Using ProtMAX to create high-mass-accuracy precursor alignments from label-free quantitative mass spectrometry data...

Area of Science:

• Biochemistry
• Analytical Chemistry
• Computational Biology

Background:

• Protein quantification using mass spectrometry (MS) is crucial for biological research.
• Current high-throughput methods face computational limitations.
• The Mass Accuracy Precursor Alignment (MAPA) strategy offers robust relative protein quantification.

Purpose of the Study:

• To introduce ProtMAX, a software tool for automated precursor alignment in MS-based protein quantification.
• To address computational bottlenecks in high-sample-throughput quantitative proteomics.
• To provide a protocol for complex quantitative MAPA analysis using ProtMAX.

Main Methods:

• Utilized the Mass Accuracy Precursor Alignment (MAPA) strategy.
• Developed and employed the ProtMAX software tool for automated precursor alignment.
• Incorporated features for 'ion intensity count' and 'target search' of specific peptides.
• Recommended specific MS settings for quantitative MAPA analysis.

Main Results:

• ProtMAX enables automated alignment of precursors from hundreds of MS runs rapidly.
• The software overcomes computational restrictions associated with large-scale quantitative proteomics.
• ProtMAX facilitates high-resolution, sensitive, and high-throughput relative protein quantification.

Conclusions:

• ProtMAX is an effective software solution for efficient and automated protein quantification in mass spectrometry.
• The MAPA strategy, when implemented with ProtMAX, enhances accuracy and throughput for quantitative proteomics.
• This protocol facilitates complex quantitative MAPA analysis, advancing the field of proteomic research.