msproteomics sitereport: reporting DIA-MS phosphoproteomics experiments at site level with ease.

SUMMARY: Identification and quantification of phosphorylation sites are essential for biological interpretation of a phosphoproteomics experiment. For data independent acquisition mass spectrometry-based (DIA-MS) phosphoproteomics, extracting a site-level report from the output of current processing software is not straightforward as multiple peptides might contribute to a single site, multiple phosphorylation sites can occur on the same peptides, and protein isoforms complicate site specification. Currently only limited support is available from a commercial software package via a platform-specific solution with a rather simple site quantification method. Here, we present sitereport, a software tool implemented in an extendable Python package called msproteomics to report phosphosites and phosphopeptides from a DIA-MS phosphoproteomics experiment with a proven quantification method called MaxLFQ. We demonstrate the use of sitereport for downstream data analysis at site level, allowing benchmarking different DIA-MS processing software tools. AVAILABILITY AND IMPLEMENTATION: sitereport is available as a command line tool in the Python package msproteomics, released under the Apache License 2.0 and available from the Python Package Index (PyPI) at https://pypi.org/project/msproteomics and GitHub at https://github.com/tvpham/msproteomics.

Kinase activities in pancreatic ductal adenocarcinoma with prognostic and therapeutic avenues.

Pancreatic ductal adenocarcinoma (PDAC) is a devastating disease with a limited number of known driver mutations but considerable cancer cell heterogeneity. Phosphoproteomics provides a direct read-out of aberrant signaling and the resultant clinically relevant phenotype. Mass spectrometry (MS)-based proteomics and phosphoproteomics were applied to 42 PDAC tumors. Data encompassed over 19 936 phosphoserine or phosphothreonine (pS/T; in 5412 phosphoproteins) and 1208 phosphotyrosine (pY; in 501 phosphoproteins) sites and a total of 3756 proteins. Proteome data identified three distinct subtypes with tumor intrinsic and stromal features. Subsequently, three phospho-subtypes were apparent: two tumor intrinsic (Phos1/2) and one stromal (Phos3), resembling known PDAC molecular subtypes. Kinase activity was analyzed by the Integrative iNferred Kinase Activity (INKA) scoring. Phospho-subtypes displayed differential phosphorylation signals and kinase activity, such as FGR and GSK3 activation in Phos1, SRC kinase family and EPHA2 in Phos2, and EGFR, INSR, MET, ABL1, HIPK1, JAK, and PRKCD in Phos3. Kinase activity analysis of an external PDAC cohort supported our findings and underscored the importance of PI3K/AKT and ERK pathways, among others. Interestingly, unfavorable patient prognosis correlated with higher RTK, PAK2, STK10, and CDK7 activity and high proliferation, whereas long survival was associated with MYLK and PTK6 activity, which was previously unknown. Subtype-associated activity profiles can guide therapeutic combination approaches in tumor and stroma-enriched tissues, and emphasize the critical role of parallel signaling pathways. In addition, kinase activity profiling identifies potential disease markers with prognostic significance.

Candidate biomarkers for treatment benefit from sunitinib in patients with advanced renal cell carcinoma using mass spectrometry-based (phospho)proteomics.

The tyrosine kinase inhibitor sunitinib is an effective first-line treatment for patients with advanced renal cell carcinoma (RCC). Hypothesizing that a functional read-out by mass spectrometry-based (phospho, p-)proteomics will identify predictive biomarkers for treatment outcome of sunitinib, tumor tissues of 26 RCC patients were analyzed. Eight patients had primary resistant (RES) and 18 sensitive (SENS) RCC. A 78 phosphosite signature (p < 0.05, fold-change > 2) was identified; 22 p-sites were upregulated in RES (unique in RES: BCAR3, NOP58, EIF4A2, GDI1) and 56 in SENS (35 unique). EIF4A1/EIF4A2 were differentially expressed in RES at the (p-)proteome and, in an independent cohort, transcriptome level. Inferred kinase activity of MAPK3 (p = 0.026) and EGFR (p = 0.045) as determined by INKA was higher in SENS. Posttranslational modifications signature enrichment analysis showed that different p-site-centric signatures were enriched (p < 0.05), of which FGF1 and prolactin pathways in RES and, in SENS, vanadate and thrombin treatment pathways, were most significant. In conclusion, the RCC (phospho)proteome revealed differential p-sites and kinase activities associated with sunitinib resistance and sensitivity. Independent validation is warranted to develop an assay for upfront identification of patients who are intrinsically resistant to sunitinib.

