De novo peptide sequencing and identification with precision mass spectrometry

Abstract

The recent proliferation of novel mass spectrometers such as Fourier transform, QTOF, and OrbiTrap marks a transition into the era of precision mass spectrometry, providing a 2 orders of magnitude boost to the mass resolution, as compared to low-precision ion-trap detectors. We investigate peptide de novo sequencing by precision mass spectrometry and explore some of the differences when compared to analysis of low-precision data. We demonstrate how the dramatically improved performance of de novo sequencing with precision mass spectrometry paves the way for novel approaches to peptide identification that are based on direct sequence lookups, rather than comparisons of spectra to a database. With the direct sequence lookup, it is not only possible to search a database very efficiently, but also to use the database in novel ways, such as searching for products of alternative splicing or products of fusion proteins in cancer. Our de novo sequencing software is available for download at http://peptide.ucsd.edu/.

Figure 1

Two approaches to peptide identification. The traditional approach based on comparing spectra to the database (red) vs. our approach based on de novo sequencing and fast database lookup (blue).

Figure 2

a) Illustration of mass spectrum of DHGMPF, the red path shows the derivation of the peptide DFMGSF which starts with by using b₁, crossing over to the y– ladder using y₂,y₃, and y₄, and then returning to the b–ions to b₅. b) The spectrum graph derived from the mass spectrum of DHGMPF. c) The same spectrum graph with vertices rearranged to show the relationship between the paths of the homeometric peptides DHGMPF and DFMGSF. The top path (0,115,252,…) represents prefix masses while the bottom path represents suffix masses of DHGMPF (the masses are rounded off to integer values).

Figure 3

Probability of homeometric peptides. Random peptides of lengths 5-25 were generated and tested for the possibility of having at least one homeometric peptide (this test was done by generating their spectrum graphs and searching for multiple paths in the spectrum graph). Two mass tolerance settings were tested: 0.5 Da for low resolution and 0.0075 for high resolution.

Figure 4

Probability that a database contains homeometric peptides. Random peptides were selected from a sequence database of 16.8 million amino acids and searched against the entire database to detect their closest δ-homeometric counterparts. Two tolerance settings where used: the top shows results for a tolerance of 0.5 Da, which models low precision data, the bottom shows results for a tolerance of 0.0075 Da, which models high precision data. The results are shown for random peptides of lengths 7, 14, and 21, and for various numbers of missing cleavages.