Broadening the horizon of MS optimization for enzymatic digestions in proteomics

Tandem mass spectrometry is an essential tool for protein analysis. Proteins are most often identified based on MS/MS spectra of their peptides produced by tryptic digestion, which are compared with theoretical spectra of peptides based on a protein database. The reliability of the peptide spectrum match is characterized by a score. The identification depends on the experimental parameters and the software used for the evaluation. Many times, instead of trypsin, enzymes with other specificity are preferable, so their use is more and more widespread. However, the optimization of MS/MS parameters is still focused on tryptic peptides. Even though collision energy (CE) has a great influence on the quality of MS/MS spectra, CE optimization in detail for digestions with alternative enzymes has not yet been carried out for a large number of non-tryptic peptides.
The objective of this study was to optimize the MS-based identification of non-tryptic pep-tides by examining the reliability of database search-based identification of peptides digested by alternative enzymes as a function of the collision energy used during MS/MS measurement. We focused on four enzymes typically used in proteomics (Asp-N, Arg-C, chymotrypsin, Glu-C,) and examined trypsin as reference. NanoLC-MS/MS measurements were performed using varied CE settings to determine the optimal value of the CE for a large number of non-tryptic peptides. Human blood plasma and MS compatible human protein extract were used as complex samples. The evaluation was carried out with Byonic and Mascot search engines.
Results indicate that the enzyme specificity has a significant effect on the optimal collision energy, and the observed differences can be explained by the fragmentation characteristics of the peptides obtained by different enzymatic digestions. While the peptides obtained with Arg-C behave analogously to tryptic peptides, in the case of Asp-N, chymotrypsin and Glu-C the optimal collision energy is 5-15 eV lower. The optimum does not depend significantly on the sample type and the database search engine used. Based on our results, a more efficient MS-based method is proposed for the investigation of non-tryptic peptides by adjusting the collision energy based on the type of enzyme. When determining its performance, in the case of alternative enzymes the number of peptides and the sequence coverage increased significantly compared to the method optimized for tryptic peptides.
Financial support from the NKFIH (grant FK-138678) is gratefully acknowledged.