Phosphoproteomics of patient-derived xenografts identifies targets and markers associated with sensitivity and resistance to EGFR blockade in colorectal cancer.

Epidermal growth factor receptor (EGFR) is a well-exploited therapeutic target in metastatic colorectal cancer (mCRC). Unfortunately, not all patients benefit from current EGFR inhibitors. Mass spectrometry-based proteomics and phosphoproteomics were performed on 30 genomically and pharmacologically characterized mCRC patient-derived xenografts (PDXs) to investigate the molecular basis of response to EGFR blockade and identify alternative drug targets to overcome resistance. Both the tyrosine and global phosphoproteome as well as the proteome harbored distinctive response signatures. We found that increased pathway activity related to mitogen-activated protein kinase (MAPK) inhibition and abundant tyrosine phosphorylation of cell junction proteins, such as CXADR and CLDN1/3, in sensitive tumors, whereas epithelial-mesenchymal transition and increased MAPK and AKT signaling were more prevalent in resistant tumors. Furthermore, the ranking of kinase activities in single samples confirmed the driver activity of ERBB2, EGFR, and MET in cetuximab-resistant tumors. This analysis also revealed high kinase activity of several members of the Src and ephrin kinase family in 2 CRC PDX models with genomically unexplained resistance. Inhibition of these hyperactive kinases, alone or in combination with cetuximab, resulted in growth inhibition of ex vivo PDX-derived organoids and in vivo PDXs. Together, these findings highlight the potential value of phosphoproteomics to improve our understanding of anti-EGFR treatment and response prediction in mCRC and bring to the forefront alternative drug targets in cetuximab-resistant tumors.

EGFR/IGF1R Signaling Modulates Relaxation in Hypertrophic Cardiomyopathy.

BACKGROUND: Diastolic dysfunction is central to diseases such as heart failure with preserved ejection fraction and hypertrophic cardiomyopathy (HCM). However, therapies that improve cardiac relaxation are scarce, partly due to a limited understanding of modulators of cardiomyocyte relaxation. We hypothesized that cardiac relaxation is regulated by multiple unidentified proteins and that dysregulation of kinases contributes to impaired relaxation in patients with HCM. METHODS: We optimized and increased the throughput of unloaded shortening measurements and screened a kinase inhibitor library in isolated adult cardiomyocytes from wild-type mice. One hundred fifty-seven kinase inhibitors were screened. To assess which kinases are dysregulated in patients with HCM and could contribute to impaired relaxation, we performed a tyrosine and global phosphoproteomics screen and integrative inferred kinase activity analysis using HCM patient myocardium. Identified hits from these 2 data sets were validated in cardiomyocytes from a homozygous MYBPC3c.2373insG HCM mouse model. RESULTS: Screening of 157 kinase inhibitors in wild-type (N=33) cardiomyocytes (n=24 563) resulted in the identification of 17 positive inotropes and 21 positive lusitropes, almost all of them novel. The positive lusitropes formed 3 clusters: cell cycle, EGFR (epidermal growth factor receptor)/IGF1R (insulin-like growth factor 1 receptor), and a small Akt (α-serine/threonine protein kinase) signaling cluster. By performing phosphoproteomic profiling of HCM patient myocardium (N=24 HCM and N=8 donors), we demonstrated increased activation of 6 of 8 proteins from the EGFR/IGFR1 cluster in HCM. We validated compounds from this cluster in mouse HCM (N=12) cardiomyocytes (n=2023). Three compounds from this cluster were able to improve relaxation in HCM cardiomyocytes. CONCLUSIONS: We showed the feasibility of screening for functional modulators of cardiomyocyte relaxation and contraction, parameters that we observed to be modulated by kinases involved in EGFR/IGF1R, Akt, cell cycle signaling, and FoxO (forkhead box class O) signaling, respectively. Integrating the screening data with phosphoproteomics analysis in HCM patient tissue indicated that inhibition of EGFR/IGF1R signaling is a promising target for treating impaired relaxation in HCM.

