaging3. The main recycling process for PET, via thermomechanical means, results in a loss of mechanical properties4. Consequently, de novo synthesis is preferred and PET waste continues to accumulate. With a high ratio of aromatic terephthalate units—which reduce chain mobility—PET is a polyester that is extremely difficult to hydrolyse5. Several PET hydrolase enzymes have been reported, but show limited productivity6,7. Here we describe an improved PET hydrolase that ultimately achieves, over 10 hours, a minimum of 90 per cent PET depolymerization into monomers, with a productivity of 16.7 grams of terephthalate per litre per hour (200 grams per kilogram of PET suspension, with an enzyme concentration of 3 milligrams per gram of PET). This highly efficient, optimized enzyme outperforms all PET hydrolases reported so far, including an enzyme8,9 from the bacterium Ideonella sakaiensis strain 201-F6 (even assisted by a secondary enzyme10) and related improved variants11–14 that have attracted recent interest. We also show that biologically recycled PET exhibiting the same properties as petrochemical PET can be produced from enzymatically depolymerized PET waste, before being processed into bottles, thereby contributing towards the concept of a circular PET economy. Computer-aided engineering produces improvements to an enzyme that breaks down poly(ethylene terephthalate) (PET) into its constituent monomers, which are used to synthesize PET of near-petrochemical grade that can be further processed into bottles.

Published
2020

30. The covalent complex of Jo-In results from a long-lived, non-covalent intermediate state with near-native structure

Author

Neil, Cox, Cyril, Charlier, Ramadoss, Vijayaraj, Marion, De La Mare, Sophie, Barbe, Isabelle, André, Guy, Lippens, Cédric Y, Montanier, Toulouse Biotechnology Institute (TBI), Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Toulouse White Biotechnology (TWB), the Computing mesocenter of Region Midi-Pyrenees (CALMIP, Toulouse, France) ., Toulouse White Biotechnology (mIMHETIQ project, 2014-2016, and Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)

Subjects
[SDV.BBM.BS]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Structural Biology [q-bio.BM], Protein Stability, Proton Magnetic Resonance Spectroscopy, Biophysics, [SDV.BBM.BM]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Molecular biology, Cell Biology, Biochemistry, Biomolecular welding tool, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, NMR, Jo-in, Streptococcus pneumoniae, Bacterial Proteins, Multiprotein Complexes, [SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], [SDV.BBM]Life Sciences [q-bio]/Biochemistry, Molecular Biology, Molecular modelling, Amino Acids, [SDV.BBM.BC]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Biochemistry [q-bio.BM], Molecular Biology, ComputingMilieux_MISCELLANEOUS, Protein Binding
Abstract

International audience; Covalent protein complexes have been used to assemble enzymes in large scaffolds for biotechnology purposes. Although the catalytic mechanism of the covalent linking of such proteins is well known, the recognition and overall structural mechanisms driving the association are far less understood but could help further functional engineering of these complexes. Here, we study the Jo-In complex by NMR spectroscopy and molecular modelling. We characterize a transient non-covalent complex, with structural elements close to those in the final covalent complex. Using site specific mutagenesis, we further show that this non-covalent association is essential for the covalent complex to form

Published
2022

33. A tripartite carbohydrate-binding module to functionalize cellulose nanocrystal

Author

Cedric Montanier, Anthony K. Henras, Pierre Millard, Amandine Verdier, Julien Pérochon, Sophie Barbe, Younes Bouchiba, Brice Enjalbert, Callum Burnard, Yves Romeo, Marion Toanen, Sébastien Nouaille, Juan Cortés, Angeline Pelus, Régis Fauré, Stéphanie Heux, Camille Wagner, Jérémy Esque, Gaëlle Bordes, Alejandro Estaña, Gilles Truan, Claude Le Men, Centre de Biologie Intégrative (CBI), Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS), Toulouse Biotechnology Institute (TBI), Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Équipe Robotique et InteractionS (LAAS-RIS), Laboratoire d'analyse et d'architecture des systèmes (LAAS), Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées, Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Unité de biologie moléculaire, cellulaire et du développement - UMR5077 (MCD), Université de Toulouse (UT)-Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS), Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université Toulouse Capitole (UT Capitole), and Université de Toulouse (UT)

Subjects
Streptavidin, [SDV.BIO]Life Sciences [q-bio]/Biotechnology, Biomedical Engineering, 02 engineering and technology, Polysaccharide, Clostridium thermocellum, 03 medical and health sciences, chemistry.chemical_compound, Polysaccharides, Molecule, General Materials Science, Cellulose, 030304 developmental biology, chemistry.chemical_classification, 0303 health sciences, Binding Sites, biology, [SDV.BBM.BM]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Molecular biology, 021001 nanoscience & nanotechnology, biology.organism_classification, Combinatorial chemistry, chemistry, Surface modification, Nanoparticles, Azide, Carbohydrate-binding module, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], 0210 nano-technology
Abstract

International audience; The development of protein and microorganism engineering have led to rising expectations of biotechnology in the design of emerging biomaterials, putatively of high interest to reduce our dependence on fossil carbon resources. In this way, cellulose, a renewable carbon based polysaccharide and derived products, displays unique properties used in many industrial applications. Although the functionalization of cellulose is common, it is however limited in terms of number and type of functions. In this work, a Carbohydrate-Binding Module (CBM) was used as a central core to provide a versatile strategy to bring a large diversity of functions to cellulose surfaces. CBM3a from Clostridium thermocellum, which has a high affinity for crystalline cellulose, was flanked through linkers with a streptavidin domain and an azide group introduced through a non-canonical amino acid. Each of these two extra domains was effectively produced and functionalized with a variety of biological and chemical molecules. Structural properties of the resulting tripartite chimeric protein were investigated using molecular modelling approaches, and its potential for the multi-functionalization of cellulose was confirmed experimentally. As a proof of concept, we show that cellulose can be labelled with a fluorescent version of the tripartite protein grafted to magnetic beads and captured using a magnet.

