Improving Reproducibility and Sensitivity in Identifying Human Proteins by Shotgun Proteomics

A Framework for Quality Control in Quantitative Proteomics

A thorough evaluation of the quality, reproducibility, and variability of bottom-up proteomics data is necessary at every stage of a workflow, from planning to analysis. We share vignettes applying adaptable quality control (QC) measures to assess sample preparation, system function, and quantitative analysis. System suitability samples are repeatedly measured longitudinally with targeted methods, and we share examples where they are used on three instrument platforms to identify severe system failures and track function over months to years. Internal QCs incorporated at the protein and peptide levels allow our team to assess sample preparation issues and to differentiate system failures from sample-specific issues. External QC samples prepared alongside our experimental samples are used to verify the consistency and quantitative potential of our results during batch correction and normalization before assessing biological phenotypes. We combine these controls with rapid analysis (Skyline), longitudinal QC metrics (AutoQC), and server-based data deposition (PanoramaWeb). We propose that this integrated approach to QC is a useful starting point for groups to facilitate rapid quality control assessment to ensure that valuable instrument time is used to collect the best quality data possible. Data are available on Panorama Public and ProteomeXchange under the identifier PXD051318.

A framework for quality control in quantitative proteomics

A thorough evaluation of the quality, reproducibility, and variability of bottom-up proteomics data is necessary at every stage of a workflow from planning to analysis. We share vignettes applying adaptable quality control (QC) measures to assess sample preparation, system function, and quantitative analysis. System suitability samples are repeatedly measured longitudinally with targeted methods, and we share examples where they are used on three instrument platforms to identify severe system failures and track function over months to years. Internal QCs incorporated at protein and peptide-level allow our team to assess sample preparation issues and to differentiate system failures from sample-specific issues. External QC samples prepared alongside our experimental samples are used to verify the consistency and quantitative potential of our results during batch correction and normalization before assessing biological phenotypes. We combine these controls with rapid analysis (Skyline), longitudinal QC metrics (AutoQC), and server-based data deposition (PanoramaWeb). We propose that this integrated approach to QC is a useful starting point for groups to facilitate rapid quality control assessment to ensure that valuable instrument time is used to collect the best quality data possible. Data are available on Panorama Public and on ProteomeXchange under the identifier PXD051318.

Fluorine-Containing Drug Administration in Rats Results in Fluorination of Selected Proteins in Liver and Brain Tissue

In many pharmaceuticals, a hydrogen atom or hydroxyl group is replaced by a fluorine to increase bioavailability and biostability. The fate of fluorine released from fluorine-containing drugs is not well investigated. The aim of this study was to examine possible fluorination of proteins in rat liver and brain after administration of the fluorinated drug cinacalcet. We assigned 18 Wistar rats to a control group (n = 6) and a group treated with cinacalcet (2 mg kg⁻¹/body weight, 5 days/week), divided into 7 day (n = 6) and 21 day (n = 6) treatment subgroups. Fluorinated proteins were identified using a free proteomics approach; chromatographic separation and analysis by high-resolution mass spectrometry; peptide/protein identification using the Mascot search algorithm; manual verification of an experimentally generated MS/MS spectrum with the theoretical MS/MS spectrum of identified fluorinated peptides. Three fluorinated proteins (spectrin beta chain; carbamoyl-phosphate synthase [ammonia], mitochondrial; 6-phosphofructo-2-kinase/fructose-2, 6-bisphosphatase 1) were identified in the liver and four (spectrin beta chain, dihydropyrimidinase-related protein 4, prominin-2, dihydropyrimidinase-related protein 4) in the brain tissue after 21 days of cinacalcet treatment, but not in the control group. Introduction of fluorine into an organism by administration of fluorinated drugs results in tissue-specific fluorination of proteins.

Suppression of p53 response by targeting p53-Mediator binding with a stapled peptide

DNA-binding transcription factors (TFs) remain challenging to target with molecular probes. Many TFs function in part through interaction with Mediator, a 26-subunit complex that controls RNA polymerase II activity genome-wide. We sought to block p53 function by disrupting the p53-Mediator interaction. Through rational design and activity-based screening, we characterize a stapled peptide, with functional mimics of both p53 activation domains, that blocks p53-Mediator binding and selectively inhibits p53-dependent transcription in human cells; importantly, this "bivalent" peptide has negligible impact, genome-wide, on non-p53 target genes. Our proof-of-concept strategy circumvents the TF entirely and targets the TF-Mediator interface instead, with desired functional outcomes (i.e., selective inhibition of p53 activation). Furthermore, these results demonstrate that TF activation domains represent viable starting points for Mediator-targeting molecular probes, as an alternative to large compound libraries. Different TFs bind Mediator through different subunits, suggesting this strategy could be broadly applied to selectively alter gene expression programs.

Comparative Proteomics Analysis of the Postmitochondrial Supernatant Fraction of Human Lens-Free Whole Eye and Liver

The increasing incidence of ocular diseases has accelerated research into therapeutic interventions needed for the eye. Ocular enzymes play important roles in the metabolism of drugs and endobiotics. Various ocular drugs are designed as prodrugs that are activated by ocular enzymes. Moreover, ocular enzymes have been implicated in the bioactivation of drugs to their toxic metabolites. The key purpose of this study was to compare global proteomes of the pooled samples of the eye (n = 11) and the liver (n = 50) with a detailed analysis of the abundance of enzymes involved in the metabolism of xenobiotics and endobiotics. We used the postmitochondrial supernatant fraction (S9 fraction) of the lens-free whole eye homogenate as a model to allow accurate comparison with the liver S9 fraction. A total of 269 proteins (including 23 metabolic enzymes) were detected exclusively in the pooled eye S9 against 648 proteins in the liver S9 (including 174 metabolic enzymes), whereas 424 proteins (including 94 metabolic enzymes) were detected in both the organs. The major hepatic cytochrome P450 and UDP-glucuronosyltransferases enzymes were not detected, but aldehyde dehydrogenases and glutathione transferases were the predominant proteins in the eye. The comparative qualitative and quantitative proteomics data in the eye versus liver is expected to help in explaining differential metabolic and physiologic activities in the eye. SIGNIFICANCE STATEMENT: Information on the enzymes involved in xenobiotic and endobiotic metabolism in the human eye in relation to the liver is scarcely available. The study employed global proteomic analysis to compare the proteomes of the lens-free whole eye and the liver with a detailed analysis of the enzymes involved in xenobiotic and endobiotic metabolism. These data will help in better understanding of the ocular metabolism and activation of drugs and endobiotics.

