Benjamin Thomas Viart

Centre National de la Recherche Scientifique

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Sofia Kossida

Institut Universitaire de France

Edgar Ernesto Gonzalez Kozlova

Universidade Federal de Minas Gerais

María Moriel‐Carretero

Université de Montpellier

Carlos Chávez-Olórtegui

Universidade Federal de Minas Gerais

Christophe Nguyen

Institut des Biomolécules Max Mousseron

Ricardo Andrez Machado‐de‐Ávila

Universidade do Extremo Sul Catarinense

Franck Molina

Modélisation et Ingénierie des Systèmes Complexes Biologiques pour le Diagnostic

Anjana Kushwaha

Institut de Génétique Humaine

Loïc Cerf

Universidade Federal de Minas Gerais

Aurélie Préchoux

Station Biologique de Roscoff

Cooperative Institutions

Centre National de la Recherche Scientifique

Université de Montpellier

Institut de Génétique Humaine

Universidade Federal de Minas Gerais

Sorbonne Université

Station Biologique de Roscoff

Laboratoire de Biologie Intégrative des Modèles Marins

Institut de Génétique Moléculaire de Montpellier

Centre National pour la Recherche Scientifique et Technique (CNRST)

Université Paris-Saclay

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EPI-peptide designer: a tool for designing peptide ligand libraries based on epitope–paratope interactions

Bioinformatics (2016)

Benjamin Thomas Viart Camila Dias-Lopes Edgar Ernesto Gonzalez Kozlova Camila Franco Batista Oliveira Christophe Nguyen

Antibodies are an important class of biological drugs, but with limitations, such as inadequate pharmacokinetics, adverse immunogenicity and high production costs. Synthetic peptides for the desired target represent an important alternative to antibodies. However, no computational tool exists to guide the design of these peptides.To identify the interacting residues in a given antibody-antigen (Ab-Ag) interface we used Interface Interacting Residue (I2R), a selection method based on computed molecular interactions. The aggregation of all the molecular interactions between epitope and paratope residues allowed us to transform the 3D Ab-Ag complex structures into interface graphs. Based on these data and the probability of molecular interaction we developed EPI-Peptide Designer tool that uses predicted paratope residues for an epitope of interest to generate targeted peptide ligand libraries. EPI-Peptide Designer successfully predicted 301 peptides able to bind to LiD1 target protein (65% of the experimentally tested peptides), an enrichment of 22% compared to randomly generated peptides. This tool should enable the development of a new generation of synthetic interacting peptides that could be very useful in the biosensor, diagnostic and therapeutic fields.All software developed in this work are available at http://www.biocomp.icb.ufmg.br/biocomp/liza@icb.ufmg.brSupplementary data are available at Bioinformatics online.

Paratope

Epitope mapping

10.1093/bioinformatics/btw014

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Citations (10)

Classification epitopes in groups based on their protein family

BMC Bioinformatics (2015)

Edgar Ernesto Gonzalez Kozlova Benjamin Thomas Viart Ricardo Andrez Machado‐de‐Ávila Liza Figueredo Felicori Carlos Chávez-Olórtegui

The humoral immune system response is based on the interaction between antibodies and antigens for the clearance of pathogens and foreign molecules. The interaction between these proteins occurs at specific positions known as antigenic determinants or B-cell epitopes. The experimental identification of epitopes is costly and time consuming. Therefore the use of in silico methods, to help discover new epitopes, is an appealing alternative due the importance of biomedical applications such as vaccine design, disease diagnostic, anti-venoms and immune-therapeutics. However, the performance of predictions is not optimal been around 70% of accuracy. Further research could increase our understanding of the biochemical and structural properties that characterize a B-cell epitope.We investigated the possibility of linear epitopes from the same protein family to share common properties. This hypothesis led us to analyze physico-chemical (PCP) and predicted secondary structure (PSS) features of a curated dataset of epitope sequences available in the literature belonging to two different groups of antigens (metalloproteinases and neurotoxins). We discovered statistically significant parameters with data mining techniques which allow us to distinguish neurotoxin from metalloproteinase and these two from random sequences. After a five cross fold validation we found that PCP based models obtained area under the curve values (AUC) and accuracy above 0.9 for regression, decision tree and support vector machine.We demonstrated that antigen's family can be inferred from properties within a single group of linear epitopes (metalloproteinases or neurotoxins). Also we discovered the characteristics that represent these two epitope groups including their similarities and differences with random peptides and their respective amino acid sequence. These findings open new perspectives to improve epitope prediction by considering the specific antigen's protein family. We expect that these findings will help to improve current computational mapping methods based on physico-chemical due it's potential application during epitope discovery.