Phosphoproteomics guides effective low-dose drug combinations against pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is a devastating disease with a limited set of known driver mutations but considerable cancer cell heterogeneity. Phosphoproteomics provides a readout of aberrant signaling and has the potential to identify new targets and guide treatment decisions. Using two-step sequential phosphopeptide enrichment, we generate a comprehensive phosphoproteome and proteome of nine PDAC cell lines, encompassing more than 20,000 phosphosites on 5,763 phospho-proteins, including 316 protein kinases. By using integrative inferred kinase activity (INKA) scoring, we identify multiple (parallel) activated kinases that are subsequently matched to kinase inhibitors. Compared with high-dose single-drug treatments, INKA-tailored low-dose 3-drug combinations against multiple targets demonstrate superior efficacy against PDAC cell lines, organoid cultures, and patient-derived xenografts. Overall, this approach is particularly more effective against the aggressive mesenchymal PDAC model compared with the epithelial model in both preclinical settings and may contribute to improved treatment outcomes in PDAC patients.

A transformer architecture for retention time prediction in liquid chromatography mass spectrometry-based proteomics.

Accurate retention time (RT) prediction is important for spectral library-based analysis in data-independent acquisition mass spectrometry-based proteomics. The deep learning approach has demonstrated superior performance over traditional machine learning methods for this purpose. The transformer architecture is a recent development in deep learning that delivers state-of-the-art performance in many fields such as natural language processing, computer vision, and biology. We assess the performance of the transformer architecture for RT prediction using datasets from five deep learning models Prosit, DeepDIA, AutoRT, DeepPhospho, and AlphaPeptDeep. The experimental results on holdout datasets and independent datasets exhibit state-of-the-art performance of the transformer architecture. The software and evaluation datasets are publicly available for future development in the field.

Inferring kinase activity from phosphoproteomic data: Tool comparison and recent applications.

Aberrant cellular signaling pathways are a hallmark of cancer and other diseases. One of the most important signaling mechanisms involves protein phosphorylation/dephosphorylation. Protein phosphorylation is catalyzed by protein kinases, and over 530 protein kinases have been identified in the human genome. Aberrant kinase activity is one of the drivers of tumorigenesis and cancer progression and results in altered phosphorylation abundance of downstream substrates. Upstream kinase activity can be inferred from the global collection of phosphorylated substrates. Mass spectrometry-based phosphoproteomic experiments nowadays routinely allow identification and quantitation of >10k phosphosites per biological sample. This substrate phosphorylation footprint can be used to infer upstream kinase activities using tools like Kinase Substrate Enrichment Analysis (KSEA), Posttranslational Modification Substrate Enrichment Analysis (PTM-SEA), and Integrative Inferred Kinase Activity Analysis (INKA). Since the topic of kinase activity inference is very active with many new approaches reported in the past 3 years, we would like to give an overview of the field. In this review, an inventory of kinase activity inference tools, their underlying algorithms, statistical frameworks, kinase-substrate databases, and user-friendliness is presented. The most widely-used tools are compared in-depth. Subsequently, recent applications of the tools are described focusing on clinical tissues and hematological samples. Two main application areas for kinase activity inference tools can be discerned. (1) Maximal biological insights can be obtained from large data sets with group comparisons using multiple complementary tools (e.g., PTM-SEA and KSEA or INKA). (2) In the oncology context where personalized treatment requires analysis of single samples, INKA for example, has emerged as tool that can prioritize actionable kinases for targeted inhibition.

