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.

中文翻译：

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植物 LRR-RLKs 中 LRR 重复序列的鉴定和表征

富含亮氨酸的重复受体样激酶 (LRR-RLKs) 在感知各种信号以调节植物发育和环境反应方面发挥核心作用。植物 LRR-RLK 的胞外域 (ECD) 包含 LRR 基序，由高度保守的残基和可变残基组成，负责作为受体或共受体的配体感知。然而，由于难以有效识别不同的 LRR 重复序列，因此很少有关于 LRR-RLKs 的 ECD 的综合研究。在当前的研究中，基于位置特定评分矩阵算法 (PSSM) 并进行了一些优化，开发了一种高效的 LRR 基序预测程序，即“Phyto-LRR 预测”程序。该程序由来自 17 种代表性陆地植物物种的 LRR-RLK 的 16 个残留植物特异性 LRR 高度保守片段 (HCS) 进行训练，并基于该程序构建了一个包含超过 55,000 个预测 LRR 的数据库。预测工具和数据库都可以在 http://phytolrr.com/ 上免费获得供网站使用，在 http://github.com/phytolrr 上供本地使用。根据序列激酶结构域 (KDs) 的最大似然系统发育分析，LRR-RLKs 被分为 18 个亚组 (SGs)。基于数据库和 SG，研究了 LRR-RLKs 的 ECD 中 LRR 基序的特征，例如 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 胞外域的综合概况。“Phyto-LRR预测”程序可有效预测植物LRR-RLKs中的LRR片段，与数据库一起将有助于探索植物LRR-RLKs功能。基于该数据库，对植物LRR-RLK胞外域的综合序列特征进行了剖析和分析。