Enhanced sequence coverage, better identification of combinatorial co-occurring PTMs and improved detection of proteoforms are key highlights of middle-down approach and hence, this can be a promiscuous approach for protein sequencing and proteomics.

Background: Middle-down (MD) proteomics is an emerging approach for reliable identification of post- translational modifications and isoforms, as this approach focuses on proteolytic peptides containing > 25 - 30 amino acid residues (a.a.r.), which are longer than typical tryptic peptides. Such longer peptides can be obtained by AspN, GluC, LysC proteases. Additionally, some special proteases were developed specifically to effect MD approach, e.g., OmpT, Sap9, etc. However, these proteases are expensive. Herein we report a cost-effective strategy, ‘arginine modification-cum trypsin digestion’, which can produce longer tryptic peptides resembling LysC peptides derived from proteins. Objective:: To obtain proteolytic peptides that resemble LysC peptides, by using 'trypsin', which is an less expensive protease. Methods: This strategy is based on the simple principle that trypsin cannot act at the C-termini of those arginines in proteins, whose sidechain guanidine groups are modified by 1,2-cyclohexanedione or phenylglyoxal. Results: As a proof of concept, we demonstrate this strategy on four models: -casein (bovine), - lactoglobulin (bovine), ovalbumin (chick) and transferrin (human), by electrospray ionization-mass spectrometry (ESI-MS) involving hybrid quadrupole time-of-flight. From the ESI-MS of these models, we obtained several arginine modified tryptic peptides, whose lengths are in the range, 30 - 60 a.a.r. The collision-induced dissociation MS/MS characteristics of some of the arginine modified longer tryptic peptides are compared with the unmodified standard tryptic peptides. Conclusion: The strategy followed in this proof-of-concept study, not only helps in obtaining longer tryptic peptides that mimic LysC proteolytic peptides, but also facilitates in enhancing the probability of missed cleavages by the trypsin. Hence, this method aids in evading the possibility of obtaining very short peptides that are < 5 - 10 a.a.r. Therefore, this is indeed an cost-effective alternative/substitute for LysC proteolysis and in turn, for those MD proteomic studies that utilize LysC. Additionally, this methodology can be fruitful for mass spectrometry based de novo protein and peptide sequencing.

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Rationale: Proteomic studies typically involve use of different types of softwares for annotating experimental tandem mass spectrometric data (MS/MS) and thereby simplify the process of peptide and protein identification. For such annotations, these softwares calculate the m/z values of the peptide/protein precursor and fragment ions, for which a database of protein sequences must be provided as input file. The calculated m/z values are stored as another database, which the user usually cannot view. ‘Database Creator for Protein/Peptide Mass Analysis’ (DC-PPMA) is a novel standalone software that can create custom databases and the user can view the custom database containing the calculated m/z values of precursor and fragment ions. Methods: Python language was used for implementation and the graphical user interface was built with Page/Tcl, making this tool more user-friendly and easier to analyze. DC-PPMA is freely available at https://vit.ac.in/PPMA/. Results: DC-PPMA contains three modules. Protein/peptide sequences as per user’s choice can be entered as input to the first module for creating custom database. In the second module, m/z values must be queried-in, which are searched within the custom database to identify protein/peptide sequences. The third module is suited for peptide mass fingerprinting, for which data arising from both ESI and MALDI MS can be utilized. Conclusions: Mass spectral data acquired from any proteomic approach: bottom-up, middle-down and top-down can be interrogated with DC-PPMA. A major facet of DC-PPMA is that the user can ‘view’ the custom database containing the m/z values of the precursor ions (e.g., proteolytic peptides) and the respective fragment ions (e.g., b & y ions), prior to the database search. The feature of ‘viewing’ the custom database cannot only be helpful for better understanding the search engine processes; but also, for ‘designing multiple reaction monitoring (MRM) methods’. Post-translational modifications and protein isoforms too can be analyzed.