MultiAlign: a multiple LC-MS analysis tool for targeted omics analysis.

BACKGROUND: MultiAlign is a free software tool that aligns multiple liquid chromatography-mass spectrometry datasets to one another by clustering mass and chromatographic elution features across datasets. Applicable to both label-free proteomics and metabolomics comparative analyses, the software can be operated in several modes. For example, clustered features can be matched to a reference database to identify analytes, used to generate abundance profiles, linked to tandem mass spectra based on parent precursor masses, and culled for targeted liquid chromatography-tandem mass spectrometric analysis. MultiAlign is also capable of tandem mass spectral clustering to describe proteome structure and find similarity in subsequent sample runs. RESULTS: MultiAlign was applied to two large proteomics datasets obtained from liquid chromatography-mass spectrometry analyses of environmental samples. Peptides in the datasets for a microbial community that had a known metagenome were identified by matching mass and elution time features to those in an established reference peptide database. Results compared favorably with those obtained using existing tools such as VIPER, but with the added benefit of being able to trace clusters of peptides across conditions to existing tandem mass spectra. MultiAlign was further applied to detect clusters across experimental samples derived from a reactor biomass community for which no metagenome was available. Several clusters were culled for further analysis to explore changes in the community structure. Lastly, MultiAlign was applied to liquid chromatography-mass spectrometry-based datasets obtained from a previously published study of wild type and mitochondrial fatty acid oxidation enzyme knockdown mutants of human hepatocarcinoma to demonstrate its utility for analyzing metabolomics datasets. CONCLUSION: MultiAlign is an efficient software package for finding similar analytes across multiple liquid chromatography-mass spectrometry feature maps, as demonstrated here for both proteomics and metabolomics experiments. The software is particularly useful for proteomic studies where little or no genomic context is known, such as with environmental proteomics.

Normalizing molecular docking rankings using virtually generated decoys.

Drug discovery research often relies on the use of virtual screening via molecular docking to identify active hits in compound libraries. An area for improvement among many state-of-the-art docking methods is the accuracy of the scoring functions used to differentiate active from nonactive ligands. Many contemporary scoring functions are influenced by the physical properties of the docked molecule. This bias can cause molecules with certain physical properties to incorrectly score better than others. Since variation in physical properties is inevitable in large screening libraries, it is desirable to account for this bias. In this paper, we present a method of normalizing docking scores using virtually generated decoy sets with matched physical properties. First, our method generates a set of property-matched decoys for every molecule in the screening library. Each library molecule and its decoy set are docked using a state-of-the-art method, producing a set of raw docking scores. Next, the raw docking score of each library molecule is normalized against the scores of its decoys. The normalized score represents the probability that the raw docking score was drawn from the background distribution of nonactive property-matched decoys. Assuming that the distribution of scores of active molecules differs from the nonactive score distribution, we expect that the score of an active compound will have a low probability of having been drawn from the nonactive score distribution. In addition to the use of decoys in normalizing docking scores, we suggest that decoy sets may be a useful tool to evaluate, improve, or develop scoring functions. We show that by analyzing docking scores of library molecules with respect to the docking scores of their virtually generated property-matched decoys, one can gain insight into the advantages, limitations, and reliability of scoring functions.

A structure-based approach for mapping adverse drug reactions to the perturbation of underlying biological pathways.

Adverse drug reactions (ADR), also known as side-effects, are complex undesired physiologic phenomena observed secondary to the administration of pharmaceuticals. Several phenomena underlie the emergence of each ADR; however, a dominant factor is the drug's ability to modulate one or more biological pathways. Understanding the biological processes behind the occurrence of ADRs would lead to the development of safer and more effective drugs. At present, no method exists to discover these ADR-pathway associations. In this paper we introduce a computational framework for identifying a subset of these associations based on the assumption that drugs capable of modulating the same pathway may induce similar ADRs. Our model exploits multiple information resources. First, we utilize a publicly available dataset pairing drugs with their observed ADRs. Second, we identify putative protein targets for each drug using the protein structure database and in-silico virtual docking. Third, we label each protein target with its known involvement in one or more biological pathways. Finally, the relationships among these information sources are mined using multiple stages of logistic-regression while controlling for over-fitting and multiple-hypothesis testing. As proof-of-concept, we examined a dataset of 506 ADRs, 730 drugs, and 830 human protein targets. Our method yielded 185 ADR-pathway associations of which 45 were selected to undergo a manual literature review. We found 32 associations to be supported by the scientific literature.

