A graph-based approach for proteoform identification and quantification using top-down homogeneous multiplexed tandem...

Area of Science:

• Proteomics
• Mass Spectrometry
• Computational Biology

Background:

• Top-down homogeneous multiplexed tandem mass spectrometry (HomMTM) generates spectra from protein variants with diverse post-translational modifications.
• Ultramodified proteins often yield proteoforms with similar molecular weights, challenging separation by liquid chromatography in mass spectrometry.
• This similarity complicates the accurate identification and quantification of distinct proteoforms.

Purpose of the Study:

• To develop a computational method for identifying and quantifying proteoforms from top-down HomMTM spectra.
• To address the challenge of distinguishing proteoforms with similar molecular weights in mass spectrometry data.
• To improve the analysis of complex and ultramodified proteins.

Main Methods:

• Formulated the proteoform identification problem as a minimum error k-splittable flow problem on graphs.
• Developed a graph-based algorithm for analyzing top-down HomMTM spectra.
• Applied the algorithm to identify and quantify proteoforms.

Main Results:

• The proposed graph-based algorithm successfully identified proteoforms from top-down HomMTM spectra.
• The method demonstrated effectiveness in distinguishing between proteoforms with similar molecular weights.
• Experiments showed the algorithm's ability to identify proteoform pairs that better explain spectral data.

Conclusions:

• The developed graph-based method provides a robust approach for proteoform identification and quantification using top-down HomMTM spectra.
• The findings suggest improved accuracy in analyzing complex protein modifications and identifying co-existing proteoforms.
• This advancement aids in a more comprehensive understanding of proteoform heterogeneity in biological systems.