Correctness of Protein Identifications of<i>Bacillus subtilis</i>Proteome with the Indication on Potential False Positive Peptides Supported by Predictions of Their Retention Times

Macur, Katarzyna; Bączek, Tomasz; Kaliszan, Roman; Temporini, Caterina; Corana, Federica; Massolini, Gabriella; Grzenkowicz-Wydra, Jolanta; Obuchowski, Michał

doi:10.1155/2010/718142

Cited by 5 publications

(5 citation statements)

References 31 publications

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“…In summary, progress in mass spectrometry allows the masses of biomolecular ions to be measured with sub-ppm precision; however, this accuracy is still far from being sequence-specific for peptides, which is the deficiency countered by MS/MS . Numerous studies have shown that retention times are sequence-specific in the case of peptides and proteins and may potentially replace MS/MS. − This intrinsic value of the chromatography data was also extensively explored, mainly to support peptide identification and validation by reducing the search space or helping the identification of the features in MS1 spectra. ,− One of the main obstacles associated with the use of chromatography for implementing true MS1-based proteomics workflows is its relatively low accuracy in the prediction of retention times for peptides, which is still much lower compared with LC’s experimental precision and reproducibility . In other words, exact masses measured with sub-ppm accuracy and retention times alone were not enough to identify a tryptic peptide from a database unambiguously.…”

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DirectMS1: MS/MS-Free Identification of 1000 Proteins of Cellular Proteomes in 5 Minutes

et al. 2020

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Proteome characterization relies heavily on tandem mass spectrometry (MS/MS) and is thus associated with instrumentation complexity, lengthy analysis time, and limited duty-cycle. It was always tempting to implement approaches which do not require MS/MS, yet, they were constantly failing in achieving meaningful depth of quantitative proteome coverage within short experimental times, which is particular important for clinical or biomarker discovery applications. Here, we report on the first successful attempt to develop a truly MS/MS-free and label-free method for bottom-up proteomics. We demonstrate identification of 1000 protein groups for a standard HeLa cell line digest using 5-minute LC gradients. The amount of loaded sample was varied in a range from 1 ng to 500 ng, and the method demonstrated 10-fold higher sensitivity compared with the standard MS/MS-based approach. Due to significantly higher sequence coverage obtained by the developed method, it outperforms all popular MS/MSbased label-free quantitation approaches. Advances in mass-spectrometry-based proteomic technologies resulted in dramatically increased depth, throughput, and sensitivity of proteome coverage. Up to 10,000 proteins can be identified in an 100 minute analysis of human cell proteomes using state-of-the-art high-resolution Orbitrap mass spectrometry 1. Recently, the notable trend in LC-MS technology developments has been toward increasing the throughput of the proteome-wide analysis, while preserving the quantitation accuracy 2,3. However, these achievements rely heavily on the use of tandem mass spectrometry (MS/MS), which includes sequential isolation of eluting peptides followed by their fragmentation. While being a crucial and seemingly the only source of sequence-specific information about the peptides, MS/MS brings a number of well-known challenges. Due to the limited both the speed of the mass analyzer (which is

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DirectMS1: MS/MS-Free Identification of 1000 Proteins of Cellular Proteomes in 5 Minutes

et al. 2020

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“…The trend remains linear (Figure 5) but not perfectly scattered along the y = x line. The deviation originates from the inclusion of peptides with lower identification scores, as well as from identifications based only on one occurrence, which, by proteomic standards, 42 could mean that they are not present in the sample.…”

Section: ■ Results and Discussionmentioning

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Section: Experimental Sectionmentioning

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“…The results from these analyses, (a) 83 peptides of eight-model proteins, and (b) 102 peptides of Bacillus subtilis proteomes, were used for QSRR model development. Performed RP-LC-MS/MS analyses are described in detail by Macur et al 42 Model Development. Prior to model construction, geometries of molecular structures of the peptides were optimized in HyperChem Professional 8 (Hypercube Inc., Gainesville, FL) software by means of the molecular mechanics method 43 with CHARMM (BIO+) force field, 44 utilizing the Polak−Ribiere conjugate algorithm 45 and root-mean-square (RMS) gradient value of 0.1 kcal mol −1 Å −1 as a stopping criterion.…”

