Number Of Peptide Spectrum Matches Research Articles

MS2Rescore 3.0 Is a Modular, Flexible, and User-Friendly Platform to Boost Peptide Identifications, as Showcased with MS Amanda 3.0.

Rescoring of peptide-spectrum matches (PSMs) has emerged as a standard procedure for the analysis of tandem mass spectrometry data. This emphasizes the need for software maintenance and continuous improvement for such algorithms. We introduce MS2Rescore 3.0, a versatile, modular, and user-friendly platform designed to increase peptide identifications. Researchers can install MS2Rescore across various platforms with minimal effort and benefit from a graphical user interface, a modular Python API, and extensive documentation. To showcase this new version, we connected MS2Rescore 3.0 with MS Amanda 3.0, a new release of the well-established search engine, addressing previous limitations on automatic rescoring. Among new features, MS Amanda now contains additional output columns that can be used for rescoring. The full potential of rescoring is best revealed when applied on challenging data sets. We therefore evaluated the performance of these two tools on publicly available single-cell data sets, where the number of PSMs was substantially increased, thereby demonstrating that MS2Rescore offers a powerful solution to boost peptide identifications. MS2Rescore's modular design and user-friendly interface make data-driven rescoring easily accessible, even for inexperienced users. We therefore expect the MS2Rescore to be a valuable tool for the wider proteomics community. MS2Rescore is available at https://github.com/compomics/ms2rescore.

Purification, Characterization and Bactericidal Action of Lysozyme, Isolated from Bacillus subtillis BSN314: A Disintegrating Effect of Lysozyme on Gram-Positive and Gram-Negative Bacteria.

In the present study, lysozyme was purified by the following multi-step methodology: salt (ammonium sulfate) precipitation, dialysis, and ultrafiltration. The lysozyme potential was measured by enzymatic activity after each purification step. However, after ultrafiltration, the resulting material was considered extra purified. It was concentrated in an ultrafiltration centrifuge tube, and the resulting protein/lysozyme was used to determine its bactericidal potential against five bacterial strains, including three gram-positive (Bacillus subtilis 168, Micrococcus luteus, and Bacillus cereus) and two gram-negative (Salmonella typhimurium and Pseudomonas aeruginosa) strains. The results of ZOI and MIC/MBC showed that lysozyme had a higher antimicrobial activity against gram-positive than gram-negative bacterial strains. The results of the antibacterial activity of lysozyme were compared with those of ciprofloxacin (antibiotic). For this purpose, two indices were applied in the present study: antimicrobial index (AMI) and percent activity index (PAI). It was found that the purified lysozyme had a higher antibacterial activity against Bacillus cereus (AMI/PAI; 1.01/101) and Bacillus subtilis 168 (AMI/PAI; 1.03/103), compared to the antibiotic (ciprofloxacin) used in this study. Atomic force microscopy (AFM) was used to determine the bactericidal action of the lysozyme on the bacterial cell. The purified protein was further processed by gel column chromatography and the eluate was collected, its enzymatic activity was 21.93 U/mL, while the eluate was processed by native-PAGE. By this analysis, the un-denatured protein with enzymatic activity of 40.9 U/mL was obtained. This step shows that the protein (lysozyme) has an even higher enzymatic potential. To determine the specific peptides (in lysozyme) that may cause the bactericidal potential and cell lytic/enzymatic activity, the isolated protein (lysozyme) was further processed by the SDS-PAGE technique. SDS-PAGE analysis revealed different bands with sizes of 34 kDa, 24 kDa, and 10 kDa, respectively. To determine the chemical composition of the peptides, the bands (from SDS-PAGE) were cut, enzymatically digested, desalted, and analyzed by LC-MS (liquid chromatography-mass spectrometry). LC-MS analysis showed that the purified lysozyme had the following composition: the number of proteins in the sample was 56, the number of peptides was 124, and the number of PSMs (peptide spectrum matches) was 309. Among them, two peptides related to lysozyme and bactericidal activities were identified as: A0A1Q9G213 (N-acetylmuramoyl-L-alanine amidase) and A0A1Q9FRD3 (D-alanyl-D-alanine carboxypeptidase). The corresponding protein sequence and nucleic acid sequence were determined by comparison with the database.

