hmSEEKER: Identification of hmSILAC Doublets in MaxQuant Output Data

Area of Science:

• Proteomics
• Mass Spectrometry
• Biochemistry

Background:

• Heavy methyl Stable Isotope Labeling with Amino acids in Cell culture (hmSILAC) is a key proteomics technique for identifying methylated peptides.
• Accurate identification of heavy and light peak doublets in MS data is crucial but computationally challenging.
• Existing computational methods for hmSILAC data analysis are limited.

Purpose of the Study:

• To develop and present hmSEEKER, a novel software tool for robust identification of methylated peptides in hmSILAC experiments.
• To provide a computational solution that works downstream of MaxQuant analysis.
• To improve the sensitivity and specificity of heavy and light peak doublet detection.

Main Methods:

• hmSEEKER software, written in Perl, analyzes MaxQuant output tables.
• The software performs an in-depth search for MS peak pairs corresponding to light and heavy methyl-peptides.
• Validation of sensitivity and specificity was performed.

Main Results:

• hmSEEKER demonstrates good sensitivity and specificity in identifying methylated peptide peak pairs.
• The software effectively addresses the challenge of automatic and robust doublet identification in hmSILAC MS data.
• It enhances the confidence of global identification of methylated peptides.

Conclusions:

• hmSEEKER offers a valuable computational tool for researchers using hmSILAC in proteomics.
• The software improves the analysis of complex MS data from metabolic labeling experiments.
• Freely available code and manual facilitate adoption and further development.