Ragp

Ragp: Hydroxyproline-Rich Glycoprotein Identification and Annotation in Plants

Ragp analyzes plant protein sequences to identify and characterize hydroxyproline-rich glycoproteins (HRGPs) using hydroxyproline-aware filtering, proline hydroxylation prediction, motif analysis, and domain-based annotation.

Key Features:

• Hydroxyproline-Aware Filtering: Filters sequences based on hydroxyproline residues representing glycosylation sites characteristic of HRGPs.
• Proline Hydroxylation Prediction: Applies machine learning models to estimate proline hydroxylation probability in protein sequences.
• Motif and Bias Analysis: Detects amino acid motifs and compositional biases associated with diverse HRGP subclasses.
• Protein Feature Prediction: Predicts N-terminal signal peptides, transmembrane regions, Pfam domains, glycosylphosphatidylinositol attachment sites, and intrinsically disordered regions.
• Sequence Annotation and Functional Enrichment: Uses hmmscan for domain annotation and performs Gene Ontology (GO) enrichment based on predicted Pfam domains.

Scientific Applications:

• Arabinogalactan Protein Analysis: Identifies and characterizes arabinogalactan proteins and other HRGP subclasses to investigate structural diversity and functional roles in plant biology.
• High-Throughput HRGP Mining: Enables large-scale screening and comparative analysis of HRGPs from plant sequence datasets.

Methodology:

Ragp implements a multi-step pipeline comprising hydroxyproline-aware sequence filtering, machine learning-based proline hydroxylation prediction, motif and compositional bias analysis, and protein feature annotation. Domain annotation is performed using hmmscan against Pfam profiles, followed by Gene Ontology enrichment analysis based on predicted domains.