A Bayesian method for 3D macromolecular structure inference using class average images from single particle electron microscopy.

MOTIVATION: Electron cryo-microscopy can be used to infer 3D structures of large macromolecules with high resolution, but the large amounts of data captured necessitate the development of appropriate statistical models to describe the data generation process, and to perform structure inference. We present a new method for performing ab initio inference of the 3D structures of macromolecules from single particle electron cryo-microscopy experiments using class average images. RESULTS: We demonstrate this algorithm on one phantom, one synthetic dataset and three real (experimental) datasets (ATP synthase, V-type ATPase and GroEL). Structures consistent with the known structures were inferred for all datasets. AVAILABILITY: The software and source code for this method is available for download from our website: http://compbio.cs.toronto.edu/cryoem/.

Novel Ser/Thr protein phosphatase 5 (PP5) regulated targets during DNA damage identified by proteomics analysis.

The DNA damage response likely includes a global phosphorylation signaling cascade process for sensing the damaged DNA condition and coordinating responses to cope with and repair the perturbed cellular state. We utilized a label-free liquid chromatography-mass spectrometry approach to evaluate changes in protein phosphorylation associated with PP5 activity during the DNA damage response. Biological replicate analyses of bleomycin-treated HeLa cells expressing either WT-PP5 or mutant inactive PP5 lead to the identification of six potential target proteins of PP5 action. Four of these putative targets have been previously reported to be involved in DNA damage responses. Using phospho-site specific antibodies, we confirmed that phosphorylation of one target, ribosomal protein S6, was selectively decreased in cells overexpressing catalytically inactive PP5. Our findings also suggest that PP5 may play a role in controlling translation and in regulating substrates for proline-directed kinases, such as MAP kinases and cyclin-dependent protein kinases that are involved in response to DNA damage.

Decon2LS: An open-source software package for automated processing and visualization of high resolution mass spectrometry data.

BACKGROUND: Data generated from liquid chromatography coupled to high-resolution mass spectrometry (LC-MS)-based studies of a biological sample can contain large amounts of biologically significant information in the form of proteins, peptides, and metabolites. Interpreting this data involves inferring the masses and abundances of biomolecules injected into the instrument. Because of the inherent complexity of mass spectral patterns produced by these biomolecules, the analysis is significantly enhanced by using visualization capabilities to inspect and confirm results. In this paper we describe Decon2LS, an open-source software package for automated processing and visualization of high-resolution MS data. Drawing extensively on algorithms developed over the last ten years for ICR2LS, Decon2LS packages the algorithms as a rich set of modular, reusable processing classes for performing diverse functions such as reading raw data, routine peak finding, theoretical isotope distribution modelling, and deisotoping. Because the source code is openly available, these functionalities can now be used to build derivative applications in relatively fast manner. In addition, Decon2LS provides an extensive set of visualization tools, such as high performance chart controls. RESULTS: With a variety of options that include peak processing, deisotoping, isotope composition, etc, Decon2LS supports processing of multiple raw data formats. Deisotoping can be performed on an individual scan, an individual dataset, or on multiple datasets using batch processing. Other processing options include creating a two dimensional view of mass and liquid chromatography (LC) elution time features, generating spectrum files for tandem MS data, creating total intensity chromatograms, and visualizing theoretical peptide profiles. Application of Decon2LS to deisotope different datasets obtained across different instruments yielded a high number of features that can be used to identify and quantify peptides in the biological sample. CONCLUSION: Decon2LS is an efficient software package for discovering and visualizing features in proteomics studies that require automated interpretation of mass spectra. Besides being easy to use, fast, and reliable, Decon2LS is also open-source, which allows developers in the proteomics and bioinformatics communities to reuse and refine the algorithms to meet individual needs.Decon2LS source code, installer, and tutorials may be downloaded free of charge at http://http:/ncrr.pnl.gov/software/.

Global systems-level analysis of Hfq and SmpB deletion mutants in Salmonella: implications for virulence and global protein translation.