A rigorous evaluation of optimal peptide targets for MS-based clinical diagnostics of Coronavirus Disease 2019 (COVID-19)

BACKGROUND

The Coronavirus Disease 2019 (COVID-19) global pandemic has had a profound, lasting impact on the world's population. A key aspect to providing care for those with COVID-19 and checking its further spread is early and accurate diagnosis of infection, which has been generally done via methods for amplifying and detecting viral RNA molecules. Detection and quantitation of peptides using targeted mass spectrometry-based strategies has been proposed as an alternative diagnostic tool due to direct detection of molecular indicators from non-invasively collected samples as well as the potential for high-throughput analysis in a clinical setting; many studies have revealed the presence of viral peptides within easily accessed patient samples. However, evidence suggests that some viral peptides could serve as better indicators of COVID-19 infection status than others, due to potential misidentification of peptides derived from human host proteins, poor spectral quality, high limits of detection etc. METHODS: In this study we have compiled a list of 636 peptides identified from Sudden Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) samples, including from in vitro and clinical sources. These datasets were rigorously analyzed using automated, Galaxy-based workflows containing tools such as PepQuery, BLAST-P, and the Multi-omic Visualization Platform as well as the open-source tools MetaTryp and Proteomics Data Viewer (PDV).

RESULTS

Using PepQuery for confirming peptide spectrum matches, we were able to narrow down the 639-peptide possibilities to 87 peptides that were most robustly detected and specific to the SARS-CoV-2 virus. The specificity of these sequences to coronavirus taxa was confirmed using Unipept and BLAST-P. Through stringent p-value cutoff combined with manual verification of peptide spectrum match quality, 4 peptides derived from the nucleocapsid phosphoprotein and membrane protein were found to be most robustly detected across all cell culture and clinical samples, including those collected non-invasively.

CONCLUSION

We propose that these peptides would be of the most value for clinical proteomics applications seeking to detect COVID-19 from patient samples. We also contend that samples harvested from the upper respiratory tract and oral cavity have the highest potential for diagnosis of SARS-CoV-2 infection from easily collected patient samples using mass spectrometry-based proteomics assays.

A rigorous evaluation of optimal peptide targets for MS-based clinical diagnostics of Coronavirus Disease 2019 (COVID-19)

The Coronavirus Disease 2019 (COVID-19) global pandemic has had a profound, lasting impact on the world's population. A key aspect to providing care for those with COVID-19 and checking its further spread is early and accurate diagnosis of infection, which has been generally done via methods for amplifying and detecting viral RNA molecules. Detection and quantitation of peptides using targeted mass spectrometry-based strategies has been proposed as an alternative diagnostic tool due to direct detection of molecular indicators from non-invasively collected samples as well as the potential for high-throughput analysis in a clinical setting; many studies have revealed the presence of viral peptides within easily accessed patient samples. However, evidence suggests that some viral peptides could serve as better indicators of COVID-19 infection status than others, due to potential misidentification of peptides derived from human host proteins, poor spectral quality, high limits of detection etc. In this study we have compiled a list of 639 peptides identified from Sudden Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) samples, including from in vitro and clinical sources. These datasets were rigorously analyzed using automated, Galaxy-based workflows containing tools such as PepQuery, BLAST-P, and the Multi-omic Visualization Platform as well as the open-source tools MetaTryp and Proteomics Data Viewer (PDV). Using PepQuery for confirming peptide spectrum matches, we were able to narrow down the 639 peptide possibilities to 87 peptides which were most robustly detected and specific to the SARS-CoV-2 virus. The specificity of these sequences to coronavirus taxa was confirmed using Unipept and BLAST-P. Applying stringent statistical scoring thresholds, combined with manual verification of peptide spectrum match quality, 4 peptides derived from the nucleocapsid phosphoprotein and membrane protein were found to be most robustly detected across all cell culture and clinical samples, including those collected non-invasively. We propose that these peptides would be of the most value for clinical proteomics applications seeking to detect COVID-19 from a variety of sample types. We also contend that samples taken from the upper respiratory tract and oral cavity have the highest potential for diagnosis of SARS-CoV-2 infection from easily collected patient samples using mass spectrometry-based proteomics assays.

MicroPOTS Analysis of Barrett's Esophageal Cell Line Models Identifies Proteomic Changes after Physiologic and Radiation Stress

Moving from macroscale preparative systems in proteomics to micro- and nanotechnologies offers researchers the ability to deeply profile smaller numbers of cells that are more likely to be encountered in clinical settings. Herein a recently developed microscale proteomic method, microdroplet processing in one pot for trace samples (microPOTS), was employed to identify proteomic changes in ∼200 Barrett’s esophageal cells following physiologic and radiation stress exposure. From this small population of cells, microPOTS confidently identified >1500 protein groups, and achieved a high reproducibility with a Pearson’s correlation coefficient value of R > 0.9 and over 50% protein overlap from replicates. A Barrett’s cell line model treated with either lithocholic acid (LCA) or X-ray had 21 (e.g., ASNS, RALY, FAM120A, UBE2M, IDH1, ESD) and 32 (e.g., GLUL, CALU, SH3BGRL3, S100A9, FKBP3, AGR2) overexpressed proteins, respectively, compared to the untreated set. These results demonstrate the ability of microPOTS to routinely identify and quantify differentially expressed proteins from limited numbers of cells.

Proteomic profile of pre-implantational ovine embryos produced in vivo

The present study was conducted to decipher the proteome of in vivo-produced pre-implantation ovine embryos. Ten locally adapted Morana Nova ewes received hormonal treatment and were inseminated 12 hr after ovulation. Six days later, 54 embryos (morula and blastocyst developmental state) were recovered from eight ewes and pooled to obtain sufficient protein for proteomic analysis. Extracted embryo proteins were analysed by LC-MS/MS, followed by identification based on four database searches (PEAKS, Proteome Discoverer software, SearchGUI software, PepExplorer). Identified proteins were analysed for gene ontology terms, protein clusters and interactions. Genes associated with the ovine embryo proteome were screened for miRNA targets using data sets of TargetScan (http://www.targetscan.org) and mIRBase (http://www.mirbase.org) servers. There were 667 proteins identified in the ovine embryos. Biological processes of such proteins were mainly related to cellular process and regulation, and molecular functions, to binding and catalytic activity. Analysis of the embryo proteins revealed 49 enriched functional clusters, linked to energy metabolism (TCA cycle, pyruvate and glycolysis metabolism), zona pellucida (ZP), MAPK signalling pathway, tight junction, binding of sperm to ZP, translation, proteasome, cell cycle and calcium/phospholipid binding. Sixteen miRNAs were related to 25 pre-implantation ovine embryo genes, all conserved in human, bovine and ovine species. The interaction network generated by miRNet showed four key miRNAs (hsa-mir-106b-5p; hsa-mir-30-5p; hsa-mir-103a-5p and hsa-mir-106a-5p) with potential interactions with embryo-expressed genes. Functional analysis of the network indicated that miRNAs modulate genes related to cell cycle, regulation of stem cell and embryonic cell differentiation, among others. Retrieved miRNAs also modulate the expression of genes involved in cell signalling pathways, such as MAPK, Wnt, TGF-beta, p53 and Toll-like receptor. The current study describes the first major proteomic profile of 6-day-old ovine embryos produced in vivo, setting a comprehensive foundation for our understanding of embryo physiology in the ovine species.

