PeptideRanger

PeptideRanger optimizes synthetic peptide selection for mass spectrometry-based proteomics to improve detection and quantification of low-abundance proteins.

Key Features:

• Physicochemical Property Optimization: Identifies peptides from target proteins that have physicochemical properties favorable for mass spectrometry detection and quantification.
• Machine Learning Integration: Applies a random forest model trained on thousands of MS experiments covering diverse sample types, chromatography setups, and instruments to predict peptide performance.
• Flexibility and Customization: Supports retraining on experiment-specific datasets and provides extensive peptide annotation to enable prioritization and filtering based on selected properties.
• Integration with Public MS Databases: Leverages public mass spectrometry databases to expand training data and peptide evidence for assay development.

Scientific Applications:

• Targeted and semitargeted MS assay development: Prioritizes candidate synthetic peptides to streamline development of targeted and semitargeted mass spectrometry assays.
• Detection and quantification of low-abundance proteins: Enhances peptide selection to improve sensitivity and quantitation of low-abundance proteins in complex biological samples.
• Synthetic peptide library design: Focuses peptide selection to reduce the time and cost associated with generating synthetic peptide libraries.
• Clinical proteomics and biomarker studies: Supports selection of proteotypic peptides relevant for clinical diagnostics and investigations of disease mechanisms.

Methodology:

Uses a random forest model trained on a comprehensive dataset from thousands of MS experiments spanning diverse sample types, chromatography setups, and instruments; scores peptides by physicochemical properties and supports retraining on experiment-specific datasets.