Phosphoproteomic Analysis of FLCN Inactivation Highlights Differential Kinase Pathways and Regulatory TFEB Phosphoserines.

In Birt-Hogg-Dubé (BHD) syndrome, germline loss-of-function mutations in the Folliculin (FLCN) gene lead to an increased risk of renal cancer. To address how FLCN inactivation affects cellular kinase signaling pathways, we analyzed comprehensive phosphoproteomic profiles of FLCNPOS and FLCNNEG human renal tubular epithelial cells (RPTEC/TERT1). In total, 15,744 phosphorylated peptides were identified from 4329 phosphorylated proteins. INKA analysis revealed that FLCN loss alters the activity of numerous kinases, including tyrosine kinases EGFR, MET, and the Ephrin receptor subfamily (EPHA2 and EPHB1), as well their downstream targets MAPK1/3. Validation experiments in the BHD renal tumor cell line UOK257 confirmed that FLCN loss contributes to enhanced MAPK1/3 and downstream RPS6K1/3 signaling. The clinically available MAPK inhibitor Ulixertinib showed enhanced toxicity in FLCNNEG cells. Interestingly, FLCN inactivation induced the phosphorylation of PIK3CD (Tyr524) without altering the phosphorylation of canonical Akt1/Akt2/mTOR/EIF4EBP1 phosphosites. Also, we identified that FLCN inactivation resulted in dephosphorylation of TFEB Ser109, Ser114, and Ser122, which may be linked to increased oxidative stress levels in FLCNNEG cells. Together, our study highlights differential phosphorylation of specific kinases and substrates in FLCNNEG renal cells. This provides insight into BHD-associated renal tumorigenesis and may point to several novel candidates for targeted therapies.

Phosphoproteomic profiling of T cell acute lymphoblastic leukemia reveals targetable kinases and combination treatment strategies.

Protein kinase inhibitors are amongst the most successful cancer treatments, but targetable kinases activated by genomic abnormalities are rare in T cell acute lymphoblastic leukemia. Nevertheless, kinases can be activated in the absence of genetic defects. Thus, phosphoproteomics can provide information on pathway activation and signaling networks that offer opportunities for targeted therapy. Here, we describe a mass spectrometry-based global phosphoproteomic profiling of 11 T cell acute lymphoblastic leukemia cell lines to identify targetable kinases. We report a comprehensive dataset consisting of 21,000 phosphosites on 4,896 phosphoproteins, including 217 kinases. We identify active Src-family kinases signaling as well as active cyclin-dependent kinases. We validate putative targets for therapy ex vivo and identify potential combination treatments, such as the inhibition of the INSR/IGF-1R axis to increase the sensitivity to dasatinib treatment. Ex vivo validation of selected drug combinations using patient-derived xenografts provides a proof-of-concept for phosphoproteomics-guided design of personalized treatments.

Tumor Drug Concentration and Phosphoproteomic Profiles After Two Weeks of Treatment With Sunitinib in Patients with Newly Diagnosed Glioblastoma.

PURPOSE: Tyrosine kinase inhibitors (TKI) have poor efficacy in patients with glioblastoma (GBM). Here, we studied whether this is predominantly due to restricted blood-brain barrier penetration or more to biological characteristics of GBM. PATIENTS AND METHODS: Tumor drug concentrations of the TKI sunitinib after 2 weeks of preoperative treatment was determined in 5 patients with GBM and compared with its in vitro inhibitory concentration (IC50) in GBM cell lines. In addition, phosphotyrosine (pTyr)-directed mass spectrometry (MS)-based proteomics was performed to evaluate sunitinib-treated versus control GBM tumors. RESULTS: The median tumor sunitinib concentration of 1.9 µmol/L (range 1.0-3.4) was 10-fold higher than in concurrent plasma, but three times lower than sunitinib IC50s in GBM cell lines (median 5.4 µmol/L, 3.0-8.5; P = 0.01). pTyr-phosphoproteomic profiles of tumor samples from 4 sunitinib-treated versus 7 control patients revealed 108 significantly up- and 23 downregulated (P < 0.05) phosphopeptides for sunitinib treatment, resulting in an EGFR-centered signaling network. Outlier analysis of kinase activities as a potential strategy to identify drug targets in individual tumors identified nine kinases, including MAPK10 and INSR/IGF1R. CONCLUSIONS: Achieved tumor sunitinib concentrations in patients with GBM are higher than in plasma, but lower than reported for other tumor types and insufficient to significantly inhibit tumor cell growth in vitro. Therefore, alternative TKI dosing to increase intratumoral sunitinib concentrations might improve clinical benefit for patients with GBM. In parallel, a complex profile of kinase activity in GBM was found, supporting the potential of (phospho)proteomic analysis for the identification of targets for (combination) treatment.

