Improved Monoisotopic Mass Estimation for Deeper Proteome Coverage.

Area of Science:

• Proteomics
• Bioinformatics
• Analytical Chemistry

Background:

• Accurate peptide identification in bottom-up proteomics relies on correct monoisotopic peak assignment.
• Misassigned or inaccurate precursor ion attribution reduces sensitivity and the number of identified peptides.

Purpose of the Study:

• To introduce Monocle, an open-source, cross-platform algorithm for rapid reassignment of precursor peaks to monoisotopic peptide assignments.
• To demonstrate the algorithm's integration capabilities within existing proteomic workflows.

Main Methods:

• Development of a performant algorithm for monoisotopic peak reassignment.
• Integration testing with common proteomic data analysis pipelines.
• Comparative analysis of peptide identification rates with and without Monocle.

Main Results:

• Monocle enables rapid conversion of data from multiple sources.
• Monoisotopic peak assignment using Monocle resulted in up to a twofold increase in peptide identifications compared to analyses without correction.
• A 44% improvement in peptide identifications was observed over previous monoisotopic peak correction algorithms.

Conclusions:

• Monocle is an effective tool for improving peptide identification in bottom-up proteomics.
• The algorithm enhances the sensitivity and accuracy of proteomic analyses.
• Monocle offers a significant advancement over existing monoisotopic peak correction methods.