State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, 163 Xianlin Ave, Nanjing, 210046, China.
BMC Mol Cell Biol. 2021 Jan 28;22(1):9. doi: 10.1186/s12860-021-00344-y.
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.
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.
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.
富含亮氨酸重复序列的受体样激酶（LRR-RLKs）在感知各种信号以调节植物发育和环境响应方面发挥着核心作用。植物 LRR-RLK 的细胞外结构域（ECDs）包含 LRR 基序，由高度保守残基和可变残基组成，负责作为受体或共受体感知配体。然而，由于有效识别分歧 LRR 重复序列的困难，对 LRR-RLK 的 ECDs 进行的综合研究较少。
在本研究中，基于位置特异性评分矩阵算法（PSSM）并进行了一些优化，开发了一种有效的 LRR 基序预测程序，即“Phyto-LRR prediction”程序。该程序由来自 17 种代表性陆地植物物种的 LRR-RLK 的 16 残基植物特异性 LRR 高度保守片段（HCS）和基于该程序构建的包含超过 55000 个预测 LRR 的数据库进行训练。预测工具和数据库均可在 http://phytolrr.com/ 网站上免费使用，也可在 http://github.com/phytolrr 上本地使用。根据序列的激酶结构域（KDs）的最大似然系统发育分析，将 LRR-RLK 分为 18 个亚组（SGs）。基于数据库和 SGs，检查了 LRR-RLKs ECDs 中 LRR 基序的特征，如 LRR 的排列、溶剂可及性、可变残基和 N-糖基化位点，揭示了植物 LRR-RLK 胞外结构域的综合特征。
“Phyto-LRR prediction”程序可有效预测植物 LRR-RLK 中的 LRR 片段，该程序与数据库一起将有助于探索植物 LRR-RLK 的功能。基于数据库，对植物 LRR-RLK 胞外结构域的综合序列特征进行了分析和分析。
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