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Molecular Descriptor Subset Selection in Theoretical Peptide Quantitative Structure–Retention Relationship Model Development Using Nature-Inspired Optimization Algorithms

et al. 2015

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In this work, performance of five nature-inspired optimization algorithms, genetic algorithm (GA), particle swarm optimization (PSO), artificial bee colony (ABC), firefly algorithm (FA), and flower pollination algorithm (FPA), was compared in molecular descriptor selection for development of quantitative structure-retention relationship (QSRR) models for 83 peptides that originate from eight model proteins. The matrix with 423 descriptors was used as input, and QSRR models based on selected descriptors were built using partial least squares (PLS), whereas root mean square error of prediction (RMSEP) was used as a fitness function for their selection. Three performance criteria, prediction accuracy, computational cost, and the number of selected descriptors, were used to evaluate the developed QSRR models. The results show that all five variable selection methods outperform interval PLS (iPLS), sparse PLS (sPLS), and the full PLS model, whereas GA is superior because of its lowest computational cost and higher accuracy (RMSEP of 5.534%) with a smaller number of variables (nine descriptors). The GA-QSRR model was validated initially through Y-randomization. In addition, it was successfully validated with an external testing set out of 102 peptides originating from Bacillus subtilis proteomes (RMSEP of 22.030%). Its applicability domain was defined, from which it was evident that the developed GA-QSRR exhibited strong robustness. All the sources of the model's error were identified, thus allowing for further application of the developed methodology in proteomics.

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“…The growth conditions of Bacillus subtilis strains 168 and ΔprpE , spore purification and protein extraction procedures were as previously described [ 33 ]. After that, the samples were treated according to the below presented digestion protocol.…”

Section: Methodsmentioning

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Proteomic analysis of small acid soluble proteins in the spore core of Bacillus subtilis ΔprpE and 168 strains with predictions of peptides liquid chromatography retention times as an additional tool in protein identification

et al. 2010

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Background Sporulation, characteristic for some bacteria such as Bacillus subtilis , has not been entirely defined yet. Protein phosphatase E (PrpE) and small, acid soluble spore proteins (SASPs) influence this process. Nevertheless, direct result of PrpE interaction on SASPs content in spore coat of B. subtilis has not been evidenced so far. As proteomic approach enables global analysis of occurring proteins, therefore it was chosen in this experiment to compare SASPs occurrence in two strains of B. subtilis , standard 168 and ΔprpE , lacking PrpE phosphatase. Proteomic analysis is still a challenge, and despite of big approach in mass spectrometry (MS) field, the identification reliability remains unsatisfactory. Therefore there is a rising interest in new methods, particularly bioinformatic tools that would harden protein identification. Most of currently applied algorithms are based on MS-data. Information from separation steps is not still in routine usage, even though they also provide valuable facts about analyzed structures. The aim of this research was to apply a model for peptides retention times prediction, based on quantitative structure-retention relationships (QSRR) in SASPs analysis, obtained from two strains of B. subtilis proteome digests after separation and identification of the peptides by LC-ESI-MS/MS. The QSRR approach was applied as the additional constraint in proteomic research verifying results of MS/MS ion search and confirming the correctness of the peptides identifications along with the indication of the potential false positives and false negatives. Results In both strains of B. subtilis , peptides characteristic for SASPs were found, however their identification confidence varied. According to the MS identity parameter X corr and difference between predicted and experimental retention times (Δt R ) four groups could be distinguished: correctly and incorrectly identified, potential false positives and false negatives. The ΔprpE strain was characterized by much higher amount of SASPs peptides than standard 168 and their identification confidence was, mostly for alpha- and beta-type SASP, satisfactory. Conclusions The QSRR-based model for predicting retention times of the peptides, was a useful additional to MS tool, enhancing protein identification. Higher content of SASPs in strain lacking PrpE phosphatase suggests that this enzyme may influence their occurrence in the spores, lowering levels of these proteins.

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Correctness of Protein Identifications ofBacillus subtilisProteome with the Indication on Potential False Positive Peptides Supported by Predictions of Their Retention Times

Cited by 5 publications

References 31 publications

DirectMS1: MS/MS-Free Identification of 1000 Proteins of Cellular Proteomes in 5 Minutes

DirectMS1: MS/MS-Free Identification of 1000 Proteins of Cellular Proteomes in 5 Minutes

Molecular Descriptor Subset Selection in Theoretical Peptide Quantitative Structure–Retention Relationship Model Development Using Nature-Inspired Optimization Algorithms

Proteomic analysis of small acid soluble proteins in the spore core of Bacillus subtilis ΔprpE and 168 strains with predictions of peptides liquid chromatography retention times as an additional tool in protein identification

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