Multiple functions of TBCK protein in neurodevelopment disorders and tumors

TBC1 domain containing kinase (TBCK) protein is composed of three conserved domains, including N-terminal Serine/Threonine kinase domain, central TBC domain and C-terminal rhodanese homology domain (RHOD). A total of 9 different transcripts (classified as long and short TBCK) generated by alternative splicing have been reported in different cell lines. Exogenous expression of long TBCK has been identified to function as a suppressor of cell growth in certain cell types. On the contrary, TBCK has also been reported to serve a tumor-promoting role in other cell lines, indicating that TBCK might function differentially, depending on the context in different cellular environments. Furthermore, deleterious homozygous or compound heterozygous mutations identified by whole-exome sequencing in the TBCK gene could ablate the function of TBCK, further impacting the mTOR signaling pathway and leading to neurogenetic disorders, such as hypotonia, global developmental delay, facial dysmorphic features and brain abnormalities. However, as a poorly explored protein, there are a lot of studies associated with the functions of TBCK that need to be performed in the future. The present review summarizes data regarding the structural features and potential roles of TBCK in developmental and neurological diseases and tumorigenesis. Future prospects of TBCK research lie in revealing numerous biological functions of TBCK.

Visualizing the agreement of peptide assignments between different search engines

There is a trend in the analysis of shotgun proteomics data that aims to combine information from multiple search engines to increase the number of peptide annotations in an experiment. Typically, the degree of search engine complementarity and search engine agreement is visually illustrated by means of Venn diagrams that present the findings of a database search on the level of the nonredundant peptide annotations. We argue this practice to be not fit-for-purpose since the diagrams do not take into account and often conceal the information on complementarity and agreement at the level of the spectrum identification. We promote a new type of visualization that provides insight on the peptide sequence agreement at the level of the peptide-spectrum match (PSM) as a measure of consensus between two search engines with nominal outcomes. We applied the visualizations and percentage sequence agreement to an in-house data set of our benchmark organism, Caenorhabditis elegans, and illustrated that when assessing the agreement between search engine, one should disentangle the notion of PSM confidence and PSM identity. The visualizations presented in this manuscript provide a more informative assessment of pairs of search engines and are made available as an R function in the Supporting Information.

Decrease in alpha-1 antiproteinase antitrypsin is observed in primary Sjogren's syndrome condition

Primary Sjogren's syndrome (pSS) is a systemic autoimmune disease that is characterized by the infiltration of immune cells. Although the loss of salivary gland function is a major manifestation observed in pSS, the factors that could promote these changes in salivary gland tissue in pSS is not yet determined. Herein, we provide evidence that loss of alpha-1 antiproteinase antitrypsin could contribute to the induction of pSS. Alpha-1 antiproteinase antitrypsin belongs to the family of serpin proteins that function as protease inhibitors and protect secretory cells against proteases, especially to elastases that is secreted from lymphocytes. Importantly, expression of alpha-1 antiproteinase antitrypsin was decreased (more than 3-fold), along with an increase in elastase expression, in pSS samples when compared with age-matched non-SS-SICCA patients. Consistent with the human data, loss of alpha-1 antiproteinase antitrypsin, as well as an increase in immune infiltration, was observed in IL14α transgenic mice that exhibit SS like symptoms. Moreover, an age-dependent increase in elastase expression was observed in IL14α transgenic mice along with a decrease in total saliva secretion. Importantly, a 4-fold increase in microRNA132 expression, but not in other microRNAs, and increased DNA methylation in the promoter/noncoding region of serpina gene was observed in pSS, which could be responsible for the inhibition of alpha-1 antiproteinase antitrypsin expression in salivary gland cells of pSS patients. Together, these findings demonstrate that epigenetic regulations that include DNA methylation and microRNAs that could modulate the expression of alpha-1 antiproteinase antitrypsin in salivary glands and could be involved in the onset of pSS.

Comparative Analysis of Erythrocyte Proteomes of Water Buffalo, Dairy Cattle, and Beef Cattle by Shotgun LC-MS/MS

A number of studies have demonstrated that Babesia orientalis (B. orientalis) can only infect water buffalo (Bubalus bubalis) and not dairy cattle (Bos taurus) or beef cattle (Bos taurus), even though all three belong to the tribe Bovini and have close evolutionary relationships. In addition, Babesia species are intracellular protozoans that obligately parasitize in erythrocytes. This may indicate that the infection specificity is due to differences in erythrocyte proteins. Totals of 491, 1,143, and 1,145 proteins were identified from water buffalo, beef cattle, and dairy cattle, respectively, by searching the Uniprot and NCBI databases. The number of proteins identified for water buffalo was far lower than for beef cattle and dairy cattle, particularly in the range from 15 to 25 kDa. Remarkably, 290 identified proteins were unique to water buffalo, of which putative gamma-globin and putative epsilon-globin had a significant possibility of being relevant to the survival of B. orientalis only in water buffalo. A total of 2,222 proteins were annotated in terms of molecular function, biological process, and cellular component according to GO annotation. The number of proteins of water buffalo in oxygen binding was far higher than for beef cattle and dairy cattle. This is the first time that the protein profiles of water buffalo, beef cattle, and dairy cattle have been comparatively analyzed. The uniquely expressed proteins in water buffalo obtained in this study may provide new insights into the mechanism of B. orientalis infection exclusivity in water buffalo and may be a benefit for the development of strategies against B. orientalis.

