cbsPredict

cbsPredict predicts carbohydrate-binding sites on protein structures by detecting coplanar surface aromatic residues indicative of glycan-binding capability.

Key Features:

• Detection of Aromatic Residues: Identifies clusters of surface aromatic amino acids that serve as signatures of protein–glycan interactions.
• Coplanar Aromatic Motif Screening: Screens Protein Data Bank (PDB) entries for coplanar aromatic motifs on protein surfaces.
• Structural Bioinformatics Approach: Compares structural motifs to those found in known glycan-binding proteins to detect similar or novel carbohydrate-binding sites.
• Gene Ontology (GO) Enrichment Analysis: Associates identified proteins with carbohydrate-related functions via GO enrichment analysis.
• Experimental Validation: Predictions have been validated using cellulose-pulldown assays and mass spectrometry.

Scientific Applications:

• Prediction of Novel Glycan-Binding Proteins: Predicts glycan-binding proteins not present in the original training set by identifying aromatic motif signatures.
• Structural Genomics: Assists annotation of proteins of unknown function by predicting carbohydrate-binding capabilities from structure.
• Identification of Evolutionary Adaptations: Revealed a unique surface motif in the tobacco pathogenesis-related protein PR-5d interpreted as an adaptation for defense against cellulose-containing pathogens.
• Plant Pathogen Defense Mechanisms: Supports investigation of protein adaptations that may confer resistance to pathogens such as Phytophthora species.

Methodology:

Screens PDB entries for coplanar aromatic motifs and compares those motifs with motifs from known glycan-binding proteins to predict potential carbohydrate-binding sites.