Published
2021

34. Cost Function Networks to Solve Large Computational Protein Design Problems

Author

David Allouche, Yahia Lebbah, Samir Loudni, Simon de Givry, Matthias Zytnicki, Sophie Barbe, Abdelkader Ouali, George Katsirelos, Thomas Schiex, David Simoncini, Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Université Fédérale Toulouse Midi-Pyrénées, Toulouse Biotechnology Institute (TBI), Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Mathématiques et Informatique Appliquées (MIA-Paris), AgroParisTech-Université Paris-Saclay-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Laboratoire d'Informatique et Technologies de l'Information d'Oran (LITIO), Université d'Oran Al-Sanya, Groupe de Recherche en Informatique, Image, Automatique et Instrumentation de Caen (GREYC), Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-École Nationale Supérieure d'Ingénieurs de Caen (ENSICAEN), Normandie Université (NU)-Centre National de la Recherche Scientifique (CNRS), Knowledge representation, reasonning (ORPAILLEUR), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Real Expression Artificial Life (IRIT-REVA), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse 1 Capitole (UT1), DAS-SAB, ANR-10-BLAN-0214,FICOLOFO,Filtrage par cohérences locales fortes pour les réseaux de fonctions de coûts et autres modèles graphiques(2010), ANR-16-CE40-0028,DE-MO-GRAPH,Décomposition de Modèles Graphiques(2016), ANR-19-P3IA-0004,ANITI,Artificial and Natural Intelligence Toulouse Institute(2019), Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS), Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA), Institut National de la Recherche Agronomique (INRA), Institut National des Sciences Appliquées (INSA), Equipe CODAG - Laboratoire GREYC - UMR6072, Normandie Université (NU)-Centre National de la Recherche Scientifique (CNRS)-Université de Caen Normandie (UNICAEN), Laboratoire d'Informatique, Signaux, et Systèmes de Sophia-Antipolis (I3S) / Groupe SCOBI, Modèles Discrets pour les Systèmes Complexes (Laboratoire I3S - MDSC), Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), Université Nice Sophia Antipolis (... - 2019) (UNS), Université Côte d'Azur (UCA)-Université Côte d'Azur (UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Nice Sophia Antipolis (... - 2019) (UNS), Université Côte d'Azur (UCA)-Université Côte d'Azur (UCA)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), Université Côte d'Azur (UCA)-Université Côte d'Azur (UCA)-Centre National de la Recherche Scientifique (CNRS), This work has been partly funded by the 'Agence nationale de la Recherche' (ANR-10-BLA-0214, ANR-12-MONU-0015-03, and ANR-16-C40-0028)., Malek Masmoudi, Bassem Jarboui, Patrick Siarry, Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-AgroParisTech-Université Paris-Saclay, Groupe de Recherche en Informatique, Image et Instrumentation de Caen (GREYC), Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Ingénieurs de Caen (ENSICAEN), Normandie Université (NU)-Normandie Université (NU)-Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Ingénieurs de Caen (ENSICAEN), Normandie Université (NU), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Mathématiques et Informatique Appliquées (MIA Paris-Saclay), Département Automatique, Productique et Informatique (IMT Atlantique - DAPI), IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Théorie, Algorithmes et Systèmes en Contraintes (LS2N - équipe TASC ), Laboratoire des Sciences du Numérique de Nantes (LS2N), Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-École Centrale de Nantes (Nantes Univ - ECN), Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ)-Nantes université - UFR des Sciences et des Techniques (Nantes univ - UFR ST), Nantes Université - pôle Sciences et technologie, Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ)-Nantes Université - pôle Sciences et technologie, Nantes Université (Nantes Univ)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Nantes Université (Nantes Univ), Université Toulouse Capitole (UT Capitole), and Université de Toulouse (UT)

Subjects
0303 health sciences, Sequence, Theoretical computer science, Computer science, media_common.quotation_subject, Protein design, education, 02 engineering and technology, Protein engineering, [INFO.INFO-RO]Computer Science [cs]/Operations Research [cs.RO], [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Task (project management), [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 03 medical and health sciences, Chain (algebraic topology), 0202 electrical engineering, electronic engineering, information engineering, Combinatorial optimization, 020201 artificial intelligence & image processing, [INFO]Computer Science [cs], [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Function (engineering), Integer programming, ComputingMilieux_MISCELLANEOUS, 030304 developmental biology, media_common
Abstract

International audience; Proteins are chains of simple molecules called amino acids. The sequence of amino acids in the chain defines the three-dimensional shape of the protein and ultimately its biochemical function. Over millions of years, living organisms have evolved a large catalog of proteins. By exploring the space of possible amino acid sequences, protein engineering aims at similarly designing tailored proteins with specific desirable properties such as therapeutic properties in biomedical engineering for healthcare purposes. In computational protein design (CPD), the challenge of identifying a protein that performs a given task is defined as the combinatorial optimization of a complex energy function over amino acid sequences. First, we introduce the CPD problem and some of the main approaches that have been used by structural biologists to solve it. The CPD problem can be formulated as a cost function network (CFN). We present some of the most efficient techniques in CFN. Overall, the CFN approach shows the best efficiency on these problems, improving by several orders of magnitude against the previous exact CPD-dedicated approaches and also against integer programming approaches.