Intra-population differences of apolipoproteins in the aqueous humor

BackgroundEvidence suggests that proteins related to lipid metabolism, such as apolipoproteins, play an important role in the maintenance of normal vision. While several members of the apolipoprotein family are abundant in human aqueous humor (AH), their study remains difficult due to the AH’s small volume, low protein concentration, and the invasive nature of sample collection. In this study, we report the use of Liquid Chromatography Tandem Mass Spectrometry (LC-MS/MS) to discover associations between AH apolipoproteins and race, gender, and ocular structure in patients with and without primary open angle glaucoma (POAG).MethodsAH samples were collected from 231 patients undergoing phacoemulsification or glaucoma incisional surgery at the Medical College of Georgia, Augusta University and subsequently analyzed via LC-MS/MS. The number of peptide spectrum matches (PSMs) for each protein was used as a semi-quantitative measure of relative protein levels. Parameters related to ocular structure were determined using Optical Coherence Tomography (OCT) and Heidelberg Retinal Tomography (HRT). These data sets were probed for relationships between apolipoprotein levels and POAG, demographics (gender and race), and ocular structure.ResultsA total of ten apolipoproteins were detected in the 231 collected AH samples, with six detected in 100% of the samples, one detected in almost 57% of the samples and three detected in less than 10% of the samples. The levels of APOA1, APOC3, and APOD were higher among POAG subjects. Stratification by gender and race revealed demographic-specific variations. The levels of five apolipoproteins (APOA1, APOA2, APOA4, APOC3, and APOD) were higher in female POAG patients, whereas no apolipoprotein levels were altered in male POAG patients. The levels of APOA1, APOA2, APOA4, and APOD were increased in glaucomatous African American patients, whereas APOE and APOH levels were decreased in glaucomatous Caucasian patients. We also found distinct associations between apolipoprotein levels and OCT and HRT parameters in patients with and without POAG.ConclusionsThe intra-population variation in apolipoprotein levels highlights the heterogeneity of glaucoma as a disease, suggesting the importance of personalized treatments. Gender and race-specific alterations may be associated with higher risks of POAG in females and members of the African American population.

Increasing the power of interpretation for soil metaproteomics data

BackgroundSoil and sediment microorganisms are highly phylogenetically diverse but are currently largely under-represented in public molecular databases. Their functional characterization by means of metaproteomics is usually performed using metagenomic sequences acquired for the same sample. However, such hugely diverse metagenomic datasets are difficult to assemble; in parallel, theoretical proteomes from isolates available in generic databases are of high quality. Both these factors advocate for the use of theoretical proteomes in metaproteomics interpretation pipelines. Here, we examined a number of database construction strategies with a view to increasing the outputs of metaproteomics studies performed on soil samples.ResultsThe number of peptide-spectrum matches was found to be of comparable magnitude when using public or sample-specific metagenomics-derived databases. However, numbers were significantly increased when a combination of both types of information was used in a two-step cascaded search. Our data also indicate that the functional annotation of the metaproteomics dataset can be maximized by using a combination of both types of databases.ConclusionsA two-step strategy combining sample-specific metagenome database and public databases such as the non-redundant NCBI database and a massive soil gene catalog allows maximizing the metaproteomic interpretation both in terms of ratio of assigned spectra and retrieval of function-derived information.4F9sWsX3PjS31f5TKAyCkGVideo abstract

Transfer posterior error probability estimation for peptide identification

BackgroundIn shotgun proteomics, database searching of tandem mass spectra results in a great number of peptide-spectrum matches (PSMs), many of which are false positives. Quality control of PSMs is a multiple hypothesis testing problem, and the false discovery rate (FDR) or the posterior error probability (PEP) is the commonly used statistical confidence measure. PEP, also called local FDR, can evaluate the confidence of individual PSMs and thus is more desirable than FDR, which evaluates the global confidence of a collection of PSMs. Estimation of PEP can be achieved by decomposing the null and alternative distributions of PSM scores as long as the given data is sufficient. However, in many proteomic studies, only a group (subset) of PSMs, e.g. those with specific post-translational modifications, are of interest. The group can be very small, making the direct PEP estimation by the group data inaccurate, especially for the high-score area where the score threshold is taken. Using the whole set of PSMs to estimate the group PEP is inappropriate either, because the null and/or alternative distributions of the group can be very different from those of combined scores.ResultsThe transfer PEP algorithm is proposed to more accurately estimate the PEPs of peptide identifications in small groups. Transfer PEP derives the group null distribution through its empirical relationship with the combined null distribution, and estimates the group alternative distribution, as well as the null proportion, using an iterative semi-parametric method. Validated on both simulated data and real proteomic data, transfer PEP showed remarkably higher accuracy than the direct combined and separate PEP estimation methods.ConclusionsWe presented a novel approach to group PEP estimation for small groups and implemented it for the peptide identification problem in proteomics. The methodology of the approach is in principle applicable to the small-group PEP estimation problems in other fields.

