Journal
GENES
Volume 11, Issue 3, Pages -Publisher
MDPI
DOI: 10.3390/genes11030286
Keywords
leucine-rich repeat prediction; supervised learning; LRR motif; LRR structure; NOD-like receptors; R proteins
Categories
Funding
- Ministry of Research and Innovation, CNCS -UEFISCDI [PN-III-ID-PCE-2016-0650, PN-III-P1-1.1-TE-2016-1852]
- National Authority for Scientific Research and Innovation
- Ministry of European Funds, through the Competitiveness Operational Programme 2014-2020, POC-A.1-A.1.1.4-E-2015 [40/02.09.2016, P_37_778]
- Dutch Technology Foundation STW
- Romanian Academy program 1 of IBAR
- Romanian Academy program 2 of IBAR
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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.
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