Challenges and emergent solutions for LC-MS/MS based untargeted metabolomics in diseases

In the past decade, advances in liquid chromatography-mass spectrometry (LC-MS) have revolutionized untargeted metabolomics analyses. By mining metabolomes more deeply, researchers are now primed to uncover key metabolites and their associations with diseases. The employment of untargeted metabolomics has led to new biomarker discoveries and a better mechanistic understanding of diseases with applications in precision medicine. However, many major pertinent challenges remain. First, compound identification has been poor, and left an overwhelming number of unidentified peaks. Second, partial, incomplete metabolomes persist due to factors such as limitations in mass spectrometry data acquisition speeds, wide-range of metabolites concentrations, and cellular/tissue/temporal-specific expression changes that confound our understanding of metabolite perturbations. Third, to contextualize metabolites in pathways and biology is difficult because many metabolites partake in multiple pathways, have yet to be described species specificity, or possess unannotated or more-complex functions that are not easily characterized through metabolomics analyses. From a translational perspective, information related to novel metabolite biomarkers, metabolic pathways, and drug targets might be sparser than they should be. Thankfully, significant progress has been made and novel solutions are emerging, achieved through sustained academic and industrial community efforts in terms of hardware, computational, and experimental approaches. Given the rapidly growing utility of metabolomics, this review will offer new perspectives, increase awareness of the major challenges in LC-MS metabolomics that will significantly benefit the metabolomics community and also the broader the biomedical community metabolomics aspire to serve.

Protein Content of Cervicovaginal Fluid Is Altered During Bacterial Vaginosis

OBJECTIVES

The aim of the study was to compare, using a proteomic approach, cervicovaginal fluid (CVF) proteins of women with bacterial vaginosis (BV) with those presenting normal microbiota.

MATERIALS AND METHODS

A total of 309 reproductive-aged women were cross-sectionally enrolled. Participants were tested for vaginal candidosis, Trichomonas vaginalis, Chlamydia trachomatis, and Neisseria gonorrhoeae and excluded if positive. Vaginal microbiota was classified microscopically according to Nugent criteria in normal, intermediate, and BV. Randomly selected CVF samples of 29 women with BV and an equal number with normal microbiota were subjected to proteomic analysis. Thus, a total of 58 CVF samples were evaluated using shotgun liquid chromatography-tandem mass spectrometry in a Q-Tof PREMIER API mass spectrometer (MicroMass/Waters) for peptide detection and relative quantification.

RESULTS

Of the 309 women enrolled, 63 (20.4%) were excluded after testing positive for at least one of the tested co-infections or because of low-quality samples. Microscopic classification of vaginal microbiota on the remaining 246 samples revealed that 132 women (53.6%) had normal microbiota, 33 (13.4%) had intermediate microbiota, and 81 (33.0%) had BV. Proteomic analysis of CVF of 58 randomly selected women with normal microbiota (n = 29) or BV (n = 29) successfully identified 74 proteins. In addition, the comparison of abundance of those proteins between the groups showed that the following five (6.7%) were enriched in BV: neutrophil elastase, kaliocin-1, neutrophil defensin-1, Ig lambda-2 chain C regions, and protein S100-A7. All of which have a recognized role in host's immunity.

CONCLUSIONS

Exclusive finding of BV affects immunity-related CVF components of reproductive-aged women.

Sequence-Specific Model for Peptide Retention Time Prediction in Strong Cation Exchange Chromatography

The development of a peptide retention prediction model for strong cation exchange (SCX) separation on a Polysulfoethyl A column is reported. Off-line 2D LC-MS/MS analysis (SCX-RPLC) of S. cerevisiae whole cell lysate was used to generate a retention dataset of ∼30 000 peptides, sufficient for identifying the major sequence-specific features of peptide retention mechanisms in SCX. In contrast to RPLC/hydrophilic interaction liquid chromatography (HILIC) separation modes, where retention is driven by hydrophobic/hydrophilic contributions of all individual residues, SCX interactions depend mainly on peptide charge (number of basic residues at acidic pH) and size. An additive model (incorporating the contributions of all 20 residues into the peptide retention) combined with a peptide length correction produces a 0.976 R² value prediction accuracy, significantly higher than the additive models for either HILIC or RPLC. Position-dependent effects on peptide retention for different residues were driven by the spatial orientation of tryptic peptides upon interaction with the negatively charged surface functional groups. The positively charged N-termini serve as a primary point of interaction. For example, basic residues (Arg, His, Lys) increase peptide retention when located closer to the N-terminus. We also found that hydrophobic interactions, which could lead to a mixed-mode separation mechanism, are largely suppressed at 20-30% of acetonitrile in the eluent. The accuracy of the final Sequence-Specific Retention Calculator (SSRCalc) SCX model (∼0.99 R² value) exceeds all previously reported predictors for peptide LC separations. This also provides a solid platform for method development in 2D LC-MS protocols in proteomics and peptide retention prediction filtering of false positive identifications.

Alternative splicing shapes transcriptome but not proteome diversity in Physcomitrella patens

Alternative splicing (AS) can significantly impact the transcriptome and proteome of a eukaryotic cell. Here, using transcriptome and proteome profiling data, we analyzed AS in two life forms of the model moss Physcomitrella patens, namely protonemata and gametophores, as well as in protoplasts. We identified 12 043 genes subject to alternative splicing and analyzed the extent to which AS contributes to proteome diversity. We could distinguish a few examples that unambiguously indicated the presence of two or more splice isoforms from the same locus at the proteomic level. Our results indicate that alternative isoforms have a small effect on proteome diversity. We also revealed that mRNAs and pre-mRNAs have thousands of complementary binding sites for long non-coding RNAs (lncRNAs) that may lead to potential interactions in transcriptome. This finding points to an additional level of gene expression and AS regulation by non-coding transcripts in Physcomitrella patens. Among the differentially expressed and spliced genes we found serine/arginine-rich (SR) genes, which are known to regulate AS in cells. We found that treatment with abscisic (ABA) and methyl jasmonic acids (MeJA) led to an isoform-specific response and suggested that ABA in gametophores and MeJA in protoplasts regulate AS and the transcription of SR genes.

hEIDI: An Intuitive Application Tool To Organize and Treat Large-Scale Proteomics Data

Advances in high-throughput proteomics have led to a rapid increase in the number, size, and complexity of the associated data sets. Managing and extracting reliable information from such large series of data sets require the use of dedicated software organized in a consistent pipeline to reduce, validate, exploit, and ultimately export data. The compilation of multiple mass-spectrometry-based identification and quantification results obtained in the context of a large-scale project represents a real challenge for developers of bioinformatics solutions. In response to this challenge, we developed a dedicated software suite called hEIDI to manage and combine both identifications and semiquantitative data related to multiple LC-MS/MS analyses. This paper describes how, through a user-friendly interface, hEIDI can be used to compile analyses and retrieve lists of nonredundant protein groups. Moreover, hEIDI allows direct comparison of series of analyses, on the basis of protein groups, while ensuring consistent protein inference and also computing spectral counts. hEIDI ensures that validated results are compliant with MIAPE guidelines as all information related to samples and results is stored in appropriate databases. Thanks to the database structure, validated results generated within hEIDI can be easily exported in the PRIDE XML format for subsequent publication. hEIDI can be downloaded from http://biodev.extra.cea.fr/docs/heidi .