Published
2021

35. A Comparative Study to Decipher the Structural and Dynamics Determinants Underlying the Activity and Thermal Stability of GH-11 Xylanases

Author

Thomas Schiex, Jelena Vucinic, Claire Dumon, Cedric Montanier, Sophie Barbe, Gleb Novikov, Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRAE), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Toulouse Biotechnology Institute (TBI), Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), INRAE, Region Occitanie, Toulouse White Biotechnology, ANR-19-P3IA-0004,ANITI,Artificial and Natural Intelligence Toulouse Institute(2019), Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA), and Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)

Subjects
0301 basic medicine, [SDV.BIO]Life Sciences [q-bio]/Biotechnology, Mutant, 01 natural sciences, Molecular dynamics, Catalytic Domain, Enzyme Stability, GH-11 xylanase, Biology (General), Spectroscopy, chemistry.chemical_classification, [SDV.BBM.BS]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Structural Biology [q-bio.BM], 010304 chemical physics, biology, Chemistry, Temperature, General Medicine, Computer Science Applications, enzyme activity, enzyme thermostability, Xylanase, QH301-705.5, free-energy landscape, Neocallimastix patriciarum, Molecular Dynamics Simulation, Catalysis, Article, Inorganic Chemistry, 03 medical and health sciences, 0103 physical sciences, Amino Acid Sequence, Physical and Theoretical Chemistry, QD1-999, Molecular Biology, Endo-1,4-beta Xylanases, Organic Chemistry, enzyme–substrate interaction, Active site, Substrate (chemistry), [CHIM.CATA]Chemical Sciences/Catalysis, molecular dynamics simulations, Kinetics, 030104 developmental biology, Enzyme, dynamic cross correlation, Helix, biology.protein, Biophysics, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Neocallimastix
Abstract

With the growing need for renewable sources of energy, the interest for enzymes capable of biomass degradation has been increasing. In this paper, we consider two different xylanases from the GH-11 family: the particularly active GH-11 xylanase from Neocallimastix patriciarum, NpXyn11A, and the hyper-thermostable mutant of the environmentally isolated GH-11 xylanase, EvXyn11TS. Our aim is to identify the molecular determinants underlying the enhanced capacities of these two enzymes to ultimately graft the abilities of one on the other. Molecular dynamics simulations of the respective free-enzymes and enzyme–xylohexaose complexes were carried out at temperatures of 300, 340, and 500 K. An in-depth analysis of these MD simulations showed how differences in dynamics influence the activity and stability of these two enzymes and allowed us to study and understand in greater depth the molecular and structural basis of these two systems. In light of the results presented in this paper, the thumb region and the larger substrate binding cleft of NpXyn11A seem to play a major role on the activity of this enzyme. Its lower thermal stability may instead be caused by the higher flexibility of certain regions located further from the active site. Regions such as the N-ter, the loops located in the fingers region, the palm loop, and the helix loop seem to be less stable than in the hyper-thermostable EvXyn11TS. By identifying molecular regions that are critical for the stability of these enzymes, this study allowed us to identify promising targets for engineering GH-11 xylanases. Eventually, we identify NpXyn11A as the ideal host for grafting the thermostabilizing traits of EvXyn11TS.

Published
2021

36. Molecular flexibility in computational protein design: an algorithmic perspective

Author

Juan Cortés, Thomas Schiex, Sophie Barbe, Younes Bouchiba, Toulouse Biotechnology Institute (TBI), Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Équipe Robotique et InteractionS (LAAS-RIS), Laboratoire d'analyse et d'architecture des systèmes (LAAS), Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT), Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRAE), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), ANR-19-P3IA-0004,ANITI,Artificial and Natural Intelligence Toulouse Institute(2019), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées, and Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA)

Subjects
Models, Molecular, Computer science, Protein Conformation, Reliability (computer networking), Distributed computing, Protein design, Bioengineering, Discrete set, 010402 general chemistry, 01 natural sciences, Biochemistry, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 03 medical and health sciences, Multistate design, Molecular Biology, 030304 developmental biology, Flexibility (engineering), 0303 health sciences, [SDV.BBM.BS]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Structural Biology [q-bio.BM], Perspective (graphical), Computational Biology, Reproducibility of Results, Provable and heuristic algorithms, 0104 chemical sciences, Range (mathematics), Continuous flexibility, Backbone perturbations, Computational problem, Computational protein design, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Engineering design process, Algorithms, Biotechnology
Abstract

Computational protein design (CPD) is a powerful technique for engineering new proteins, with both great fundamental implications and diverse practical interests. However, the approximations usually made for computational efficiency, using a single fixed backbone and a discrete set of side chain rotamers, tend to produce rigid and hyper-stable folds that may lack functionality. These approximations contrast with the demonstrated importance of molecular flexibility and motions in a wide range of protein functions. The integration of backbone flexibility and multiple conformational states in CPD, in order to relieve the inaccuracies resulting from these simplifications and to improve design reliability, are attracting increased attention. However, the greatly increased search space that needs to be explored in these extensions defines extremely challenging computational problems. In this review, we outline the principles of CPD and discuss recent effort in algorithmic developments for incorporating molecular flexibility in the design process.

Published
2021

37. Seven amino acid types suffice to reconstruct the core fold of RNA polymerase

Author

Aditya K. Padhi, Thomas Schiex, Reiko Nakagawa, David Simoncini, Sota Yagi, Jelena Vucinic, Kam Y. J. Zhang, Sophie Barbe, and Shunsuke Tagami

Subjects
chemistry.chemical_classification, chemistry.chemical_compound, Enzyme, Translation system, chemistry, Extant taxon, Biochemistry, RNA polymerase, Gene duplication, Peptide, Genetic code, Amino acid
Abstract

The extant complex proteins must have evolved from ancient short and simple ancestors. Nevertheless, how such prototype proteins emerged on the primitive earth remains enigmatic. The double-psi beta-barrel (DPBB) is one of the oldest protein folds and conserved in various fundamental enzymes, such as the core domain of RNA polymerase. Here, by reverse engineering a modern DPBB domain, we reconstructed its evolutionary pathway started by “interlacing homo- dimerization” of a half-size peptide, followed by gene duplication and fusion. Furthermore, by simplifying the amino acid repertoire of the peptide, we successfully created the DPBB fold with only seven amino acid types (Ala, Asp, Glu, Gly, Lys, Arg, and Val), which can be coded by only GNN and ARR (R = A or G) codons in the modern translation system. Thus, the DPBB fold could have been materialized by the early translation system and genetic code.

