iCarPS

iCarPS predicts protein carbonylation sites from protein sequence information to identify oxidative stress–induced post-translational modifications.

Key Features:

• Target: Predicts protein carbonylation sites, a stable and irreversible post-translational modification formed under oxidative stress.
• Input data: Operates on protein sequence information to identify potential carbonylation residues.
• Feature encoding: Implements residues conical coordinates as a novel encoding scheme that integrates physicochemical properties of proteins.
• Feature selection: Applies a feature selection technique to eliminate redundant features and reduce dimensionality.
• Validation: Reports performance evaluated on both training and independent datasets.
• Benchmarking: Includes comparative analyses demonstrating superior predictive power relative to existing methods.

Scientific Applications:

• Proteome-scale mapping: Enables large-scale analysis of protein carbonylation across proteomes using sequence-based predictions.
• Oxidative stress research: Supports studies of oxidative stress–related protein modifications and their roles in disease.
• Disease mechanism investigation: Assists investigation of disease development and progression linked to protein carbonylation.

Methodology:

Uses protein sequence information encoded via residues conical coordinates that integrate physicochemical properties, applies feature selection to remove redundant features, and validates predictive performance on training and independent datasets with comparative analyses to existing methods.