MS2-Deisotoper: A Tool for Deisotoping High-Resolution MS/MS Spectra in Normal and Heavy Isotope-Labelled Samples.

High-resolution MS/MS spectra of peptides can be deisotoped to identify monoisotopic masses of peptide fragments. The use of such masses should improve protein identification rates. However, deisotoping is not universally used and its benefits have not been fully explored. Here, MS2-Deisotoper, a tool for use prior to database search, is used to identify monoisotopic peaks in centroided MS/MS spectra. MS2-Deisotoper works by comparing the mass and relative intensity of each peptide fragment peak to every other peak of greater mass, and by applying a set of rules concerning mass and intensity differences. After comprehensive parameter optimization, it is shown that MS2-Deisotoper can improve the number of peptide spectrum matches (PSMs) identified by up to 8.2% and proteins by up to 2.8%. It is effective with SILAC and non-SILAC MS/MS data. The identification of unique peptide sequences is also improved, increasing the number of human proteoforms by 3.7%. Detailed investigation of results shows that deisotoping increases Mascot ion scores, improves FDR estimation for PSMs, and leads to greater protein sequence coverage. At a peptide level, it is found that the efficacy of deisotoping is affected by peptide mass and charge. MS2-Deisotoper can be used via a user interface or as a command-line tool.

Exploring the oviductal fluid proteome by a lectin-based affinity approach.

The analysis of glycoproteins in body fluids represents a central task in the study of vital processes. Herein, we assessed the combined use of Concanavalin A and Wheat Germ Agglutinin as ligands to fractionate and enrich glycoproteins from oviductal fluid (OF), which is a source of molecules involved in fertilization. First, the selectivity was corroborated by a gel-based approach using glycoprotein staining and enzymatic deglycosylation. Nanoliquid chromatography-tandem mass spectrometry (nLC-ESI-MS/MS) further allowed the reliable identification of 134 nonbound as well as 130 lectin-bound OF proteins. Enrichment analysis revealed that 77% of the annotated proteins in the lectin-bound fraction were known glycoproteins (p-value [FDR] = 1.45E-31). The low variance of the number of peptide spectrum matches for each protein within replicates indicated a consistent reproducibility of the whole workflow (median CV 17.3% for technical replicates and 20.7% for biological replicates). Taken together, this study highlights the applicability of a lectin-based workflow for the comprehensive analysis of OF proteins and gives for the first time an insight into the broad glycoprotein content of OF.

Proteome analysis of stratum corneum from atopic dermatitis patients by hybrid quadrupole-orbitrap mass spectrometer

Proteome analysis of stratum corneum from atopic dermatitis patients by hybrid quadrupole-orbitrap mass spectrometer

N-terminal sequence tagging using reliably determined b2 ions: a useful approach to deconvolute tandem mass spectra of co-fragmented peptides in proteomics.

With the recent introduction of higher-energy collisional dissociation (HCD) in Orbitrap mass spectrometry, the popularity of that technique has grown tremendously in the proteomics society. HCD spectra, however, are characterized by a limited distribution of bn-type ions, which permit the generation of reliable sequence tags based on complementary b,y pairs both for de novo sequencing and sequence tagging strategies. Instead, most peptide HCD spectra (~95%) are dominated with b2 ions. In this work, we analyzed positive predictive values of b2 ions in HCD, and found that b2 ions can be determined with >97% certainty in the presence of a2 and its complementary yn-2 ions. Analytically, b2 ions provide information on the composition of the first two N-terminal amino acids in peptides. Their utilization in N-terminal sequence tagging leads to a significant decrease in false discovery rate by filtering out false positives while retaining true positive identifications. As a consequence, the number of peptide spectrum matches (PSMs) increased by 4.8% at fixed FDR (1%). This approach allows for deconvolution of mixture spectra and increased the number of PSM to 9.2% in a complex human sample and to 24% in a complex sample of synthetic peptides at 1% FDR.