Using sample-matched transcriptomics and proteomics measurements it is now possible to begin to understand the impact of post-transcriptional regulatory programs in Enterobacteria. In bacteria post-transcriptional regulation is mediated by relatively few identified RNA-binding protein factors including CsrA, Hfq and SmpB. A mutation in any one of these three genes, csrA, hfq, and smpB, in Salmonella is attenuated for mouse virulence and unable to survive in macrophages. CsrA has a clearly defined specificity based on binding to a specific mRNA sequence to inhibit translation. However, the proteins regulated by Hfq and SmpB are not as clearly defined. Previous work identified proteins regulated by hfq using purification of the RNA-protein complex with direct sequencing of the bound RNAs and found binding to a surprisingly large number of transcripts. In this report we have used global proteomics to directly identify proteins regulated by Hfq or SmpB by comparing protein abundance in the parent and isogenic hfq or smpB mutant. From these same samples we also prepared RNA for microarray analysis to determine if alteration of protein expression was mediated post-transcriptionally. Samples were analyzed from bacteria grown under four different conditions; two laboratory conditions and two that are thought to mimic the intracellular environment. We show that mutants of hfq and smpB directly or indirectly modulate at least 20% and 4% of all possible Salmonella proteins, respectively, with limited correlation between transcription and protein expression. These proteins represent a broad spectrum of Salmonella proteins required for many biological processes including host cell invasion, motility, central metabolism, LPS biosynthesis, two-component regulatory systems, and fatty acid metabolism. Our results represent one of the first global analyses of post-transcriptional regulons in any organism and suggest that regulation at the translational level is widespread and plays an important role in virulence regulation and environmental adaptation for Salmonella.

Identification of a putative protein profile associated with tamoxifen therapy resistance in breast cancer.

Tamoxifen resistance is a major cause of death in patients with recurrent breast cancer. Current clinical factors can correctly predict therapy response in only half of the treated patients. Identification of proteins that are associated with tamoxifen resistance is a first step toward better response prediction and tailored treatment of patients. In the present study we intended to identify putative protein biomarkers indicative of tamoxifen therapy resistance in breast cancer using nano-LC coupled with FTICR MS. Comparative proteome analysis was performed on approximately 5,500 pooled tumor cells (corresponding to approximately 550 ng of protein lysate/analysis) obtained through laser capture microdissection (LCM) from two independently processed data sets (n = 24 and n = 27) containing both tamoxifen therapy-sensitive and therapy-resistant tumors. Peptides and proteins were identified by matching mass and elution time of newly acquired LC-MS features to information in previously generated accurate mass and time tag reference databases. A total of 17,263 unique peptides were identified that corresponded to 2,556 non-redundant proteins identified with > or = 2 peptides. 1,713 overlapping proteins between the two data sets were used for further analysis. Comparative proteome analysis revealed 100 putatively differentially abundant proteins between tamoxifen-sensitive and tamoxifen-resistant tumors. The presence and relative abundance for 47 differentially abundant proteins were verified by targeted nano-LC-MS/MS in a selection of unpooled, non-microdissected discovery set tumor tissue extracts. ENPP1, EIF3E, and GNB4 were significantly associated with progression-free survival upon tamoxifen treatment for recurrent disease. Differential abundance of our top discriminating protein, extracellular matrix metalloproteinase inducer, was validated by tissue microarray in an independent patient cohort (n = 156). Extracellular matrix metalloproteinase inducer levels were higher in therapy-resistant tumors and significantly associated with an earlier tumor progression following first line tamoxifen treatment (hazard ratio, 1.87; 95% confidence interval, 1.25-2.80; p = 0.002). In summary, comparative proteomics performed on laser capture microdissection-derived breast tumor cells using nano-LC-FTICR MS technology revealed a set of putative biomarkers associated with tamoxifen therapy resistance in recurrent breast cancer.

Large-scale multiplexed quantitative discovery proteomics enabled by the use of an (18)O-labeled "universal" reference sample.

