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Comparative Evaluation of MaxQuant and Proteome Discoverer MS1-Based Protein Quantification Tools.

ABSTRACT: MS1-based label-free quantification can compare precursor ion peaks across runs, allowing reproducible protein measurements. Among bioinformatic platforms enabling MS1-based quantification, MaxQuant (MQ) is one of the most used, while Proteome Discoverer (PD) has recently introduced the Minora tool. Here, we present a comparative evaluation of six MS1-based quantification methods available in MQ and PD. Intensity (MQ and PD) and area (PD only) of the precursor ion peaks were measured and then subjected or not to normalization. The six methods were applied to data sets simulating various differential proteomics scenarios and covering a wide range of protein abundance ratios and amounts. PD outperformed MQ in terms of quantification yield, dynamic range, and reproducibility, although neither platform reached a fully satisfactory quality of measurements at low-abundance ranges. PD methods including normalization were the most accurate in estimating the abundance ratio between groups and the most sensitive when comparing groups with a narrow abundance ratio; on the contrary, MQ methods generally reached slightly higher specificity, accuracy, and precision values. Moreover, we found that applying an optimized log ratio-based threshold can maximize specificity, accuracy, and precision. Taken together, these results can help researchers choose the most appropriate MS1-based protein quantification strategy for their studies.

SUBMITTER: Palomba A

PROVIDER: S-EPMC8280745 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundAlthough the importance of proteins of the biomineral organic matrix and their posttranslational modifications for biomineralization is generally recognized, the number of published matrix proteomes is still small. This is mostly due to the lack of comprehensive sequence databases, usually derived from genomic sequencing projects. However, in-depth mass spectrometry-based proteomic analysis, which critically depends on high-quality sequence databases, is a very fast tool to identify candidates for functional biomineral matrix proteins and their posttranslational modifications. Identification of such candidate proteins is facilitated by at least approximate quantitation of the identified proteins, because the most abundant ones may also be the most interesting candidates for further functional analysis.ResultsRe-quantification of previously identified Lottia shell matrix proteins using the intensity-based absolute quantification (iBAQ) method as implemented in the MaxQuant identification and quantitation software showed that only 57 of the 382 accepted identifications constituted 98% of the total identified matrix proteome. This group of proteins did not contain obvious intracellular proteins, such as cytoskeletal components or ribosomal proteins, invariably identified as minor components of high-throughput biomineral matrix proteomes. Fourteen of these major proteins were phosphorylated to a variable extent. All together we identified 52 phospho sites in 20 of the 382 accepted proteins with high confidence.ConclusionsWe show that iBAQ quantitation may be a useful tool to narrow down the group of functional biomineral matrix protein candidates for further research in cell biology, genetics or materials research. Knowledge of posttranslational modifications in these major proteins could be a valuable addition to previously published proteomes. This is true especially for phosphorylation, because this modification was already shown to modify mineralization processes in some instances.

Project description:In-depth and reproducible protein measurement in many biological samples is often critical for pharmaceutical/biomedical proteomics but remains challenging. MS1-based quantification using quadrupole/ultrahigh-field Orbitrap (Q/UHF-Orbitrap) holds great promise, but the critically important experimental approaches enabling reliable large-cohort analysis have long been overlooked. Here we described an IonStar experimental strategy achieving excellent quantitative quality of MS1 quantification. Key features include: (i) an optimized, surfactant-aided sample preparation approach provides highly efficient (>75% recovery) and reproducible (<15% CV) peptide recovery across large cell/tissue cohorts; (ii) a long column with modest gradient length (2.5 h) yields the optimal balance of depth/throughput on a Q/UHF-Orbitrap; (iii) a large-ID trap not only enables highly reproducible gradient delivery as for the first time observed via real-time conductivity monitoring, but also increases quantitative loading capacity by >8-fold and quantified >25% more proteins; (iv) an optimized HCD-OT markedly outperforms HCD-IT when analyzing large cohorts with high loading amounts; (v) selective removal of hydrophobic/hydrophilic matrix components using a novel selective trapping/delivery approach enables reproducible, robust LC-MS analysis of >100 biological samples in a single set, eliminating batch effect; (vi) MS1 acquired at higher resolution (fwhm = 120 k) provides enhanced S/N and quantitative accuracy/precision for low-abundance species. We examined this pipeline by analyzing a 5 group, 20 samples biological benchmark sample set, and quantified 6273 unique proteins (?2 peptides/protein) under stringent cutoffs without fractionation, 6234 (>99.4%) without missing data in any of the 20 samples. The strategy achieved high quantitative accuracy (3-6% media error), low intragroup variation (6-9% media intragroup CV) and low false-positive biomarker discovery rates (3-8%) across the five groups, with quantified protein abundances spanning >6.5 orders of magnitude. Finally, this strategy is straightforward, robust, and broadly applicable in pharmaceutical/biomedical investigations.

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Comparative Evaluation of MaxQuant and Proteome Discoverer MS1-Based Protein Quantification Tools.

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