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 peptide-level 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).
Highlights
Over the past few decades, mass spectrometry (MS) coupled to-liquid chromatography has become an indispensable analytical tool in identification, quantitation, and characterization of proteins.[1−10] The most established method is the bottom-up approach, called shotgun proteomics, where proteins in a complex sample are first digested to peptides, and the latter are separated in one or more dimensions and identified via tandem MS
As a first part of this work, we wanted to broaden the scope of our earlier investigation on collision energy dependence of the identification score
It was found that Byonic score and log Prob values gave practically identical energy dependence, so we will discuss only the Byonic score here; results on log Prob can be found in Supporting Information S5
Summary
Over the past few decades, mass spectrometry (MS) coupled to (nano)-liquid chromatography (nano-LC) has become an indispensable analytical tool in identification, quantitation, and characterization of proteins.[1−10] The most established method is the bottom-up approach, called shotgun proteomics, where proteins in a complex sample are first digested to peptides, and the latter are separated in one or more dimensions and identified via tandem MS. We broaden our previous investigation on Mascot to other search engines.[12,13] We chose Andromeda since it is the one integrated into the widely used MaxQuant quantitative proteomics platform.[32,33] We included Byonic, a widely used hybrid method, which uses a small amount of de novo sequencing to extract candidate peptides from the database[34] and overcomes the limitation of pure database searching programs, which cannot deal efficiently with nonspecific cleavages or several peptide modifications To transform these peptide-level results showing complicated and diverse energy dependence into practical approaches, we create and test workflows using combinations of multiple collision energies, within the same run or in two separate runs. For the study of the energy dependence of peptide fragmentation, we used our recently developed program called Serac.[26] Analogous to our previous work on Mascot data,[30] we collected identification scores as a function of collision energy from the energydependent mass spectrometric data series for the Byonic and Andromeda search engines and determined the optimal collision energy. The practical proteomics performance of LC-MS/MS runs using various collision energy settings in conjunction with various search engines was compared using Scaffold version 4.10 (Proteome Software, Portland, OR), using the following settings: LFDR-rescoring on, 95% peptide threshold, 1% FDR for proteins, and a minimum of two peptides was required for the identification of proteins
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