Identification and characterization of the LRR repeats in plant LRR-RLKs
Abstract 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 variabl...
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BMC
2021-01-01
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Online Access: | https://doi.org/10.1186/s12860-021-00344-y |
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author | Tianshu Chen |
author_facet | Tianshu Chen |
author_sort | Tianshu Chen |
collection | DOAJ |
description | Abstract 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. |
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issn | 2661-8850 |
language | English |
last_indexed | 2024-12-14T23:39:55Z |
publishDate | 2021-01-01 |
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series | BMC Molecular and Cell Biology |
spelling | doaj.art-e561f7e2b4fd40a7ad8a097dd6edfb962022-12-21T22:43:33ZengBMCBMC Molecular and Cell Biology2661-88502021-01-0122111610.1186/s12860-021-00344-yIdentification and characterization of the LRR repeats in plant LRR-RLKsTianshu Chen0State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing UniversityAbstract 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.https://doi.org/10.1186/s12860-021-00344-yPlant LRR-RLKsN-glycosylationLigand bindingLRR motif predictionPSSM |
spellingShingle | Tianshu Chen Identification and characterization of the LRR repeats in plant LRR-RLKs BMC Molecular and Cell Biology Plant LRR-RLKs N-glycosylation Ligand binding LRR motif prediction PSSM |
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 | Plant LRR-RLKs N-glycosylation Ligand binding LRR motif prediction PSSM |
url | https://doi.org/10.1186/s12860-021-00344-y |
work_keys_str_mv | AT tianshuchen identificationandcharacterizationofthelrrrepeatsinplantlrrrlks |