Improved peptide identification for proteomic analysis based on comprehensive characterization of electron transfer dissociation spectra.
In recent years, electron transfer dissociation (ETD) has enjoyed widespread applications from sequencing of peptides with or without post-translational modifications to top-down analysis of intact proteins. However, peptide identification rates from ETD spectra compare poorly with those from collision induced dissociation (CID) spectra, especially for doubly charged precursors. This is in part due to an insufficient understanding of the characteristics of ETD and consequently a failure of database search engines to make use of the rich information contained in the ETD spectra. In this study, we statistically characterized ETD fragmentation patterns from a collection of 461 440 spectra and subsequently implemented our findings into pFind, a database search engine developed earlier for CID data. From ETD spectra of doubly charged precursors, pFind 2.1 identified 63-122% more unique peptides than Mascot 2.2 under the same 1% false discovery rate. For higher charged peptides as well as phosphopeptides, pFind 2.1 also consistently obtained more identifications. Of the features built into pFind 2.1, the following two greatly enhanced its performance: (1) refined automatic detection and removal of high-intensity peaks belonging to the precursor, charge-reduced precursor, or related neutral loss species, whose presence often set spectral matching askew; (2) a thorough consideration of hydrogen-rearranged fragment ions such as z + H and c - H for peptide precursors of different charge states. Our study has revealed that different charge states of precursors result in different hydrogen rearrangement patterns. For a fragment ion, its propensity of gaining or losing a hydrogen depends on (1) the ion type (c or z) and (2) the size of the fragment relative to the precursor, and both dependencies are affected by (3) the charge state of the precursor. In addition, we discovered ETD characteristics that are unique for certain types of amino acids (AAs), such as a prominent neutral loss of SCH(2)CONH(2) (90.0014 Da) from z ions with a carbamidomethylated cysteine at the N-terminus and a neutral loss of histidine side chain C(4)N(2)H(5) (81.0453 Da) from precursor ions containing histidine. The comprehensive list of ETD characteristics summarized in this paper should be valuable for automated database search, de novo peptide sequencing, and manual spectral validation.
Sun
Rui-Xiang
RX
Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China. rxsun@ict.ac.cn
Dong
Meng-Qiu
MQ
Song
Chun-Qing
CQ
Chi
Hao
H
Yang
Bing
B
Xiu
Li-Yun
LY
Tao
Li
L
Jing
Zhi-Yi
ZY
Liu
Chao
C
Wang
Le-Heng
LH
Fu
Yan
Y
He
Si-Min
SM
D000595:Amino Acid Sequence; D004579:Electron Transport; D013058:Mass Spectrometry / Q000379:methods*; D008969:Molecular Sequence Data; D010455:Peptides / Q000032:analysis* / Q000737:chemistry; D010748:Phosphopeptides / Q000032:analysis / Q000737:chemistry; D040901:Proteomics / Q000379:methods*; D015203:Reproducibility of Results
D010455:Peptides; D010748:Phosphopeptides