Systematic Evaluation of Protein Sequence Filtering Algorithms for Proteoform Identification Using Top-Down Mass...

Area of Science:

• Proteomics
• Computational Biology
• Biochemistry

Background:

• Complex proteoforms exhibit structural variations due to genetic, RNA, and protein alterations.
• Top-down mass spectrometry is crucial for analyzing intact proteoforms, offering complete sequence information.
• Identifying proteoforms with unknown modifications computationally is challenging due to the vast search space.

Purpose of the Study:

• To develop and evaluate efficient approximate spectrum-based filtering algorithms for proteoform identification.
• To improve the speed and accuracy of high-throughput proteome-level analyses using top-down mass spectrometry.
• To enhance the identification of complex proteoforms, including those missed by existing software.

Main Methods:

• Proposed two novel approximate spectrum-based filtering algorithms.
• Evaluated algorithm performance against existing methods using simulated and real top-down mass spectrometry data.
• Integrated proposed filtering algorithms with mass graph alignment for comprehensive proteoform analysis.

Main Results:

• The proposed filtering algorithms demonstrated superior performance compared to existing methods for complex proteoform identification.
• The combined approach identified numerous proteoforms missed by the ProSightPC software in proteome-level analyses.
• Experiments confirmed the efficiency and sensitivity of the new algorithms on diverse datasets.

Conclusions:

• The developed algorithms offer a significant advancement in computational proteoform identification.
• These methods enhance the capabilities of top-down mass spectrometry for large-scale proteomic studies.
• The improved filtering strategies facilitate more comprehensive and accurate proteome analysis.