Published
2021

38. Seven Amino Acid Types Suffice to Create the Core Fold of RNA Polymerase

Author

Thomas Schiex, Sota Yagi, Aditya K. Padhi, Shunsuke Tagami, Reiko Nakagawa, Sophie Barbe, David Simoncini, Jelena Vucinic, Kam Y. J. Zhang, RIKEN Center for Biosystems Dynamics Research [Kobe] (RIKEN BDR), RIKEN - Institute of Physical and Chemical Research [Japon] (RIKEN), Real Expression Artificial Life (IRIT-REVA), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées, Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Toulouse Biotechnology Institute (TBI), Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA), ANR-19-P3IA-0004,ANITI,Artificial and Natural Intelligence Toulouse Institute(2019), ANR-20-CE45-0016,PISA,Evaluation in silico de protéines(2020), Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT)-Université de Toulouse (UT)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Toulouse Mind & Brain Institut (TMBI), Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), and Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRAE)

Subjects
Models, Molecular, Protein Folding, Protein Conformation, Peptide, Biochemistry, Catalysis, Core domain, 03 medical and health sciences, chemistry.chemical_compound, 0302 clinical medicine, Colloid and Surface Chemistry, Protein Domains, RNA polymerase, Gene duplication, Amino Acid Sequence, Amino Acids, 030304 developmental biology, chemistry.chemical_classification, Genetics, 0303 health sciences, Translation system, [SDV.BBM.BS]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Structural Biology [q-bio.BM], General Chemistry, DNA-Directed RNA Polymerases, Genetic code, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Amino acid, Enzyme, chemistry, 030217 neurology & neurosurgery, [CHIM.CHEM]Chemical Sciences/Cheminformatics
Abstract

International audience; The extant complex proteins must have evolved from ancient short and simple ancestors. The double-ψ β-barrel (DPBB) is one of the oldest protein folds and conserved in various fundamental enzymes, such as the core domain of RNA polymerase. Here, by reverse engineering a modern DPBB domain, we reconstructed its plausible evolutionary pathway started by “interlacing homodimerization” of a half-size peptide, followed by gene duplication and fusion. Furthermore, by simplifying the amino acid repertoire of the peptide, we successfully created the DPBB fold with only seven amino acid types (Ala, Asp, Glu, Gly, Lys, Arg, and Val), which can be coded by only GNN and ARR (R = A or G) codons in the modern translation system. Thus, the DPBB fold could have been materialized by the early translation system and genetic code.

Published
2021

39. Temporal work by consultants in nascent market categories: constructing a market for knowledge in quantum computing

Author

Amber Geurts, Nina Granqvist, Anne-Sophie Barbe, Oona Hilkamo, Rathenau Institute, Department of Management Studies, Aalto-yliopisto, and Aalto University

Subjects
Knowledge management, business.industry, Computer science, Strategy and Management, 05 social sciences, Inductive analysis, Management Science and Operations Research, 050905 science studies, quantum computing, temporal work, Intermediary, market for knowledge, Work (electrical), Market categories, 0502 economics and business, emergence, 0509 other social sciences, business, 050203 business & management, Quantum computer
Abstract

Publisher Copyright: © 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. The literatures on market categories and temporal work pay limited attention to the agentic role of intermediaries in nascent market categories. Through an inductive analysis of quantum computing, we explore how management consultancies perform temporal work in such settings. We find that management consultancies construct a market for knowledge by engaging in three types of temporal work. First, they bring the future market category into present existence and thus make it an object for action. Second, they construct ultimate uncertainty and ambiguity and therefore a need for external knowledge. Third, they create a sense of urgency for immediate market engagement. Our findings shed light on the active intermediating role of management consultancies in nascent market categories, allowing them to capitalise on novel markets very early on.

Published
2021

40. Variable Neighborhood Search with Cost Function Networks To Solve Large Computational Protein Design Problems

Author

David Mignon, Sophie Barbe, Thomas Schiex, Juan Cortés, Thomas Simonson, David Allouche, Antoine Charpentier, ATOS Origin, Laboratoire de Biochimie de l'Ecole polytechnique (BIOC), École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Ingénierie des Systèmes Biologiques et des Procédés (LISBP), Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de la Recherche Agronomique (INRA), Équipe Robotique et InteractionS (LAAS-RIS), Laboratoire d'analyse et d'architecture des systèmes (LAAS), Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Université Toulouse 1 Capitole (UT1)-Université Toulouse - Jean Jaurès (UT2J)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Université Toulouse 1 Capitole (UT1)-Université Toulouse - Jean Jaurès (UT2J), Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA), Institut National de la Recherche Agronomique (INRA), Institut National de la Recherche Agronomique (INRA)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS), Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT), ANR-16-CE40-0028,DE-MO-GRAPH,Décomposition de Modèles Graphiques(2016), Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse 1 Capitole (UT1), and Université Fédérale Toulouse Midi-Pyrénées