Quantitative Phosphoproteomic Analysis Reveals Dendritic Cell- Specific STAT Signaling After α2-3-Linked Sialic Acid Ligand Binding.

Dendritic cells (DCs) are key initiators of the adaptive immunity, and upon recognition of pathogens are able to skew T cell differentiation to elicit appropriate responses. DCs possess this extraordinary capacity to discern external signals using receptors that recognize pathogen-associated molecular patterns. These can be glycan-binding receptors that recognize carbohydrate structures on pathogens or pathogen-associated patterns that additionally bind receptors, such as Toll-like receptors (TLRs). This study explores the early signaling events in DCs upon binding of α2-3 sialic acid (α2-3sia) that are recognized by Immune inhibitory Sialic acid binding immunoglobulin type lectins. α2-3sias are commonly found on bacteria, e.g. Group B Streptococcus, but can also be expressed by tumor cells. We investigated whether α2-3sia conjugated to a dendrimeric core alters DC signaling properties. Through phosphoproteomic analysis, we found differential signaling profiles in DCs after α2-3sia binding alone or in combination with LPS/TLR4 co-stimulation. α2-3sia was able to modulate the TLR4 signaling cascade, resulting in 109 altered phosphoproteins. These phosphoproteins were annotated to seven biological processes, including the regulation of the IL-12 cytokine pathway. Secretion of IL-10, the inhibitory regulator of IL-12 production, by DCs was found upregulated after overnight stimulation with the α2-3sia dendrimer. Analysis of kinase activity revealed altered signatures in the JAK-STAT signaling pathway. PhosphoSTAT3 (Ser727) and phosphoSTAT5A (Ser780), involved in the regulation of the IL-12 pathway, were both downregulated. Flow cytometric quantification indeed revealed de- phosphorylation over time upon stimulation with α2-3sia, but no α2-6sia. Inhibition of both STAT3 and -5A in moDCs resulted in a similar cytokine secretion profile as α-3sia triggered DCs. Conclusively, this study revealed a specific alteration of the JAK-STAT pathway in DCs upon simultaneous α2-3sia and LPS stimulation, altering the IL10:IL-12 cytokine secretion profile associated with reduction of inflammation. Targeted control of the STAT phosphorylation status is therefore an interesting lead for the abrogation of immune escape that bacteria or tumors impose on the host.

Phosphotyrosine-based Phosphoproteomics for Target Identification and Drug Response Prediction in AML Cell Lines.