Proteoform-Specific Insights into Cellular Proteome Regulation

Knowledge regarding compositions of proteomes at the proteoform level enhances insights into cellular phenotypes. A strategy is described herein for discovery of proteoform-specific information about cellular proteomes. This strategy involved analysis of data obtained by bottom-up mass spectrometry of multiple protein OGE separations on a fraction by fraction basis. The strategy was exemplified using five matched sets of lysates of uninfected and human respiratory syncytial virus-infected A549 cells. Template matching demonstrated that 67.3% of 10475 protein profiles identified focused to narrow pI windows indicative of efficacious focusing. Furthermore, correlation between experimental and theoretical pI gradients indicated reproducible focusing. Based on these observations a proteoform profiling strategy was developed to identify proteoforms, detect proteoform diversity and discover potential proteoform regulation. One component of this strategy involved examination of the focusing profiles for protein groups. A novel concordance analysis facilitated differentiation between proteoforms, including proteoforms generated by alternate splicing and proteolysis. Evaluation of focusing profiles and concordance analysis were applicable to cells from a single and/or multiple biological states. Statistical analyses identified proteoform variation between biological states. Regulation relevant to cellular responses to human respiratory syncytial virus was revealed. Western blotting and Protomap analyses validated the proteoform regulation. Discovery of STAT1, WARS, MX1, and HSPB1 proteoform regulation by human respiratory syncytial virus highlighted the impact of the profiling strategy. Novel truncated proteoforms of MX1 were identified in infected cells and phosphorylation driven regulation of HSPB1 proteoforms was correlated with infection. The proteoform profiling strategy is generally applicable to investigating interactions between viruses and host cells and the analysis of other biological systems.

l2 Multiple Kernel Fuzzy SVM-Based Data Fusion for Improving Peptide Identification

SEQUEST is a database-searching engine, which calculates the correlation score between observed spectrum and theoretical spectrum deduced from protein sequences stored in a flat text file, even though it is not a relational and object-oriental repository. Nevertheless, the SEQUEST score functions fail to discriminate between true and false PSMs accurately. Some approaches, such as PeptideProphet and Percolator, have been proposed to address the task of distinguishing true and false PSMs. However, most of these methods employ time-consuming learning algorithms to validate peptide assignments [1] . In this paper, we propose a fast algorithm for validating peptide identification by incorporating heterogeneous information from SEQUEST scores and peptide digested knowledge. To automate the peptide identification process and incorporate additional information, we employ l2 multiple kernel learning (MKL) to implement the current peptide identification task. Results on experimental datasets indicate that compared with state-of-the-art methods, i.e., PeptideProphet and Percolator, our data fusing strategy has comparable performance but reduces the running time significantly.

Systematic Errors in Peptide and Protein Identification and Quantification by Modified Peptides

The principle of shotgun proteomics is to use peptide mass spectra in order to identify corresponding sequences in a protein database. The quality of peptide and protein identification and quantification critically depends on the sensitivity and specificity of this assignment process. Many peptides in proteomic samples carry biochemical modifications, and a large fraction of unassigned spectra arise from modified peptides. Spectra derived from modified peptides can erroneously be assigned to wrong amino acid sequences. However, the impact of this problem on proteomic data has not yet been investigated systematically. Here we use combinations of different database searches to show that modified peptides can be responsible for 20-50% of false positive identifications in deep proteomic data sets. These false positive hits are particularly problematic as they have significantly higher scores and higher intensities than other false positive matches. Furthermore, these wrong peptide assignments lead to hundreds of false protein identifications and systematic biases in protein quantification. We devise a "cleaned search" strategy to address this problem and show that this considerably improves the sensitivity and specificity of proteomic data. In summary, we show that modified peptides cause systematic errors in peptide and protein identification and quantification and should therefore be considered to further improve the quality of proteomic data annotation.

Monitoring host responses to the gut microbiota

The gastrointestinal (GI) ecosystem is increasingly understood to be a fundamental component of health, and has been identified as a new focal point for diagnosing, correcting and preventing countless disorders. Shotgun DNA sequencing has emerged as the dominant technology for determining the genetic and microbial composition of the gut microbiota. This technology has linked microbiota dysbioses to numerous GI diseases including inflammatory bowel disease, obesity and allergy, and to non-GI diseases like autism and depression. The importance of establishing causality in the deterioration of the host-microbiota relationship is well appreciated; however, discovery of candidate molecules and pathways that underlie mechanisms remains a major challenge. Targeted approaches, transcriptional assays, cytokine panels and imaging analyses, applied to animals, have yielded important insight into host responses to the microbiota. However, non-invasive, hypothesis-independent means of measuring host responses in humans are necessary to keep pace with similarly unbiased sequencing efforts that monitor microbes. Mass spectrometry-based proteomics has served this purpose in many other fields, but stool proteins exist in such diversity and dynamic range as to overwhelm conventional proteomics technologies. Focused analysis of host protein secretion into the gut lumen and monitoring proteome-level dynamics in stool provides a tractable route toward non-invasively evaluating dietary, microbial, surgical or pharmacological intervention efficacies. This review is intended to guide GI biologists and clinicians through the methods currently used to elucidate host responses in the gut, with a specific focus on mass spectrometry-based shotgun proteomics applied to the study of host protein dynamics within the GI ecosystem.

Use of composite protein database including search result sequences for mass spectrometric analysis of cell secretome

Mass spectrometric (MS) data of human cell secretomes are usually run through the conventional human database for identification. However, the search may result in false identifications due to contamination of the secretome with fetal bovine serum (FBS) proteins. To overcome this challenge, here we provide a composite protein database including human as well as 199 FBS protein sequences for MS data search of human cell secretomes. Searching against the human-FBS database returned more reliable results with fewer false-positive and false-negative identifications compared to using either a human only database or a human-bovine database. Furthermore, the improved results validated our strategy without complex experiments like SILAC. We expect our strategy to improve the accuracy of human secreted protein identification and to also add value for general use.