Linear epitope

Reverse vaccinology

10.1186/1471-2105-16-s19-s7

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Citations (13)

Carrageenan catabolism is encoded by a complex regulon in marine heterotrophic bacteria

Nature Communications (2017)

E. Ficko-Blean Aurélie Préchoux François Thomas Tatiana Rochat Robert Larocque

Abstract Macroalgae contribute substantially to primary production in coastal ecosystems. Their biomass, mainly consisting of polysaccharides, is cycled into the environment by marine heterotrophic bacteria using largely uncharacterized mechanisms. Here we describe the complete catabolic pathway for carrageenans, major cell wall polysaccharides of red macroalgae, in the marine heterotrophic bacterium Zobellia galactanivorans . Carrageenan catabolism relies on a multifaceted carrageenan-induced regulon, including a non-canonical polysaccharide utilization locus (PUL) and genes distal to the PUL, including a susCD -like pair. The carrageenan utilization system is well conserved in marine Bacteroidetes but modified in other phyla of marine heterotrophic bacteria. The core system is completed by additional functions that might be assumed by non-orthologous genes in different species. This complex genetic structure may be the result of multiple evolutionary events including gene duplications and horizontal gene transfers. These results allow for an extension on the definition of bacterial PUL-mediated polysaccharide digestion.

Regulon

Catabolism

10.1038/s41467-017-01832-6

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Citations (145)

IMGT® databases, related tools and web resources through three main axes of research and development

Nucleic Acids Research (2021)

Taciana Manso Géraldine Folch Véronique Giudicelli Joumana Jabado-Michaloud Anjana Kushwaha

Abstract IMGT®, the international ImMunoGeneTics information system®, http://www.imgt.org/, is at the forefront of the immunogenetics and immunoinformatics fields with more than 30 years of experience. IMGT® makes available databases and tools to the scientific community pertaining to the adaptive immune response, based on the IMGT-ONTOLOGY. We focus on the recent features of the IMGT® databases, tools, reference directories and web resources, within the three main axes of IMGT® research and development. Axis I consists in understanding the adaptive immune response, by deciphering the identification and characterization of the immunoglobulin (IG) and T cell receptor (TR) genes in jawed vertebrates. It is the starting point of the two other axes, namely the analysis and exploration of the expressed IG and TR repertoires based on comparison with IMGT reference directories in normal and pathological situations (Axis II) and the analysis of amino acid changes and functions of 2D and 3D structures of antibody and TR engineering (Axis III).

10.1093/nar/gkab1136

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Citations (51)

Computational B-cell epitope identification and production of neutralizing murine antibodies against Atroxlysin-I

Scientific Reports (2018)

Edgar Ernesto Gonzalez Kozlova Loïc Cerf Francisco Santos Schneider Benjamin Thomas Viart Christophe Nguyen

Epitope identification is essential for developing effective antibodies that can detect and neutralize bioactive proteins. Computational prediction is a valuable and time-saving alternative for experimental identification. Current computational methods for epitope prediction are underused and undervalued due to their high false positive rate. In this work, we targeted common properties of linear B-cell epitopes identified in an individual protein class (metalloendopeptidases) and introduced an alternative method to reduce the false positive rate and increase accuracy, proposing to restrict predictive models to a single specific protein class. For this purpose, curated epitope sequences from metalloendopeptidases were transformed into frame-shifted Kmers (3 to 15 amino acid residues long). These Kmers were decomposed into a matrix of biochemical attributes and used to train a decision tree classifier. The resulting prediction model showed a lower false positive rate and greater area under the curve when compared to state-of-the-art methods. Our predictions were used for synthesizing peptides mimicking the predicted epitopes for immunization of mice. A predicted linear epitope that was previously undetected by an experimental immunoassay was able to induce neutralizing-antibody production in mice. Therefore, we present an improved prediction alternative and show that computationally identified epitopes can go undetected during experimental mapping.

