Proteoform identification based on top-down tandem mass spectra with peak error corrections

Area of Science:

• Computational Biology
• Proteomics
• Bioinformatics

Background:

• Identifying complex proteoforms from mass spectrometry data is challenging due to the combinatorial complexity of protein alterations.
• Existing methods for proteoform identification often struggle with mass errors in spectral data, leading to inaccurate alignments.
• Previous approaches using simple error tolerance can result in significant error accumulation during alignment.

Purpose of the Study:

• To develop a novel computational model for accurate proteoform identification from top-down tandem mass spectrometry data.
• To address the limitations of existing methods in handling mass errors and combinatorial complexity in proteoform analysis.
• To improve the accuracy and efficiency of aligning proteoform mass graphs (PMGs) with spectrum mass graphs (SMGs).

Main Methods:

• Formulated the proteoform identification problem as an alignment problem between a proteoform mass graph (PMG) and a spectrum mass graph (SMG).
• Proposed a new model to handle mass errors by allowing predefined error ranges for spectral peaks and enforcing consistent mass differences between matched nodes.
• Designed algorithms, including a diagonal alignment approach, to efficiently find maximum matched node and peak pairs between PMG and SMG.

Main Results:

• The new model accurately corrects peak masses within predefined error ranges, ensuring consistent mass differences across alignments.
• Experimental results demonstrate that the proposed algorithms achieve significantly higher numbers of matched node pairs compared to existing methods.
• The diagonal alignment algorithm effectively handles large-scale datasets, providing reasonable computation time and memory usage.

Conclusions:

• The developed model and algorithms offer a more accurate and robust approach to identifying complex proteoforms from mass spectrometry data.
• The method's ability to handle mass errors and improve alignment accuracy has significant implications for proteomic research.
• The publicly available software and datasets facilitate further research and application in proteoform discovery.