QC metrics from CPTAC raw LC-MS/MS data interpreted through multivariate statistics

Shotgun proteomics experiments integrate a complex sequence of processes, any of which can introduce variability. Quality metrics computed from LC-MS/MS data have relied upon identifying MS/MS scans, but a new mode for the QuaMeter software produces metrics that are independent of identifications. Rather than evaluating each metric independently, we have created a robust multivariate statistical toolkit that accommodates the correlation structure of these metrics and allows for hierarchical relationships among data sets. The framework enables visualization and structural assessment of variability. Study 1 for the Clinical Proteomics Technology Assessment for Cancer (CPTAC), which analyzed three replicates of two common samples at each of two time points among 23 mass spectrometers in nine laboratories, provided the data to demonstrate this framework, and CPTAC Study 5 provided data from complex lysates under Standard Operating Procedures (SOPs) to complement these findings. Identification-independent quality metrics enabled the differentiation of sites and run-times through robust principal components analysis and subsequent factor analysis. Dissimilarity metrics revealed outliers in performance, and a nested ANOVA model revealed the extent to which all metrics or individual metrics were impacted by mass spectrometer and run time. Study 5 data revealed that even when SOPs have been applied, instrument-dependent variability remains prominent, although it may be reduced, while within-site variability is reduced significantly. Finally, identification-independent quality metrics were shown to be predictive of identification sensitivity in these data sets. QuaMeter and the associated multivariate framework are available from http://fenchurch.mc.vanderbilt.edu and http://homepages.uc.edu/~wang2x7/, respectively.

Population proteomics: addressing protein diversity in humans

In the past several years, proteomics and its subdiscipline clinical proteomics have been engaged in the discovery of the next generation protein of biomarkers. As the effort and the intensive debate it has sparked continue, it is becoming apparent that a paradigm shift is needed in proteomics in order to truly comprehend the complexity of the human proteome and assess its subtle variations among individuals. This review introduces the concept of population proteomics as a future direction in proteomics research. Population proteomics is the study of protein diversity in human populations. High-throughput, top-down mass spectrometric approaches are employed to investigate, define and understand protein diversity and modulations across and within populations. Population proteomics is a discovery-oriented endeavor with a goal of establishing the incidence of protein structural variations and quantitative regulation of these modifications. Assessing human protein variations among and within populations is viewed as a paramount undertaking that can facilitate clinical proteomics' effort in discovery and validation of protein features that can be used as markers for early diagnosis of disease, monitoring of disease progression and assessment of therapy. This review outlines the growing need for analyzing individuals' proteomes and describes the approaches that are likely to be applied in such a population proteomics endeavor.

MiR-138 downregulates miRNA processing in HeLa cells by targeting RMND5A and decreasing Exportin-5 stability

MicroRNAs (miRNAs) are a class of non-coding small RNAs that consist of ∼22 nt and are involved in several biological processes by regulating target gene expression. MiR-138 has many biological functions and is often downregulated in cancers. Our results showed that overexpression of miR-138 downregulated target RMND5A (required for meiotic nuclear division 5 homolog A) and reduced Exportin-5 stability, which results in decreased levels of pre-miRNA nuclear export in HeLa cells. We also found that miR-138 could significantly inhibit HeLa cell migration by targeting RMND5A. Our study therefore identifies miR-138–RMND5A–Exportin-5 as a previously unknown miRNA processing regulatory pathway in HeLa cells.

Proteomic analysis of seminal fluid from men exhibiting oxidative stress

BACKGROUND

Seminal plasma serves as a natural reservoir of antioxidants. It helps to remove excessive formation of reactive oxygen species (ROS) and consequently, reduce oxidative stress. Proteomic profiling of seminal plasma proteins is important to understand the molecular mechanisms underlying oxidative stress and sperm dysfunction in infertile men.

METHODS

This prospective study consisted of 52 subjects: 32 infertile men and 20 healthy donors. Once semen and oxidative stress parameters were assessed (ROS, antioxidant concentration and DNA damage), the subjects were categorized into ROS positive (ROS+) or ROS negative (ROS-). Seminal plasma from each group was pooled and subjected to proteomics analysis. In-solution digestion and protein identification with liquid chromatography tandem mass spectrometry (LC-MS/MS), followed by bioinformatics analyses was used to identify and characterize potential biomarker proteins.

RESULTS

A total of 14 proteins were identified in this analysis with 7 of these common and unique proteins were identified in both the ROS+ and ROS- groups through MASCOT and SEQUEST analyses, respectively. Prolactin-induced protein was found to be more abundantly present in men with increased levels of ROS. Gene ontology annotations showed extracellular distribution of proteins with a major role in antioxidative activity and regulatory processes.

CONCLUSIONS

We have identified proteins that help protect against oxidative stress and are uniquely present in the seminal plasma of the ROS- men. Men exhibiting high levels of ROS in their seminal ejaculate are likely to exhibit proteins that are either downregulated or oxidatively modified, and these could potentially contribute to male infertility.

Host-centric proteomics of stool: a novel strategy focused on intestinal responses to the gut microbiota

The diverse community of microbes that inhabits the human bowel is vitally important to human health. Host-expressed proteins are essential for maintaining this mutualistic relationship and serve as reporters on the status of host-microbiota interaction. Therefore, unbiased and sensitive methods focused on host proteome characterization are needed. Herein we describe a novel method for applying shotgun proteomics to the analysis of feces, focusing on the secreted host proteome. We have conducted the most complete analysis of the extracellular mouse gut proteome to date by employing a gnotobiotic mouse model. Using mice colonized with defined microbial communities of increasing complexity or a complete human microbiota ('humanized'), we show that the complexity of the host stool proteome mirrors the complexity of microbiota composition. We further show that host responses exhibit signatures specific to the different colonization states. We demonstrate feasibility of this approach in human stool samples and provide evidence for a "core" stool proteome as well as personalized host response features. Our method provides a new avenue for noninvasive monitoring of host-microbiota interaction dynamics via host-produced proteins in stool.

Characterization of a high field Orbitrap mass spectrometer for proteome analysis

The field of proteomics continues to be driven by improvements in analytical technology, notably in peptide separation, quantitative MS, and informatics. In this study, we have characterized a hybrid linear ion trap high field Orbitrap mass spectrometer (Orbitrap Elite) for proteomic applications. The very high resolution available on this instrument allows 95% of all peptide masses to be measured with sub-ppm accuracy that in turn improves protein identification by database searching. We further confirm again that mass accuracy in tandem mass spectra is a valuable parameter for improving the success of protein identification. The new CID rapid scan type of the Orbitrap Elite achieves similar performance as higher energy collision induced dissociation fragmentation and both allow the identification of hundreds of proteins from as little as 0.1 ng of protein digest on column. The new instrument outperforms its predecessor the Orbitrap Velos by a considerable margin on each metric assessed that makes it a valuable and versatile tool for MS-based proteomics.

