Value of using multiple proteases for large-scale mass spectrometry-based proteomics

Abstract

Large-scale protein sequencing methods rely on enzymatic digestion of complex protein mixtures to generate a collection of peptides for mass spectrometric analysis. Here we examine the use of multiple proteases (trypsin, LysC, ArgC, AspN, and GluC) to improve both protein identification and characterization in the model organism Saccharomyces cerevisiae. Using a data-dependent, decision tree-based algorithm to tailor MS(2) fragmentation method to peptide precursor, we identified 92 095 unique peptides (609 665 total) mapping to 3908 proteins at a 1% false discovery rate (FDR). These results were a significant improvement upon data from a single protease digest (trypsin) - 27 822 unique peptides corresponding to 3313 proteins. The additional 595 protein identifications were mainly from those at low abundances (i.e., < 1000 copies/cell); sequence coverage for these proteins was likewise improved nearly 3-fold. We demonstrate that large portions of the proteome are simply inaccessible following digestion with a single protease and that multiple proteases, rather than technical replicates, provide a direct route to increase both protein identifications and proteome sequence coverage.

Figure 1

Plot of peptide length distribution for yeast proteome. Panel A displays peptide length profile for five proteases following an in silico digestion of the yeast proteome. Also shown is a plot of experimentally identified tryptic peptides – these peptides were drawn from five recent publications.- We independently considered each amino acid in the yeast proteome and ranked the sizes of the five peptides that contained it for each of the five proteases from panel A. In each instance we retained the peptide with the length that was most frequently observed in the experimental distribution. This best case distribution is plotted in Panel B and confirms that nearly all amino acids in the yeast proteome (94.8%) are contained in at least one peptide of suitable length for MS sequencing technology.

Figure 2

Experimental workflow. Following isolation, proteins from Saccharomyces cerevisiae cells, were separated into aliquots and digested with one of the following proteases: trypsin, LysC, ArgC, GluC, ApsN. Each digest was independently fractionated via strong cation exchange, followed by reversed-phase nano HPLC- MS². The method of MS² was selected using a decision tree-driven approach. All data was then searched against the Saccharomyces Genome Database using OMSSA and filtered first to a 1% FDR at the peptide level, and finally to a 1% FDR at the protein level.

Figure 3

Comparison of protein and non-redundant amino acid identifications. The overlap of non-redundant amino acid identifications (a) and proteins (b) between trypsin and the combined datasets from ArgC, AspN, GluC, and LysC. The number of identification unique to each group alone is displayed along with the percent overlap. The percent increase in proteins and non-redundant amino acids when comparing the mean of triplicate analyses of a single protease to the mean of any permutation of additional protease is shown in panel c. Panel d presents a comparison of single replicates of different protease vs. technical replicates of a single protease. Error bars represent the maximum and minimum percent increases observed and, in panel c, the protease combinations resulting in the maximum amino acid identifications are displayed above.

Figure 4

Assessment of in silico and experimental peptide length. Panel a presents the average peptide length as calculated in silico. Panel b displays the number of unique peptide identifications vs. average in silico peptide length for each protease dataset. Finally, panel c shows the experimental distribution of peptide lengths resulting from cleavage with 5 different proteases.

Figure 5

Evaluation of protein identifications and protein sequence coverage as a function of protein abundance. The viewgraph in panel a displays the percentage of proteins identified from an individual protease dataset and collectively (black bars). Panel b presents sequence coverage in a similar fashion.