Next Article in Journal
The Role of Polycomb Repressive Complex in Malignant Peripheral Nerve Sheath Tumor
Next Article in Special Issue
NLGenomeSweeper: A Tool for Genome-Wide NBS-LRR Resistance Gene Identification
Previous Article in Journal
Heterologous Expression of SvMBD5 from Salix viminalis L. Promotes Flowering in Arabidopsis thaliana L.
Previous Article in Special Issue
Population Genetics of the Highly Polymorphic RPP8 Gene Family
Article

LRRpredictor—A New LRR Motif Detection Method for Irregular Motifs of Plant NLR Proteins Using an Ensemble of Classifiers

1
Department of Bioinformatics and Structural Biochemistry, Institute of Biochemistry of the Romanian Academy, Splaiul Independentei 296, 060031 Bucharest, Romania
2
Laboratory of Nematology, Wageningen University and Research, 6700ES Wageningen, The Netherlands
3
Space Comp SRL, 041512 Bucharest, Romania
*
Authors to whom correspondence should be addressed.
Genes 2020, 11(3), 286; https://doi.org/10.3390/genes11030286
Received: 7 February 2020 / Revised: 28 February 2020 / Accepted: 4 March 2020 / Published: 8 March 2020
(This article belongs to the Special Issue NLR Gene Evolution in Plants)
Leucine-rich-repeats (LRRs) belong to an archaic procaryal protein architecture that is widely involved in protein–protein interactions. In eukaryotes, LRR domains developed into key recognition modules in many innate immune receptor classes. Due to the high sequence variability imposed by recognition specificity, precise repeat delineation is often difficult especially in plant NOD-like Receptors (NLRs) notorious for showing far larger irregularities. To address this problem, we introduce here LRRpredictor, a method based on an ensemble of estimators designed to better identify LRR motifs in general but particularly adapted for handling more irregular LRR environments, thus allowing to compensate for the scarcity of structural data on NLR proteins. The extrapolation capacity tested on a set of annotated LRR domains from six immune receptor classes shows the ability of LRRpredictor to recover all previously defined specific motif consensuses and to extend the LRR motif coverage over annotated LRR domains. This analysis confirms the increased variability of LRR motifs in plant and vertebrate NLRs when compared to extracellular receptors, consistent with previous studies. Hence, LRRpredictor is able to provide novel insights into the diversification of LRR domains and a robust support for structure-informed analyses of LRRs in immune receptor functioning. View Full-Text
Keywords: leucine-rich repeat prediction; supervised learning; LRR motif; LRR structure; NOD-like receptors; R proteins leucine-rich repeat prediction; supervised learning; LRR motif; LRR structure; NOD-like receptors; R proteins
Show Figures

Figure 1

MDPI and ACS Style

Martin, E.C.; Sukarta, O.C.A.; Spiridon, L.; Grigore, L.G.; Constantinescu, V.; Tacutu, R.; Goverse, A.; Petrescu, A.-J. LRRpredictor—A New LRR Motif Detection Method for Irregular Motifs of Plant NLR Proteins Using an Ensemble of Classifiers. Genes 2020, 11, 286. https://doi.org/10.3390/genes11030286

AMA Style

Martin EC, Sukarta OCA, Spiridon L, Grigore LG, Constantinescu V, Tacutu R, Goverse A, Petrescu A-J. LRRpredictor—A New LRR Motif Detection Method for Irregular Motifs of Plant NLR Proteins Using an Ensemble of Classifiers. Genes. 2020; 11(3):286. https://doi.org/10.3390/genes11030286

Chicago/Turabian Style

Martin, Eliza C., Octavina C.A. Sukarta, Laurentiu Spiridon, Laurentiu G. Grigore, Vlad Constantinescu, Robi Tacutu, Aska Goverse, and Andrei-Jose Petrescu. 2020. "LRRpredictor—A New LRR Motif Detection Method for Irregular Motifs of Plant NLR Proteins Using an Ensemble of Classifiers" Genes 11, no. 3: 286. https://doi.org/10.3390/genes11030286

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop