Phyto-LRR

Phyto-LRR predicts and characterizes leucine-rich repeat (LRR) motifs in the extracellular domains of plant leucine-rich repeat receptor-like kinases (LRR-RLKs) to enable analysis of ligand perception and ectodomain features relevant to development and environmental responses.

Key Features:

• PSSM-based prediction: Uses a position-specific scoring matrix (PSSM) algorithm with optimizations to improve detection of divergent LRR repeats.
• Plant-specific training dataset: Trained on 16-residue plant-specific LRR-highly conserved segments (HCS) derived from LRR-RLKs across 17 representative land plant species.
• Large predicted LRR repository: Produces and aggregates a database containing over 55,000 predicted LRRs.
• LRR-RLK subgroup classification: Classifies LRR-RLKs into 18 subgroups based on maximum-likelihood phylogenetic analysis of kinase domains.
• Ectodomain motif profiling: Profiles LRR motif arrangement, solvent accessibility, variable residues, and N-glycosylation sites within ectodomains.

Scientific Applications:

• LRR motif identification: Identification and characterization of plant LRR motifs to investigate ligand perception and receptor/co-receptor function.
• Comparative and evolutionary analysis: Comparative analysis of LRR repeats across 17 land plant species and phylogenetic classification of LRR-RLK families.
• Motif mining and sequence resource: Use of the >55,000 predicted LRRs database for motif mining, sequence analysis, and hypothesis generation.
• Ectodomain structural/functional inference: Analysis of motif arrangement, solvent accessibility, variable residues, and N-glycosylation to infer ectodomain structural and functional properties.

Methodology:

Prediction is performed using a PSSM algorithm with optimizations and was trained on 16-residue plant-specific LRR-HCS from 17 land plant species; subgroup classification is based on maximum-likelihood phylogenetic analysis of kinase domains.