Subjects
Models, Molecular, Mathematical optimization, Protein Conformation, PREDICTION, Computer science, General Chemical Engineering, Protein design, Monte Carlo method, SOFTWARE, Library and Information Sciences, Protein Engineering, Energy minimization, 01 natural sciences, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Software, MONTE-CARLO, 0103 physical sciences, DEAD-END ELIMINATION, [SDV.BBM.BC]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Biochemistry [q-bio.BM], OPTIMIZATION, 010304 chemical physics, business.industry, REDESIGN, ALGORITHMS, Probabilistic logic, Computational Biology, Proteins, General Chemistry, Protein engineering, FRAMEWORK, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, [SDV.BBM.BC]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Biomolecules [q-bio.BM], 0104 chemical sciences, Computer Science Applications, 010404 medicinal & biomolecular chemistry, Dead-end elimination, LIBRARY, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], SIDE-CHAIN, business, Monte Carlo Method, Variable neighborhood search
Abstract

International audience; Computational protein design (CPD) aims to predict amino acid sequences that fold to specific structures and perform desired functions. CPD depends on a rotamer library, an energy function, and an algorithm to search the sequence/conformation space. Variable neighborhood search (VNS) with cost function networks is a powerful framework that can provide tight upper bounds on the global minimum energy. We propose a new CPD heuristic based on VNS in which a subset of the solution space (a “neighborhood”) is explored, whose size is gradually increased with a dedicated probabilistic heuristic. The algorithm was tested on 99 protein designs with fixed backbones involving nine proteins from the SH2, SH3, and PDZ families. The number of mutating positions was 20, 30, or all of the amino acids, while the rest of the protein explored side-chain rotamers. VNS was more successful than Monte Carlo (MC), replica-exchange MC, and a heuristic steepest-descent energy minimization, providing solutions with equal or lower best energies in most cases. For complete protein redesign, it gave solutions that were 2.5 to 11.2 kcal/mol lower in energy than those obtained with the other approaches. VNS is implemented in the toulbar2 software. It could be very helpful for large and/or complex design problems.

Published
2018

41. Guaranteed Diversity & Quality for the Weighted CSP

Author

Jelena Vucinic, Sophie Barbe, Simon de Givry, George Katsirelos, Thomas Schiex, Manon Ruffini, Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Mathématiques et Informatique Appliquées (MIA-Paris), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-AgroParisTech-Université Paris-Saclay, Laboratoire d'Ingénierie des Systèmes Biologiques et des Procédés (LISBP), Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de la Recherche Agronomique (INRA), Funded by the Agence Nationale de la Recherche, ANR-16-CE40-002, ANR-19-PIA3-0004, ANR-16-CE40-0028,DE-MO-GRAPH,Décomposition de Modèles Graphiques(2016), ANR-19-P3IA-0004,ANITI,Artificial and Natural Intelligence Toulouse Institute(2019), Institut National de la Recherche Agronomique (INRA), Toulouse Biotechnology Institute (TBI), Institut National de la Recherche Agronomique (INRA)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, contrat REGION doctorant TOULOUZETA n° 00000950-CT15000449, Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRAE), Mathématiques et Informatique Appliquées (MIA Paris-Saclay), AgroParisTech-Université Paris-Saclay-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), and Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Protein Design, Mathematical optimization, Computer science, Bioinformatics, [SDV]Life Sciences [q-bio], 02 engineering and technology, 010501 environmental sciences, 01 natural sciences, Constraint Programming, Set (abstract data type), CONSISTENCY, [SDV.IDA]Life Sciences [q-bio]/Food engineering, 0202 electrical engineering, electronic engineering, information engineering, Constraint programming, [SPI.GPROC]Engineering Sciences [physics]/Chemical and Process Engineering, [INFO]Computer Science [cs], COMPUTATIONAL PROTEIN DESIGN, [MATH]Mathematics [math], OPTIMIZATION, Constraint satisfaction problem, ComputingMilieux_MISCELLANEOUS, 0105 earth and related environmental sciences, Soft Arc Consistency, Diversity, Bayesian network, Function (mathematics), CONSTRAINT, Dual and Hidden representation, Weighted Constraint Satisfaction Problem, Semi-metric, Bounded function, [SDE]Environmental Sciences, 020201 artificial intelligence & image processing
Abstract

International audience; In many applications of constraint programming, it is often impossible to capture all the relevant information in one numerical criterion. In this case, it is useful to produce a set of high quality yet diverse solutions. In this paper, motivated by a Computational Protein Design application, we consider the general problem of producing a diverse set of high-quality solutions of a given Weighted Constraint Satisfaction Problem, with guarantees both on solution quality and diversity. We use weighted automata decomposed in functions of bounded arity, incremental CFN solving, a simple form of predictive bounding and compressed representations of distance constraints for improved efficiency. We show that this approach can be successfully applied to a variety of problems that include both Protein Design Problems but also large Bayesian networks represented as Cost Function Networks. We also show that our approach has the capacity to enumerate so-called local delta-modes and that it does provide improved protein designs.

Published
2019

42. Protein Design with Deep Learning

Author

Thomas Schiex, Marianne Defresne, Sophie Barbe, Toulouse Biotechnology Institute (TBI), Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), ANR-19-PI3A-0004,Future - PI3A, and ANR-18-EURE-0021,BIOECO,Biotechnology Building Bio-based Economy(2018)

Subjects
generative models, QH301-705.5, Computer science, Review, Protein Engineering, Machine learning, computer.software_genre, Catalysis, Inorganic Chemistry, 03 medical and health sciences, Protein Domains, language models, Leverage (statistics), [SDV.BBM]Life Sciences [q-bio]/Biochemistry, Molecular Biology, Biology (General), protein structure, Physical and Theoretical Chemistry, Architecture, Design methods, QD1-999, Molecular Biology, Spectroscopy, 030304 developmental biology, 0303 health sciences, Artificial neural network, business.industry, Deep learning, 030302 biochemistry & molecular biology, Organic Chemistry, Computational Biology, Proteins, inverse folding problem, deep learning, General Medicine, Computer Science Applications, Variety (cybernetics), Chemistry, computational protein design, Artificial intelligence, Language model, business, Raw data, computer, artificial neural network
Abstract

