Tailoring to Search Engines: Bottom-Up Proteomics with Collision Energies Optimized for Identification Confidence

Abstract

Bottom-up proteomics relies on identification of peptides from tandem mass spectra, usually via matching against sequence databases. Confidence in a peptide−spectrum match can be characterized by a score value given by the database search engines, and it depends on the information content and the quality of the spectrum. The latter are influenced by experimental parameters, of which the collision energy is the most important one in the case of collision-induced dissociation. We examined how the identification score of the Byonic and Andromeda (MaxQuant) engines varies with collision energy for more than a thousand individual peptides from a HeLa tryptic digest on a QTof instrument. We thereby extended our earlier study on Mascot scores and corroborated its findings on the potential bimodal nature of this energy dependence. Optimal energies as a function of m/z show comparable linear trends for the three engines. On the basis of peptidelevel results, we designed methods with one or two liquid chromatography−tandem mass spectrometry (LC-MS/MS) runs and various collision energy settings and assessed their practical performance in peptide and protein identification from the HeLa standard sample. A 10−40% gain in various measures, such as the number of identified proteins or sequence coverage, was obtained over the factory default settings. Best performing methods differ for the three engines, suggesting that the experimental parameters should be fine-tuned to the choice of the engine. We also recommend a simple approach and provide reference data to ease the transfer of the optimized methods to other mass spectrometers relevant for proteomics. We demonstrate the utility of this approach on an Orbitrap instrument. Data sets can be accessed via the MassIVE repository (MSV000086379).

Stepwise Collision Energy-Resolved Tandem Mass Spectrometric Experiments for the Improved Identification of Citrullinated Peptides

“…Since Byonic is a commercial software, we refrain from a detailed explanation of this phenomena. On the other hand, similar trend was found for unmodified tryptic peptides 24 earlier. The key reasons may include the difference in what types of fragment ions the engines look for and how they utilize experimental intensity information.…”

“…22,23 For peptide identification, 28% NCE was found to be the optimal value on an Orbitrap mass spectrometer. 24 Confirmed sequence coverage of mAb peptides was also investigated, and a workflow using a step of two collision energies 6−10 eV apart was suggested. 22 For post-translationally modified peptides like N-or O-glycosylated ones, a stepped NCE value of 30−50% was found to be ideal by Cao et al, 25 but generally (30 ± 10)% is used by the community.…”

Diversity Matters: Optimal Collision Energies for Tandem Mass Spectrometric Analysis of a Large Set of N-Glycopeptides

“…Given the differences in the peak picking and scoring algorithm of available search engines, 4 , 33 , 34 one may expect somewhat different experimental parameters to be optimal when different data evaluation procedures are used. This was indeed confirmed for unmodified peptides 35 but has not yet been explored for glycopeptides.…”

“…The latter were measured on the same instrument earlier in our group. 35 Since pGlyco is designed for glycopeptide identification only, no pGlyco analysis was carried out in this section. It was found that N-glycopeptides need ca.…”

“…We recommended and successfully applied this concept for tryptic peptides earlier. 35 The idea is that even though the trend line itself may differ between instruments, the property of whether a given species lies close to the trend line or farther away seems well conserved. We have therefore collected a list of reference N-glycopeptides, for which the optimum CE was close to (within 8% of) the respective trend line in our experiments for both investigated search engines.…”