Linear epitope

10.1038/s41598-018-33298-x

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Citations (30)

PickPocket : Pocket binding prediction for specific ligands family using neural networks.

bioRxiv (Cold Spring Harbor Laboratory) (2020)

Benjamin Thomas Viart Claudio Lorenzi María Moriel‐Carretero Sofia Kossida

Most of the protein biological functions occur through contacts with other proteins or ligands. The residues that constitute the contact surface of a ligand-binding pocket are usually located far away within its sequence. Therefore, the identification of such motifs is more challenging than the linear protein domains. To discover new binding sites, we developed a tool called PickPocket that focuses on a small set of user-defined ligands and uses neural networks to train a ligand-binding prediction model. We tested PickPocket on fatty acid-like ligands due to their structural similarities and their under-representation in the ligand-pocket binding literature. Our results show that for fatty acid-like molecules, pocket descriptors and secondary structures are enough to obtain predictions with accuracy >90% using a dataset of 1740 manually curated ligand-binding pockets. The trained model could also successfully predict the ligand-binding pockets using unseen structural data of two recently reported fatty acid-binding proteins. We think that the PickPocket tool can help to discover new protein functions by investigating the binding sites of specific ligand families. The source code and all datasets contained in this work are freely available at https://github.com/benjaminviart/PickPocket .

Identification

Structural bioinformatics

Representation

Protein ligand

10.1101/2020.04.15.042655

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Citations (2)

PyFuncover: full proteome search for a specific function using BLAST and PFAM

EMBnet journal (2019)

Yoan Bouzin Benjamin Thomas Viart María Moriel‐Carretero Sofia Kossida

Python Function uncover (PyFuncover) is a new bioinformatic tool able to search proteins with a specific function in a full proteome. The pipeline coded in python uses BLAST alignment and the sequences from a PFAM family as the search seed. We tested PyFuncover using the fatty acid-binding family (FABP) Lipocalin_7 from PFAM (version 32, 2019) against the Homo sapiens NCBI proteome. After applying the scoring function in all the BLAST results, the data were classified and submitted to a GO-TERM analysis using bioDBnet. Analyses showed that all families of FABPs were ranked within the top scores. Included within this category were also families able to bind to hydrophobic molecules similar to fatty acids such as the retinol acid transporter and the cellular retinoic acid-binding protein.

Proteome

Protein family

Human proteome project

Python

Retinol binding protein

10.14806/ej.24.0.925

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Citations (2)

Lipid Droplets Are a Physiological Nucleoporin Reservoir

Cells (2021)

Sylvain Kumanski Benjamin Thomas Viart Sofia Kossida María Moriel‐Carretero

Lipid Droplets (LD) are dynamic organelles that originate in the Endoplasmic Reticulum and mostly bud off toward the cytoplasm, where they store neutral lipids for energy and protection purposes. LD also have diverse proteins on their surface, many of which are necessary for the their correct homeostasis. However, these organelles also act as reservoirs of proteins that can be made available elsewhere in the cell. In this sense, they act as sinks that titrate key regulators of many cellular processes. Among the specialized factors that reside on cytoplasmic LD are proteins destined for functions in the nucleus, but little is known about them and their impact on nuclear processes. By screening for nuclear proteins in publicly available LD proteomes, we found that they contain a subset of nucleoporins from the Nuclear Pore Complex (NPC). Exploring this, we demonstrate that LD act as a physiological reservoir, for nucleoporins, that impacts the conformation of NPCs and hence their function in nucleo-cytoplasmic transport, chromatin configuration, and genome stability. Furthermore, our in silico modeling predicts a role for LD-released fatty acids in regulating the transit of nucleoporins from LD through the cytoplasm and to nuclear pores.

Nucleoporin

Nuclear pore

Organelle

Transport protein

Lipid droplet

Cellular compartment

10.3390/cells10020472

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Citations (15)