International audience; Computational Protein Design (CPD) has produced impressive results for engineering new proteins, resulting in a wide variety of applications. In the past few years, various efforts have aimed at replacing or improving existing design methods using Deep Learning technology to leverage the amount of publicly available protein data. Deep Learning (DL) is a very powerful tool to extract patterns from raw data, provided that data are formatted as mathematical objects and the architecture processing them is well suited to the targeted problem. In the case of protein data, specific representations are needed for both the amino acid sequence and the protein structure in order to capture respectively 1D and 3D information. As no consensus has been reached about the most suitable representations, this review describes the representations used so far, discusses their strengths and weaknesses, and details their associated DL architecture for design and related tasks.

Published
2021

43. What’s New (for Scholars) about Transaction Platforms? A Systematic Literature Review

Author

Anne Sophie Barbe

Subjects
Systematic review, Computer science, General Medicine, GeneralLiterature_REFERENCE(e.g.,dictionaries,encyclopedias,glossaries), Data science, Database transaction
Abstract

This paper systematically reviews the literature on transaction platforms by examining what makes transaction platforms novel for organization and management (O&M) scholars. To do so, it tracks the...

Published
2021

44. Rational design of adjuvants targeting the C-type lectin Mincle

Author

Eric Perouzel, Alain Vercellone, Jacques Prandi, Martine Gilleron, Sandro Silva-Gomes, David Sancho, Jérôme Nigou, Francisco J. Cueto, Gérard Tiraby, Thierry Lioux, Sophie Barbe, Alexiane Decout, Daniel Drocourt, Isabelle André, Institut de pharmacologie et de biologie structurale (IPBS), Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS), Research Department, InvivoGen, Laboratoire d'Ingénierie des Systèmes Biologiques et des Procédés (LISBP), Institut National de la Recherche Agronomique (INRA)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS), Fundación Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), Centre National de la Recherche Scientifique (CNRS)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de la Recherche Agronomique (INRA), Association Nationale de la Recherche et de la Technologie (ANRT), Nigou, Jérôme, Université de Toulouse (UT)-Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS), and Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)

Subjects
mycobacteria, glycolipid, innate immunity, 0301 basic medicine, vaccin, [SDV]Life Sciences [q-bio], medicine.medical_treatment, Mannose, immunogenicity, Adaptive Immunity, approche pharmacologique, Mice, chemistry.chemical_compound, 0302 clinical medicine, glycolipide, Cell Wall, C-type lectin, vaccine, immunogenicite, mycobactérie, Receptors, Immunologic, Multidisciplinary, Immunogenicity, Biological Sciences, 3. Good health, Process Engineering, Biochemistry, Vaccines, Subunit, Cord Factors, paroi cellulaire, Adjuvant, cytokine anti inflammatoire, vaccine adjuvants, Médecine humaine et pathologie, Molecular Dynamics Simulation, Biology, Mycobacterium, 03 medical and health sciences, Glycolipid, Immune system, adjuvant, Adjuvants, Immunologic, medicine, Animals, Humans, Tuberculosis, [SPI.GPROC]Engineering Sciences [physics]/Chemical and Process Engineering, Lectins, C-Type, Génie des procédés, Rational design, Lectin, 030104 developmental biology, chemistry, Mutagenesis, biology.protein, Human health and pathology, immunité, [SDV.MHEP]Life Sciences [q-bio]/Human health and pathology, 030215 immunology
Abstract

Adresse actuelle Decout Alexiane : GLYcoDiag, Res Dept, F-45067 Orléans 2, France; The advances in subunit vaccines development have intensified the search for potent adjuvants, particularly adjuvants inducing cell-mediated immune responses. Identification of the C-type lectin Mincle as one of the receptors underlying the remarkable immunogenicity of the mycobacterial cell wall, via recognition of trehalose-6,6'-dimycolate (TDM), has opened avenues for the rational design of such molecules. Using a combination of chemical synthesis, biological evaluation, molecular dynamics simulations, and protein mutagenesis, we gained insight into the molecular bases of glycolipid recognition by Mincle. Unexpectedly, the fine structure of the fatty acids was found to play a key role in the binding of a glycolipid to the carbohydrate recognition domain of the lectin. Glucose and mannose esterified at O-6 by a synthetic alpha-ramified 32-carbon fatty acid showed agonist activity similar to that of TDM, despite their much simpler structure. Moreover, they were seen to stimulate proinflammatory cytokine production in primary human and murine cells in a Mincle-dependent fashion. Finally, they were found to induce strong Th1 and Th17 immune responses in vivo in immunization experiments in mice and conferred protection in a murine model of Mycobacterium tuberculosis infection. Here we describe the rational development of new molecules with powerful adjuvant properties.

