Cargando…

Identification and characterization of the LRR repeats in plant LRR-RLKs

BACKGROUND: Leucine-rich-repeat receptor-like kinases (LRR-RLKs) play central roles in sensing various signals to regulate plant development and environmental responses. The extracellular domains (ECDs) of plant LRR-RLKs contain LRR motifs, consisting of highly conserved residues and variable residu...

Descripción completa

Detalles Bibliográficos
Autor principal: Chen, Tianshu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7841916/
https://www.ncbi.nlm.nih.gov/pubmed/33509084
http://dx.doi.org/10.1186/s12860-021-00344-y
_version_ 1783643905187643392
author Chen, Tianshu
author_facet Chen, Tianshu
author_sort Chen, Tianshu
collection PubMed
description BACKGROUND: Leucine-rich-repeat receptor-like kinases (LRR-RLKs) play central roles in sensing various signals to regulate plant development and environmental responses. The extracellular domains (ECDs) of plant LRR-RLKs contain LRR motifs, consisting of highly conserved residues and variable residues, and are responsible for ligand perception as a receptor or co-receptor. However, there are few comprehensive studies on the ECDs of LRR-RLKs due to the difficulty in effectively identifying the divergent LRR repeats. RESULTS: In the current study, an efficient LRR motif prediction program, the “Phyto-LRR prediction” program, was developed based on the position-specific scoring matrix algorithm (PSSM) with some optimizations. This program was trained by 16-residue plant-specific LRR-highly conserved segments (HCS) from LRR-RLKs of 17 represented land plant species and a database containing more than 55,000 predicted LRRs based on this program was constructed. Both the prediction tool and database are freely available at http://phytolrr.com/ for website usage and at http://github.com/phytolrr for local usage. The LRR-RLKs were classified into 18 subgroups (SGs) according to the maximum-likelihood phylogenetic analysis of kinase domains (KDs) of the sequences. Based on the database and the SGs, the characteristics of the LRR motifs in the ECDs of the LRR-RLKs were examined, such as the arrangement of the LRRs, the solvent accessibility, the variable residues, and the N-glycosylation sites, revealing a comprehensive profile of the plant LRR-RLK ectodomains. CONCLUSION: The “Phyto-LRR prediction” program is effective in predicting the LRR segments in plant LRR-RLKs, which, together with the database, will facilitate the exploration of plant LRR-RLKs functions. Based on the database, comprehensive sequential characteristics of the plant LRR-RLK ectodomains were profiled and analyzed. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12860-021-00344-y.
format Online
Article
Text
id pubmed-7841916
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-78419162021-01-28 Identification and characterization of the LRR repeats in plant LRR-RLKs Chen, Tianshu BMC Mol Cell Biol Research Article BACKGROUND: Leucine-rich-repeat receptor-like kinases (LRR-RLKs) play central roles in sensing various signals to regulate plant development and environmental responses. The extracellular domains (ECDs) of plant LRR-RLKs contain LRR motifs, consisting of highly conserved residues and variable residues, and are responsible for ligand perception as a receptor or co-receptor. However, there are few comprehensive studies on the ECDs of LRR-RLKs due to the difficulty in effectively identifying the divergent LRR repeats. RESULTS: In the current study, an efficient LRR motif prediction program, the “Phyto-LRR prediction” program, was developed based on the position-specific scoring matrix algorithm (PSSM) with some optimizations. This program was trained by 16-residue plant-specific LRR-highly conserved segments (HCS) from LRR-RLKs of 17 represented land plant species and a database containing more than 55,000 predicted LRRs based on this program was constructed. Both the prediction tool and database are freely available at http://phytolrr.com/ for website usage and at http://github.com/phytolrr for local usage. The LRR-RLKs were classified into 18 subgroups (SGs) according to the maximum-likelihood phylogenetic analysis of kinase domains (KDs) of the sequences. Based on the database and the SGs, the characteristics of the LRR motifs in the ECDs of the LRR-RLKs were examined, such as the arrangement of the LRRs, the solvent accessibility, the variable residues, and the N-glycosylation sites, revealing a comprehensive profile of the plant LRR-RLK ectodomains. CONCLUSION: The “Phyto-LRR prediction” program is effective in predicting the LRR segments in plant LRR-RLKs, which, together with the database, will facilitate the exploration of plant LRR-RLKs functions. Based on the database, comprehensive sequential characteristics of the plant LRR-RLK ectodomains were profiled and analyzed. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12860-021-00344-y. BioMed Central 2021-01-28 /pmc/articles/PMC7841916/ /pubmed/33509084 http://dx.doi.org/10.1186/s12860-021-00344-y Text en © The Author(s) 2021 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research Article
Chen, Tianshu
Identification and characterization of the LRR repeats in plant LRR-RLKs
title Identification and characterization of the LRR repeats in plant LRR-RLKs
title_full Identification and characterization of the LRR repeats in plant LRR-RLKs
title_fullStr Identification and characterization of the LRR repeats in plant LRR-RLKs
title_full_unstemmed Identification and characterization of the LRR repeats in plant LRR-RLKs
title_short Identification and characterization of the LRR repeats in plant LRR-RLKs
title_sort identification and characterization of the lrr repeats in plant lrr-rlks
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7841916/
https://www.ncbi.nlm.nih.gov/pubmed/33509084
http://dx.doi.org/10.1186/s12860-021-00344-y
work_keys_str_mv AT chentianshu identificationandcharacterizationofthelrrrepeatsinplantlrrrlks