Acute myeloid leukemia (AML) is a clonal disorder arising from hematopoietic myeloid progenitors. Aberrantly activated tyrosine kinases (TK) are involved in leukemogenesis and are associated with poor treatment outcome. Kinase inhibitor (KI) treatment has shown promise in improving patient outcome in AML. However, inhibitor selection for patients is suboptimal.In a preclinical effort to address KI selection, we analyzed a panel of 16 AML cell lines using phosphotyrosine (pY) enrichment-based, label-free phosphoproteomics. The Integrative Inferred Kinase Activity (INKA) algorithm was used to identify hyperphosphorylated, active kinases as candidates for KI treatment, and efficacy of selected KIs was tested.Heterogeneous signaling was observed with between 241 and 2764 phosphopeptides detected per cell line. Of 4853 identified phosphopeptides with 4229 phosphosites, 4459 phosphopeptides (4430 pY) were linked to 3605 class I sites (3525 pY). INKA analysis in single cell lines successfully pinpointed driver kinases (PDGFRA, JAK2, KIT and FLT3) corresponding with activating mutations present in these cell lines. Furthermore, potential receptor tyrosine kinase (RTK) drivers, undetected by standard molecular analyses, were identified in four cell lines (FGFR1 in KG-1 and KG-1a, PDGFRA in Kasumi-3, and FLT3 in MM6). These cell lines proved highly sensitive to specific KIs. Six AML cell lines without a clear RTK driver showed evidence of MAPK1/3 activation, indicative of the presence of activating upstream RAS mutations. Importantly, FLT3 phosphorylation was demonstrated in two clinical AML samples with a FLT3 internal tandem duplication (ITD) mutation.Our data show the potential of pY-phosphoproteomics and INKA analysis to provide insight in AML TK signaling and identify hyperactive kinases as potential targets for treatment in AML cell lines. These results warrant future investigation of clinical samples to further our understanding of TK phosphorylation in relation to clinical response in the individual patient.

iq: an R package to estimate relative protein abundances from ion quantification in DIA-MS-based proteomics.

SUMMARY: We present an R package called iq to enable accurate protein quantification for label-free data-independent acquisition (DIA) mass spectrometry-based proteomics, a recently developed global approach with superior quantitative consistency. We implement the popular maximal peptide ratio extraction module of the MaxLFQ algorithm, so far only applicable to data-dependent acquisition mode using the software suite MaxQuant. Moreover, our implementation shows, for each protein separately, the validity of quantification over all samples. Hence, iq exports a state-of-the-art protein quantification algorithm to the emerging DIA mode in an open-source implementation. AVAILABILITY AND IMPLEMENTATION: The open-source R package is available on CRAN, https://github.com/tvpham/iq/releases and oncoproteomics.nl/iq. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

INKA, an integrative data analysis pipeline for phosphoproteomic inference of active kinases.

Identifying hyperactive kinases in cancer is crucial for individualized treatment with specific inhibitors. Kinase activity can be discerned from global protein phosphorylation profiles obtained with mass spectrometry-based phosphoproteomics. A major challenge is to relate such profiles to specific hyperactive kinases fueling growth/progression of individual tumors. Hitherto, the focus has been on phosphorylation of either kinases or their substrates. Here, we combined label-free kinase-centric and substrate-centric information in an Integrative Inferred Kinase Activity (INKA) analysis. This multipronged, stringent analysis enables ranking of kinase activity and visualization of kinase-substrate networks in a single biological sample. To demonstrate utility, we analyzed (i) cancer cell lines with known oncogenes, (ii) cell lines in a differential setting (wild-type versus mutant, +/- drug), (iii) pre- and on-treatment tumor needle biopsies, (iv) cancer cell panel with available drug sensitivity data, and (v) patient-derived tumor xenografts with INKA-guided drug selection and testing. These analyses show superior performance of INKA over its components and substrate-based single-sample tool KARP, and underscore target potential of high-ranking kinases, encouraging further exploration of INKA's functional and clinical value.

Proteomic identification of Axc, a novel beta-lactamase with carbapenemase activity in a meropenem-resistant clinical isolate of Achromobacter xylosoxidans.

The development of antibiotic resistance during treatment is a threat to patients and their environment. Insight in the mechanisms of resistance development is important for appropriate therapy and infection control. Here, we describe how through the application of mass spectrometry-based proteomics, a novel beta-lactamase Axc was identified as an indicator of acquired carbapenem resistance in a clinical isolate of Achromobacter xylosoxidans. Comparative proteomic analysis of consecutively collected susceptible and resistant isolates from the same patient revealed that high Axc protein levels were only observed in the resistant isolate. Heterologous expression of Axc in Escherichia coli significantly increased the resistance towards carbapenems. Importantly, direct Axc mediated hydrolysis of imipenem was demonstrated using pH shift assays and 1H-NMR, confirming Axc as a legitimate carbapenemase. Whole genome sequencing revealed that the susceptible and resistant isolates were remarkably similar. Together these findings provide a molecular context for the fast development of meropenem resistance in A. xylosoxidans during treatment and demonstrate the use of mass spectrometric techniques in identifying novel resistance determinants.