The quantitative comparison of protein abundances across a large number of biological or patient samples represents an important proteomics challenge that needs to be addressed for proteomics discovery applications. Herein, we describe a strategy that incorporates a stable isotope (18)O-labeled "universal" reference sample as a comprehensive set of internal standards for analyzing large sample sets quantitatively. As a pooled sample, the (18)O-labeled "universal" reference sample is spiked into each individually processed unlabeled biological sample and the peptide/protein abundances are quantified based on (16)O/(18)O isotopic peptide pair abundance ratios that compare each unlabeled sample to the identical reference sample. This approach also allows for the direct application of label-free quantitation across the sample set simultaneously along with the labeling-approach (i.e., dual-quantitation) since each biological sample is unlabeled except for the labeled reference sample that is used as internal standards. The effectiveness of this approach for large-scale quantitative proteomics is demonstrated by its application to a set of 18 plasma samples from severe burn patients. When immunoaffinity depletion and cysteinyl-peptide enrichment-based fractionation with high resolution LC-MS measurements were combined, a total of 312 plasma proteins were confidently identified and quantified with a minimum of two unique peptides per protein. The isotope labeling data was directly compared with the label-free (16)O-MS intensity data extracted from the same data sets. The results showed that the (18)O reference-based labeling approach had significantly better quantitative precision compared to the label-free approach. The relative abundance differences determined by the two approaches also displayed strong correlation, illustrating the complementary nature of the two quantitative methods. The simplicity of including the (18)O-reference for accurate quantitation makes this strategy especially attractive when a large number of biological samples are involved in a study where label-free quantitation may be problematic, for example, due to issues associated with instrument platform robustness. The approach will also be useful for more effectively discovering subtle abundance changes in broad systems biology studies.

Characterization of strategies for obtaining confident identifications in bottom-up proteomics measurements using hybrid FTMS instruments.

Hybrid FTMS instruments, such as the LTQ-FT and LTQ-Orbitrap, are capable of generating high duty cycle linear ion trap MS/MS data along with high resolution information without compromising the overall throughput of measurements. Combined with online LC separations, these instruments provide powerful capabilities for proteomics research. In the present work, we explore three alternative strategies for high throughput proteomics measurements using hybrid FTMS instruments. Our accurate mass and time tag (AMT tag) strategy enables identification of thousands of peptides in a single LC-FTMS analysis by comparing accurate molecular mass and LC elution time information from the analysis to a reference database. An alternative strategy considered here, termed accurate precursor mass filter (APMF), employs linear ion trap (low resolution) MS/MS identifications generated by an appropriate search engine, such as SEQUEST, refined with high resolution precursor ion data obtained from FTMS mass spectra. The APMF results can be additionally filtered using the LC elution time information from the AMT tag database, which constitutes a precursor mass and time filter (PMTF), the third approach implemented in this study. Both the APMF and the PMTF approaches are evaluated for coverage and confidence of peptide identifications and contrasted with the AMT tag strategy. The commonly used decoy database method and an alternative method based on mass accuracy histograms were used to reliably quantify identification confidence, revealing that both methods yielded similar results. Comparison of the AMT, APMF and PMTF approaches indicates that the AMT tag approach is preferential for studies desiring a highest achievable number of identified peptides. In contrast, the APMF approach does not require an AMT tag database and provides a moderate level of peptide coverage combined with acceptable confidence values of approximately 99%. The PMTF approach yielded a significantly better peptide identification confidence, >99.9%, that essentially excluded any false peptide identifications. Since AMT tag databases that exclude incorrect identifications are desirable, this study points to the value of a multipass APMF approach to generate AMT tag databases, which are then validated using the PMTF approach. The resulting compact, high quality databases can then be used for subsequent high-throughput, high peptide coverage AMT tag studies.

DAnTE: a statistical tool for quantitative analysis of -omics data.

UNLABELLED: Data Analysis Tool Extension (DAnTE) is a statistical tool designed to address challenges associated with quantitative bottom-up, shotgun proteomics data. This tool has also been demonstrated for microarray data and can easily be extended to other high-throughput data types. DAnTE features selected normalization methods, missing value imputation algorithms, peptide-to-protein rollup methods, an extensive array of plotting functions and a comprehensive hypothesis-testing scheme that can handle unbalanced data and random effects. The graphical user interface (GUI) is designed to be very intuitive and user friendly. AVAILABILITY: DAnTE may be downloaded free of charge at http://omics.pnl.gov/software/. SUPPLEMENTARY INFORMATION: An example dataset with instructions on how to perform a series of analysis steps is available at http://omics.pnl.gov/software/

Application of the accurate mass and time tag approach in studies of the human blood lipidome.