Published
2017

45. Novel product specificity toward erlose and panose exhibited by multisite engineered mutants of amylosucrase

Author

Magali Remaud-Simeon, Sophie Barbe, Isabelle André, Emmanuelle Cambon, Claire Moulis, and Alizée Vergès

Subjects
0301 basic medicine, chemistry.chemical_classification, biology, Chemistry, Stereochemistry, Mutant, Active site, Protein engineering, 010402 general chemistry, 01 natural sciences, Biochemistry, 0104 chemical sciences, PANOSE, Bacterial protein, 03 medical and health sciences, Amylosucrase, 030104 developmental biology, Glucosyltransferases, Enzyme, biology.protein, Molecular Biology
Abstract

A computer-aided engineering approach recently enabled to deeply reshape the active site of N. polysaccharea amylosucrase for recognition of non-natural acceptor substrates. Libraries of variants were constructed and screened on sucrose allowing the identification of 17 mutants able to synthesize molecules from sole sucrose, which are not synthesized by the parental wild-type enzyme. Three of the isolated mutants as well as the new products synthesized were characterized in details. Mutants contain between 7 and 11 mutations in the active site and the new molecules were identified as being a sucrose derivative, named erlose (α-d-glucopyranosyl-(1→4)-α-d-glucopyranosyl-(1→2)-β-d-Fructose), and a new malto-oligosaccharide named panose (α-d-glucopyranosyl-(1→6)-α-d-glucopyranosyl-(1→4)-α-d-Glucose). These product specificities were never reported for none of the amylosucrases characterized to date, nor their engineered variants. Optimization of the production of these trisaccharides of potential interest as sweeteners or prebiotic molecules was carried out. Molecular modelling studies were also performed to shed some light on the molecular factors involved in the novel product specificities of these amylosucrase variants.

Published
2017

46. 'The war of the worlds won't occur': Decentralized evaluation systems and orders of worth in market organizations of the sharing economy

Author

Caroline Hussler, Anne-Sophie Barbe, Laboratoire de Recherche Magellan, Université Jean Moulin - Lyon 3 (UJML), and Université de Lyon-Université de Lyon-Institut d'Administration des Entreprises (IAE) - Lyon

Subjects
Pragmatism, Evaluation system, 020209 energy, media_common.quotation_subject, 05 social sciences, 02 engineering and technology, Mathematical proof, Market organization, [SHS]Humanities and Social Sciences, Spanish Civil War, Sharing economy, Pluralism (political theory), Management of Technology and Innovation, 0502 economics and business, 0202 electrical engineering, electronic engineering, information engineering, [SHS.GESTION]Humanities and Social Sciences/Business administration, Business and International Management, Economic system, 050203 business & management, Applied Psychology, ComputingMilieux_MISCELLANEOUS, media_common
Abstract

Market organizations of the sharing economy have gained economic and social prominence. Most of these market organizations incorporate decentralized evaluation systems into their digital platform. However, these market organizations involve users and organizers with different ideas on what is valuable and what is not in sharing-economy transactions. Building on neo-institutional insights coupled with French pragmatist sociology, we investigate the role of decentralized evaluation systems in supporting/solving the co-existence and/or competition of several conceptions of worth in market organizations of the sharing economy. In a case study on the French peer-to-peer carpooling platform BlaBlaCar, we show that its decentralized evaluation system embodies the organizer's conception of what is valuable and only supports limited pluralism among users. We argue that incorporating a decentralized evaluation system in the digital platform of a market organization is not neutral: certain users prefer to self-exclude rather than to use the system to discriminate between potential future interlocutors. Although evaluations do not incorporate imposed criteria, they constitute proofs of worth in some worlds but not in others. Finally, this evaluation system creates room for several market organizations to co-exist.

Published
2019

47. Molecular study of hydrolysis/transglycosylation modulation in retaining glycoside hydrolases

Author

Jiao Zhao, Tobias Tandrup, Bastien Bissaro, Sophie Barbe, Isabelle André, Claire Dumon, Leila Lo Leggio, Donohue, Michael O., Régis Fauré, Laboratoire d'Ingénierie des Systèmes Biologiques et des Procédés (LISBP), Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de la Recherche Agronomique (INRA), University of Copenhagen = Københavns Universitet (KU), Biodiversité et Biotechnologie Fongiques (BBF), Aix Marseille Université (AMU)-Institut National de la Recherche Agronomique (INRA)-École Centrale de Marseille (ECM), Institut National de la Recherche Agronomique (INRA)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS), University of Copenhagen = Københavns Universitet (UCPH), and Institut National de la Recherche Agronomique (INRA)-Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)

Subjects
[SDV.BIO]Life Sciences [q-bio]/Biotechnology, ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2019

48. Positive multistate protein design

Author

Manon Ruffini, Sophie Barbe, Jelena Vucinic, Thomas Schiex, David Simoncini, Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRAE), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Toulouse Biotechnology Institute (TBI), Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Real Expression Artificial Life (IRIT-REVA), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT)-Université de Toulouse (UT)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Toulouse Mind & Brain Institut (TMBI), Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT), Contrat REGION doctorant TOULOUZETA n° 00000950-CT15000449, DAS-SAB, ANR-19-P3IA-0004,ANITI,Artificial and Natural Intelligence Toulouse Institute(2019), ANR-16-CE40-0028,DE-MO-GRAPH,Décomposition de Modèles Graphiques(2016), Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse 1 Capitole (UT1), and Université Fédérale Toulouse Midi-Pyrénées

Subjects
Statistics and Probability, Mathematical optimization, Computer science, Protein Conformation, [SDV]Life Sciences [q-bio], Protein design, Protein Engineering, 01 natural sciences, Biochemistry, 03 medical and health sciences, 0103 physical sciences, [SDV.IDA]Life Sciences [q-bio]/Food engineering, [INFO]Computer Science [cs], [SPI.GPROC]Engineering Sciences [physics]/Chemical and Process Engineering, Amino Acid Sequence, [MATH]Mathematics [math], Molecular Biology, 030304 developmental biology, chemistry.chemical_classification, 0303 health sciences, 010304 chemical physics, Computational Biology, Proteins, Protein engineering, Computer Science Applications, Amino acid, Computational Mathematics, Computational Theory and Mathematics, chemistry, Computational problem, Algorithms, Software
Abstract