Identifying proteins in zebrafish embryos using spectral libraries generated from dissected adult organs and tissues.

Spectral libraries provide a sensitive and accurate method for identifying peptides from tandem mass spectra, complementary to searching genome-derived databases or sequencing de novo. Their application requires comprehensive libraries including peptides from low-abundant proteins. Here we describe a method for constructing such libraries using biological differentiation to "fractionate" the proteome by harvesting adult organs and tissues and build comprehensive libraries for identifying proteins in zebrafish (Danio rerio) embryos and larvae (an important and widely used model system). Hierarchical clustering using direct comparison of spectra was used to prioritize organ selection. The resulting and publicly available library covers 14,164 proteins, significantly improved the number of peptide-spectrum matches in zebrafish developmental stages, and can be used on data from different instruments and laboratories. The library contains information on tissue and organ expression of these proteins and is also applicable for adult experiments. The approach itself is not limited to zebrafish but would work for any model system.

Retention time prediction and protein identification.

In bottom-up proteomics, proteins are typically identified by enzymatic digestion into peptides, tandem mass spectrometry and comparison of the tandem mass spectra with those predicted from a sequence database for peptides within measurement uncertainty from the experimentally obtained mass. Although now decreasingly common, isolated proteins or simple protein mixtures can also be identified by measuring only the masses of the peptides resulting from the enzymatic digest, without any further fragmentation. Separation methods such as liquid chromatography and electrophoresis are often used to fractionate complex protein or peptide mixtures prior to analysis by mass spectrometry. Although the primary reason for this is to avoid ion suppression and improve data quality, these separations are based on physical and chemical properties of the peptides or proteins and therefore also provide information about them. Depending on the separation method, this could be protein molecular weight (SDS-PAGE), isoelectric point (IEF), charge at a known pH (ion exchange chromatography), or hydrophobicity (reversed phase chromatography). These separations produce approximate measurements on properties that to some extent can be predicted from amino acid sequences. In the case of molecular weight of proteins without posttranslational modifications this is straightforward: simply add the molecular weights of the amino acid residues in the protein. For IEF, charge and hydrophobicity, the order of the amino acids, and folding state of the peptide or protein also matter, but it is nevertheless possible to predict the behavior of peptides and proteins in these separation methods to a degree which renders such predictions useful. This chapter reviews the topic of using data from separation methods for identification and validation in proteomics, with special emphasis on predicting retention times of tryptic peptides in reversed-phase chromatography under acidic conditions, as this is one of the most commonly used separation methods in proteomics.

Cloud parallel processing of tandem mass spectrometry based proteomics data.

Data analysis in mass spectrometry based proteomics struggles to keep pace with the advances in instrumentation and the increasing rate of data acquisition. Analyzing this data involves multiple steps requiring diverse software, using different algorithms and data formats. Speed and performance of the mass spectral search engines are continuously improving, although not necessarily as needed to face the challenges of acquired big data. Improving and parallelizing the search algorithms is one possibility; data decomposition presents another, simpler strategy for introducing parallelism. We describe a general method for parallelizing identification of tandem mass spectra using data decomposition that keeps the search engine intact and wraps the parallelization around it. We introduce two algorithms for decomposing mzXML files and recomposing resulting pepXML files. This makes the approach applicable to different search engines, including those relying on sequence databases and those searching spectral libraries. We use cloud computing to deliver the computational power and scientific workflow engines to interface and automate the different processing steps. We show how to leverage these technologies to achieve faster data analysis in proteomics and present three scientific workflows for parallel database as well as spectral library search using our data decomposition programs, X!Tandem and SpectraST.