Empirical Multidimensional Space for Scoring Peptide Spectrum Matches in Shotgun Proteomics

Data-dependent tandem mass spectrometry (MS/MS) is one of the main techniques for protein identification in shotgun proteomics. In a typical LC-MS/MS workflow, peptide product ion mass spectra (MS/MS spectra) are compared with those derived theoretically from a protein sequence database. Scoring of these matches results in peptide identifications. A set of peptide identifications is characterized by false discovery rate (FDR), which determines the fraction of false identifications in the set. The total number of peptides targeted for fragmentation is in the range of 10,000 to 20,000 for a several-hour LC-MS/MS run. Typically, <50% of these MS/MS spectra result in peptide-spectrum matches (PSMs). A small fraction of PSMs pass the preset FDR level (commonly 1%) giving a list of identified proteins, yet a large number of correct PSMs corresponding to the peptides originally present in the sample are left behind in the "grey area" below the identity threshold. Following the numerous efforts to recover these correct PSMs, here we investigate the utility of a scoring scheme based on the multiple PSM descriptors available from the experimental data. These descriptors include retention time, deviation between experimental and theoretical mass, number of missed cleavages upon in-solution protein digestion, precursor ion fraction (PIF), PSM count per sequence, potential modifications, median fragment mass error, (13)C isotope mass difference, charge states, and number of PSMs per protein. The proposed scheme utilizes a set of metrics obtained for the corresponding distributions of each of the descriptors. We found that the proposed PSM scoring algorithm differentiates equally or more efficiently between correct and incorrect identifications compared with existing postsearch validation approaches.

Enhanced Peptide Identification by Electron Transfer Dissociation Using an Improved Mascot Percolator

Peptide identification using tandem mass spectrometry is a core technology in proteomics. Latest generations of mass spectrometry instruments enable the use of electron transfer dissociation (ETD) to complement collision induced dissociation (CID) for peptide fragmentation. However, a critical limitation to the use of ETD has been optimal database search software. Percolator is a post-search algorithm, which uses semi-supervised machine learning to improve the rate of peptide spectrum identifications (PSMs) together with providing reliable significance measures. We have previously interfaced the Mascot search engine with Percolator and demonstrated sensitivity and specificity benefits with CID data. Here, we report recent developments in the Mascot Percolator V2.0 software including an improved feature calculator and support for a wider range of ion series. The updated software is applied to the analysis of several CID and ETD fragmented peptide data sets. This version of Mascot Percolator increases the number of CID PSMs by up to 80% and ETD PSMs by up to 60% at a 0.01 q-value (1% false discovery rate) threshold over a standard Mascot search, notably recovering PSMs from high charge state precursor ions. The greatly increased number of PSMs and peptide coverage afforded by Mascot Percolator has enabled a fuller assessment of CID/ETD complementarity to be performed. Using a data set of CID and ETcaD spectral pairs, we find that at a 1% false discovery rate, the overlap in peptide identifications by CID and ETD is 83%, which is significantly higher than that obtained using either stand-alone Mascot (69%) or OMSSA (39%). We conclude that Mascot Percolator is a highly sensitive and accurate post-search algorithm for peptide identification and allows direct comparison of peptide identifications using multiple alternative fragmentation techniques.

Assessment of Resolution Parameters for CID-Based Shotgun Proteomic Experiments on the LTQ-Orbitrap Mass Spectrometer

Shotgun proteomics has been used extensively for characterization of a number of proteomes. High-resolution Fourier transform mass spectrometry (FTMS) has emerged as a powerful tool owing to its high mass accuracy and resolving power. One of its major limitations, however, is that the confidence level of peptide identification and sensitivity cannot be maximized simultaneously. Although it is generally assumed that higher resolution is better for peptide identifications, the precise effect of varying resolution as a parameter on peptide identification has not yet been systematically evaluated. We used the Escherichia coli proteome and a standard 48 protein mix to study the effect of different resolution parameters on peptide identifications in the setting of a shotgun proteomics experiment on an LTQ-Orbitrap mass spectrometer. We observed a higher number of peptide-spectrum matches (PSMs) whenever the MS scan was carried out by FT and the MS/MS in the ion-trap (IT) with the maximum PSMs obtained at an MS resolution of 30,000. In contrast, when samples were analyzed by FT for both MS and MS/MS, the number of PSMs was significantly lower (approximately 40% compared with FT-IT experiments) with the maximum PSMs obtained when both the MS and MS/MS resolution were set to 15,000. Thus, a 15K-15K resolution setting may provide the best compromise for studies where both speed and accuracy such as high-throughput post-translational analysis and de novo sequencing are important. We hope that our study will allow researchers to choose between different resolution parameters to achieve their desired results from proteomic analyses.