Extending the coverage of spectral libraries: a neighbor-based approach to predicting intensities of peptide fragmentation spectra

Searching spectral libraries in MS/MS is an important new approach to improving the quality of peptide and protein identification. The idea relies on the observation that ion intensities in an MS/MS spectrum of a given peptide are generally reproducible across experiments, and thus, matching between spectra from an experiment and the spectra of previously identified peptides stored in a spectral library can lead to better peptide identification compared to the traditional database search. However, the use of libraries is greatly limited by their coverage of peptide sequences: even for well-studied organisms a large fraction of peptides have not been previously identified. To address this issue, we propose to expand spectral libraries by predicting the MS/MS spectra of peptides based on the spectra of peptides with similar sequences. We first demonstrate that the intensity patterns of dominant fragment ions between similar peptides tend to be similar. In accordance with this observation, we develop a neighbor-based approach that first selects peptides that are likely to have spectra similar to the target peptide and then combines their spectra using a weighted K-nearest neighbor method to accurately predict fragment ion intensities corresponding to the target peptide. This approach has the potential to predict spectra for every peptide in the proteome. When rigorous quality criteria are applied, we estimate that the method increases the coverage of spectral libraries available from the National Institute of Standards and Technology by 20-60%, although the values vary with peptide length and charge state. We find that the overall best search performance is achieved when spectral libraries are supplemented by the high quality predicted spectra.

Computational approaches to protein inference in shotgun proteomics

Shotgun proteomics has recently emerged as a powerful approach to characterizing proteomes in biological samples. Its overall objective is to identify the form and quantity of each protein in a high-throughput manner by coupling liquid chromatography with tandem mass spectrometry. As a consequence of its high throughput nature, shotgun proteomics faces challenges with respect to the analysis and interpretation of experimental data. Among such challenges, the identification of proteins present in a sample has been recognized as an important computational task. This task generally consists of (1) assigning experimental tandem mass spectra to peptides derived from a protein database, and (2) mapping assigned peptides to proteins and quantifying the confidence of identified proteins. Protein identification is fundamentally a statistical inference problem with a number of methods proposed to address its challenges. In this review we categorize current approaches into rule-based, combinatorial optimization and probabilistic inference techniques, and present them using integer programming and Bayesian inference frameworks. We also discuss the main challenges of protein identification and propose potential solutions with the goal of spurring innovative research in this area.

Imputation of exome sequence variants into population- based samples and blood-cell-trait-associated loci in African Americans: NHLBI GO Exome Sequencing Project

Researchers have successfully applied exome sequencing to discover causal variants in selected individuals with familial, highly penetrant disorders. We demonstrate the utility of exome sequencing followed by imputation for discovering low-frequency variants associated with complex quantitative traits. We performed exome sequencing in a reference panel of 761 African Americans and then imputed newly discovered variants into a larger sample of more than 13,000 African Americans for association testing with the blood cell traits hemoglobin, hematocrit, white blood count, and platelet count. First, we illustrate the feasibility of our approach by demonstrating genome-wide-significant associations for variants that are not covered by conventional genotyping arrays; for example, one such association is that between higher platelet count and an MPL c.117G>T (p.Lys39Asn) variant encoding a p.Lys39Asn amino acid substitution of the thrombopoietin receptor gene (p = 1.5 × 10(-11)). Second, we identified an association between missense variants of LCT and higher white blood count (p = 4 × 10(-13)). Third, we identified low-frequency coding variants that might account for allelic heterogeneity at several known blood cell-associated loci: MPL c.754T>C (p.Tyr252His) was associated with higher platelet count; CD36 c.975T>G (p.Tyr325(∗)) was associated with lower platelet count; and several missense variants at the α-globin gene locus were associated with lower hemoglobin. By identifying low-frequency missense variants associated with blood cell traits not previously reported by genome-wide association studies, we establish that exome sequencing followed by imputation is a powerful approach to dissecting complex, genetically heterogeneous traits in large population-based studies.

SIR: Deterministic protein inference from peptides assigned to MS data

Currently the bottom up approach is the most popular for characterizing protein samples by mass spectrometry. This is mainly attributed to the fact that the bottom up approach has been successfully optimized for high throughput studies. However, the bottom up approach is associated with a number of challenges such as loss of linkage information between peptides. Previous publications have addressed some of these problems which are commonly referred to as protein inference. Nevertheless, all previous publications on the subject are oversimplified and do not represent the full complexity of the proteins identified. To this end we present here SIR (spectra based isoform resolver) that uses a novel transparent and systematic approach for organizing and presenting identified proteins based on peptide spectra assignments. The algorithm groups peptides and proteins into five evidence groups and calculates sixteen parameters for each identified protein that are useful for cases where deterministic protein inference is the goal. The novel approach has been incorporated into SIR which is a user-friendly tool only concerned with protein inference based on imports of Mascot search results. SIR has in addition two visualization tools that facilitate further exploration of the protein inference problem.

AN ITERATIVE ALGORITHM TO QUANTIFY FACTORS INFLUENCING PEPTIDE FRAGMENTATION DURING TANDEM MASS SPECTROMETRY

In protein identification by tandem mass spectrometry, it is critical to accurately predict the theoretical spectrum for a peptide sequence. To date, the widely-used database searching methods adopted simple statistical models for predicting. For some peptide, these models usually yield a theoretical spectrum with a significant deviation from the experimental one. In this paper, in order to derive an improved predicting model, we utilized a non-linear programming model to quantify the factors impacting peptide fragmentation. Then, an iterative algorithm was proposed to solve this optimization problem. Upon a training set of 1803 spectra, the experimental result showed a good agreement with some known principles about peptide fragmentation, such as the tendency to cleave at the middle of peptide, and Pro's preference of the N-terminal cleavage. Moreover, upon a testing set of 941 spectra, comparison of the predicted spectra against the experimental ones showed that this method can generate reasonable predictions. The results in this paper can offer help to both database searching and de novo methods.