Motivation Structure-based computational protein design (CPD) plays a critical role in advancing the field of protein engineering. Using an all-atom energy function, CPD tries to identify amino acid sequences that fold into a target structure and ultimately perform a desired function. The usual approach considers a single rigid backbone as a target, which ignores backbone flexibility. Multistate design (MSD) allows instead to consider several backbone states simultaneously, defining challenging computational problems. Results We introduce efficient reductions of positive MSD problems to Cost Function Networks with two different fitness definitions and implement them in the Pompd (Positive Multistate Protein design) software. Pompd is able to identify guaranteed optimal sequences of positive multistate full protein redesign problems and exhaustively enumerate suboptimal sequences close to the MSD optimum. Applied to nuclear magnetic resonance and back-rubbed X-ray structures, we observe that the average energy fitness provides the best sequence recovery. Our method outperforms state-of-the-art guaranteed computational design approaches by orders of magnitudes and can solve MSD problems with sizes previously unreachable with guaranteed algorithms. Availability and implementation https://forgemia.inra.fr/thomas.schiex/pompd as documented Open Source. Supplementary information Supplementary data are available at Bioinformatics online.

Published
2019

49. An Atomistic Statistically Effective Energy Function for Computational Protein Design

Author

Isabelle André, Christopher M. Topham, Sophie Barbe, Laboratoire d'Ingénierie des Systèmes Biologiques et des Procédés (LISBP), Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de la Recherche Agronomique (INRA), We gratefully acknowledge access granted to the HPC resources of the Midi-Pyrenees Regional Computing Centre (CALMIP, Toulouse, France)., ANR-12-MONU-0015,ProtiCAD,Modèles multi-physiques et algorithmes robotiques pour la conception assistée par ordinateur de protéines(2012), French National Research Agency (ANR Project PROTICAD) [ANR-12-MONU-0015-03] Funding Text : This work was supported by the French National Research Agency (ANR Project PROTICAD, ANR-12-MONU-0015-03). We gratefully acknowledge access granted to the HPC resources of the Midi-Pyrenees Regional Computing Centre (CALMIP, Toulouse, France)., Institut National de la Recherche Agronomique (INRA)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), and Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)

Subjects
0301 basic medicine, Protein Folding, Protein Conformation, Globular protein, [SDV]Life Sciences [q-bio], Protein domain, Protein design, Ligands, globular-proteins, 010402 general chemistry, Polymorphism, Single Nucleotide, 01 natural sciences, Accessible surface area, Viral Proteins, 03 medical and health sciences, Superoxide Dismutase-1, Protein structure, Atom, scoring function, data-bank, Bacteriophage T4, Humans, Statistical physics, Physical and Theoretical Chemistry, Databases, Protein, knowledge-based potentials, Protein Unfolding, ligand-binding affinities, chemistry.chemical_classification, Quantitative Biology::Biomolecules, reference state improves, Function (mathematics), structure prediction, 0104 chemical sciences, Computer Science Applications, 030104 developmental biology, chemistry, Mutagenesis, mean force, Thermodynamics, Muramidase, accessible surface-area, Protein folding, Atomic physics, quasi-chemical approximation
Abstract

Shortcomings in the definition of effective free-energy surfaces of proteins are recognized to be a major contributory factor responsible for the low success rates of existing automated methods for computational protein design (CPD). The formulation of an atomistic statistically effective energy function (SEEF) suitable for a wide range of CPD applications and its derivation from structural data extracted from protein domains and protein-ligand complexes are described here. The proposed energy function comprises nonlocal atom-based and local residue based SEEFs, which are coupled using a novel atom connectivity number factor to scale short-range, pairwise, nonbonded atomic interaction energies and a surface-area-dependent cavity energy term. This energy function was used to derive additional SEEFs describing the unfolded-state ensemble of any given residue sequence based on computed average energies for partially or fully solvent-exposed fragments in regions of irregular structure in native proteins. Relative thermal stabilities of 97 T4 bacteriophage lysozyme mutants were predicted from calculated energy differences for folded and unfolded states with an average unsigned error (AUE) of 0.84 kcal mol(-1) when compared to experiment. To demonstrate the utility of the energy function for CPD, further validation was carried out in tests of its capacity to recover cognate protein sequences and to discriminate native and near-native protein folds, loop conformers, and small-molecule ligand binding poses from non-native benchmark decoys. Experimental ligand binding free energies for a diverse set of 80 protein complexes could be predicted with an AUE of 2.4 kcal mol(-1) using an additional energy term to account for the loss in ligand configurational entropy upon binding. The atomistic SEEF is expected to improve the accuracy of residue-based coarse-grained SEEFs currently used in CPD and to extend the range of applications of extant atom-based protein statistical potentials.

Published
2016

50. Constraint Programming and Graphical models - Pushing data into your models, The protein design case

Author

Thomas Schiex, Sophie Barbe, David Simoncini, Jelena Vucinic, Manon Ruffini, David Allouche, Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA), Institut National de la Recherche Agronomique (INRA), Laboratoire d'Ingénierie des Systèmes Biologiques et des Procédés (LISBP), Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de la Recherche Agronomique (INRA), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse 1 Capitole (UT1)-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées, Institut National de la Recherche Agronomique (INRA)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS), Real Expression Artificial Life (IRIT-REVA), Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse 1 Capitole (UT1), contrat REGION doctorant TOULOUZETA n° 00000950-CT15000449, Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS), Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT)-Université de Toulouse (UT)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Toulouse Mind & Brain Institut (TMBI), Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université Toulouse Capitole (UT Capitole), and Université de Toulouse (UT)

Subjects
[SDV]Life Sciences [q-bio]
Abstract

Constraint Programming and Graphical models - Pushing data into your models, The protein design case.. 23rd International Symposium on Mathematical Programming (ISMP-18)

Published
2018

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