IPPD

IPPD detects and extracts isotopic peak patterns from raw mass spectrometry spectra to improve identification of peptide isotopic envelopes in proteomic analyses.

Key Features:

• Isotope pattern detection: Accurately identifies and extracts isotopic peak patterns from raw mass spectra for peptide analysis in proteomic applications.
• Noise and overlap handling: Differentiates signal from noise and addresses overlapping patterns arising from noise artifacts and post-translational modifications.
• Non-negative regression fitting: Implements non-negative least squares (NNLS) and non-negative least absolute deviation (L1) fitting to model spectral intensities.
• Template-based modeling: Uses templates that mimic isotope patterns to fit and interpret raw mass spectra.
• Overlap disentanglement without regularization: Disentangles overlapping isotopic envelopes without requiring regularization to prevent overfitting.
• Thresholding feature selection: Employs thresholding as a feature selection method as an alternative to regularization-based approaches.
• Validation scheme: Includes a validation scheme demonstrating superior pattern picking performance.
• Interpretable parameters: Provides well-interpretable model parameters for spectral pattern analysis.
• Mass spectrometry data support: Applicable to LCMS runs and other mass spectrometry data types used in proteomics.

Scientific Applications:

• Peptide isotopic envelope identification: Extraction and identification of peptide isotope patterns in proteomic mass spectra.
• Handling PTM-induced overlaps: Resolving overlapping isotope patterns caused by post-translational modifications.
• Pattern picking in LCMS data: Automated pattern picking and evaluation in LCMS and other mass spectrometry datasets.

Methodology:

Raw spectra are modeled using templates that mimic isotope patterns and fitted using non-negative least squares (NNLS) and non-negative least absolute deviation (L1) fitting, with thresholding for feature selection and a validation scheme to assess pattern picking; overlaps are disentangled without regularization.