Generic comparison of protein inference engines

Protein identifications, instead of peptide-spectrum matches, constitute the biologically relevant result of shotgun proteomics studies. How to appropriately infer and report protein identifications has triggered a still ongoing debate. This debate has so far suffered from the lack of appropriate performance measures that allow us to objectively assess protein inference approaches. This study describes an intuitive, generic and yet formal performance measure and demonstrates how it enables experimentalists to select an optimal protein inference strategy for a given collection of fragment ion spectra. We applied the performance measure to systematically explore the benefit of excluding possibly unreliable protein identifications, such as single-hit wonders. Therefore, we defined a family of protein inference engines by extending a simple inference engine by thousands of pruning variants, each excluding a different specified set of possibly unreliable identifications. We benchmarked these protein inference engines on several data sets representing different proteomes and mass spectrometry platforms. Optimally performing inference engines retained all high confidence spectral evidence, without posterior exclusion of any type of protein identifications. Despite the diversity of studied data sets consistently supporting this rule, other data sets might behave differently. In order to ensure maximal reliable proteome coverage for data sets arising in other studies we advocate abstaining from rigid protein inference rules, such as exclusion of single-hit wonders, and instead consider several protein inference approaches and assess these with respect to the presented performance measure in the specific application context.

Data analysis strategy for maximizing high-confidence protein identifications in complex proteomes such as human tumor secretomes and human serum

Detection of biologically interesting, low-abundance proteins in complex proteomes such as serum typically requires extensive fractionation and high-performance mass spectrometers. Processing of the resulting large data sets involves trade-offs between confidence of identification and depth of protein coverage; that is, higher stringency filters preferentially reduce the number of low-abundance proteins identified. In the current study, an alternative database search and results filtering strategies were evaluated using test samples ranging from purified proteins to ovarian tumor secretomes and human serum to maximize peptide and protein coverage. Full and partial tryptic searches were compared because substantial numbers of partial tryptic peptides were observed in all samples, and the proportion of partial tryptic peptides was particularly high for serum. When data filters that yielded similar false discovery rates (FDR) were used, full tryptic searches detected far fewer peptides than partial tryptic searches. In contrast to the common practice of using full tryptic specificity and a narrow precursor mass tolerance, more proteins and peptides could be confidently identified using a partial tryptic database search with a 100 ppm precursor mass tolerance followed by filtering of results using 10 ppm mass error and full tryptic boundaries.

Bifunctionalized SBA-15 as a novel micropipette tip sorbent for selective removal and enrichment of biomolecules

Selective enrichment or removal of specific proteins prior to analysis can minimize nonspecific interferences as well as information loss, which has been an important issue in current proteomics fields. In this work, two kinds of mesoporous silica SBA-15 (mean pore diameter of 5 nm and 7 nm), bifunctionalized with quaternary ammonium and C18 groups via silylanization reaction, was used to investigate the adsorption behavior for different proteins (bovine serum albumin (BSA), ovalbumin (OVA), hemoglobin (Hb), lysozyme (Lys) and cytochrome c (cyt c)). As possessing anion exchange (quaternary ammonium) groups, the bifunctionalized SBA-15 showed selective adsorption of the negative charged proteins, BSA and OVA. Based on these properties, sequential fractionation based on different SBA-15 materials as micropipette tip sorbents was developed for sample preparation prior to protein analysis. As expected, after the sequential treatment of the sample, the detection signal of the high abundant BSA was significantly decreased and that of the low abundant cyt c was obviously enlarged, which solved the problem that low abundant protein was suppressed by adjacent high abundant protein. The sample preparation technique significantly improved protein identification and this sequential fractionation approach could be potentially applied to extend information on the protein identification for biological samples with a wide dynamic range.

IsoformResolver: A peptide-centric algorithm for protein inference

When analyzing proteins in complex samples using tandem mass spectrometry of peptides generated by proteolysis, the inference of proteins can be ambiguous, even with well-validated peptides. Unresolved questions include whether to show all possible proteins vs a minimal list, what to do when proteins are inferred ambiguously, and how to quantify peptides that bridge multiple proteins, each with distinguishing evidence. Here we describe IsoformResolver, a peptide-centric protein inference algorithm that clusters proteins in two ways, one based on peptides experimentally identified from MS/MS spectra, and the other based on peptides derived from an in silico digest of the protein database. MS/MS-derived protein groups report minimal list proteins in the context of all possible proteins, without redundantly listing peptides. In silico-derived protein groups pull together functionally related proteins, providing stable identifiers. The peptide-centric grouping strategy used by IsoformResolver allows proteins to be displayed together when they share peptides in common, providing a comprehensive yet concise way to organize protein profiles. It also summarizes information on spectral counts and is especially useful for comparing results from multiple LC–MS/MS experiments. Finally, we examine the relatedness of proteins within IsoformResolver groups and compare its performance to other protein inference software.

IsoformResolver addresses problems in protein inference using a peptide-centric protein inference strategy. Inferred proteins are reported in the context of two types of protein groups, based on peptides observed from MS/MS spectra, and from an in silico digest of the protein database. This allows for complete and concise output, without replicated peptides, and counteracting volatility caused by protein inference. IsoformResolver algorithms and compare profile output are presented.

Spectrum-to-spectrum searching using a proteome-wide spectral library

The unambiguous assignment of tandem mass spectra (MS/MS) to peptide sequences remains a key unsolved problem in proteomics. Spectral library search strategies have emerged as a promising alternative for peptide identification, in which MS/MS spectra are directly compared against a reference library of confidently assigned spectra. Two problems relate to library size. First, reference spectral libraries are limited to rediscovery of previously identified peptides and are not applicable to new peptides, because of their incomplete coverage of the human proteome. Second, problems arise when searching a spectral library the size of the entire human proteome. We observed that traditional dot product scoring methods do not scale well with spectral library size, showing reduction in sensitivity when library size is increased. We show that this problem can be addressed by optimizing scoring metrics for spectrum-to-spectrum searches with large spectral libraries. MS/MS spectra for the 1.3 million predicted tryptic peptides in the human proteome are simulated using a kinetic fragmentation model (MassAnalyzer version2.1) to create a proteome-wide simulated spectral library. Searches of the simulated library increase MS/MS assignments by 24% compared with Mascot, when using probabilistic and rank based scoring methods. The proteome-wide coverage of the simulated library leads to 11% increase in unique peptide assignments, compared with parallel searches of a reference spectral library. Further improvement is attained when reference spectra and simulated spectra are combined into a hybrid spectral library, yielding 52% increased MS/MS assignments compared with Mascot searches. Our study demonstrates the advantages of using probabilistic and rank based scores to improve performance of spectrum-to-spectrum search strategies.