Literature DB >> 33174598

LectomeXplore, an update of UniLectin for the discovery of carbohydrate-binding proteins based on a new lectin classification.

François Bonnardel1,2,3, Julien Mariethoz2,3,4, Serge Pérez1, Anne Imberty1, Frédérique Lisacek2,3,4.   

Abstract

Lectins are non-covalent glycan-binding proteins mediating cellular interactions but their annotation in newly sequenced organisms is lacking. The limited size of functional domains and the low level of sequence similarity challenge usual bioinformatics tools. The identification of lectin domains in proteomes requires the manual curation of sequence alignments based on structural folds. A new lectin classification is proposed. It is built on three levels: (i) 35 lectin domain folds, (ii) 109 classes of lectins sharing at least 20% sequence similarity and (iii) 350 families of lectins sharing at least 70% sequence similarity. This information is compiled in the UniLectin platform that includes the previously described UniLectin3D database of curated lectin 3D structures. Since its first release, UniLectin3D has been updated with 485 additional 3D structures. The database is now complemented by two additional modules: PropLec containing predicted β-propeller lectins and LectomeXplore including predicted lectins from sequences of the NBCI-nr and UniProt for every curated lectin class. UniLectin is accessible at https://www.unilectin.eu/.
© The Author(s) 2020. Published by Oxford University Press on behalf of Nucleic Acids Research.

Entities:  

Year:  2020        PMID: 33174598     DOI: 10.1093/nar/gkaa1019

Source DB:  PubMed          Journal:  Nucleic Acids Res        ISSN: 0305-1048            Impact factor:   16.971


  4 in total

1.  Comprehensive analysis of lectin-glycan interactions reveals determinants of lectin specificity.

Authors:  Daniel E Mattox; Chris Bailey-Kellogg
Journal:  PLoS Comput Biol       Date:  2021-10-06       Impact factor: 4.475

2.  LectinOracle: A Generalizable Deep Learning Model for Lectin-Glycan Binding Prediction.

Authors:  Jon Lundstrøm; Emma Korhonen; Frédérique Lisacek; Daniel Bojar
Journal:  Adv Sci (Weinh)       Date:  2021-12-04       Impact factor: 16.806

3.  N-glycosylation of cervicovaginal fluid reflects microbial community, immune activity, and pregnancy status.

Authors:  Gang Wu; Paola Grassi; David A MacIntyre; Belen Gimeno Molina; Lynne Sykes; Samit Kundu; Cheng-Te Hsiao; Kay-Hooi Khoo; Phillip R Bennett; Anne Dell; Stuart M Haslam
Journal:  Sci Rep       Date:  2022-10-10       Impact factor: 4.996

4.  Proteome-wide prediction of bacterial carbohydrate-binding proteins as a tool for understanding commensal and pathogen colonisation of the vaginal microbiome.

Authors:  François Bonnardel; Stuart M Haslam; Anne Dell; Ten Feizi; Yan Liu; Virginia Tajadura-Ortega; Yukie Akune; Lynne Sykes; Phillip R Bennett; David A MacIntyre; Frédérique Lisacek; Anne Imberty
Journal:  NPJ Biofilms Microbiomes       Date:  2021-06-15       Impact factor: 7.290

  4 in total

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