We report a preliminary demonstration of the accurate mass and time (AMT) tag approach for lipidomics. Initial data-dependent LC-MS/MS analyses of human plasma, erythrocyte, and lymphocyte lipids were performed in order to identify lipid molecular species in conjunction with complementary accurate mass and isotopic distribution information. Identified lipids were used to populate initial lipid AMT tag databases containing 250 and 45 entries for those species detected in positive and negative electrospray ionization (ESI) modes, respectively. The positive ESI database was then utilized to identify human plasma, erythrocyte, and lymphocyte lipids in high-throughput LC-MS analyses based on the AMT tag approach. We were able to define the lipid profiles of human plasma, erythrocytes, and lymphocytes based on qualitative and quantitative differences in lipid abundance.

The influence of sample preparation and replicate analyses on HeLa Cell phosphoproteome coverage.

Ongoing optimization of proteomic methodologies seeks to improve both the coverage and confidence of protein identifications. The optimization of sample preparation, inclusion of technical replicates (repeated instrumental analysis of the same sample), and biological replicates (multiple individual samples) are crucial in proteomic studies to avoid the pitfalls associated with single point analysis and under-sampling. Phosphopeptides were isolated from HeLa cells and analyzed by nano-reversed phase liquid chromatography electrospray ionization tandem mass spectrometry (nano-RP-LC-MS/MS). We observed that a detergent-based protein extraction approach, followed with additional steps for nucleic acid removal, provided a simple alternative to the broadly used Trizol extraction. The evaluation of four technical replicates demonstrated measurement reproducibility with low percent variance in peptide responses at approximately 3%, where additional peptide identifications were made with each added technical replicate. The inclusion of six technical replicates for moderately complex protein extracts (approximately 4000 uniquely identified peptides per data set) affords the optimal collection of peptide information.

DeconMSn: a software tool for accurate parent ion monoisotopic mass determination for tandem mass spectra.

UNLABELLED: DeconMSn accurately determines the monoisotopic mass and charge state of parent ions from high-resolution tandem mass spectrometry data, offering significant improvement for LTQ_FT and LTQ_Orbitrap instruments over the commercially delivered Thermo Fisher Scientific's extract_msn tool. Optimal parent ion mass tolerance values can be determined using accurate mass information, thus improving peptide identifications for high-mass measurement accuracy experiments. For low-resolution data from LCQ and LTQ instruments, DeconMSn incorporates a support-vector-machine-based charge detection algorithm that identifies the most likely charge of a parent species through peak characteristics of its fragmentation pattern. AVAILABILITY: http://ncrr.pnl.gov/software/ or http://www.proteomicsresource.org/.

Elimination of systematic mass measurement errors in liquid chromatography-mass spectrometry based proteomics using regression models and a priori partial knowledge of the sample content.

The high mass measurement accuracy and precision available with recently developed mass spectrometers is increasingly used in proteomics analyses to confidently identify tryptic peptides from complex mixtures of proteins, as well as post-translational modifications and peptides from nonannotated proteins. To take full advantage of high mass measurement accuracy instruments, it is necessary to limit systematic mass measurement errors. It is well known that errors in m/z measurements can be affected by experimental parameters that include, for example, outdated calibration coefficients, ion intensity, and temperature changes during the measurement. Traditionally, these variations have been corrected through the use of internal calibrants (well-characterized standards introduced with the sample being analyzed). In this paper, we describe an alternative approach where the calibration is provided through the use of a priori knowledge of the sample being analyzed. Such an approach has previously been demonstrated based on the dependence of systematic error on m/z alone. To incorporate additional explanatory variables, we employed multidimensional, nonparametric regression models, which were evaluated using several commercially available instruments. The applied approach is shown to remove any noticeable biases from the overall mass measurement errors and decreases the overall standard deviation of the mass measurement error distribution by 1.2-2-fold, depending on instrument type. Subsequent reduction of the random errors based on multiple measurements over consecutive spectra further improves accuracy and results in an overall decrease of the standard deviation by 1.8-3.7-fold. This new procedure will decrease the false discovery rates for peptide identifications using high-accuracy mass measurements.

Applying a targeted label-free approach using LC-MS AMT tags to evaluate changes in protein phosphorylation following phosphatase inhibition.

To identify phosphoproteins regulated by the phosphoprotein phosphatase (PPP) family of S/T phosphatases, we performed a large-scale characterization of changes in protein phosphorylation on extracts from HeLa cells treated with or without calyculin A, a potent PPP enzyme inhibitor. A label-free comparative phosphoproteomics approach using immobilized metal ion affinity chromatography and targeted tandem mass spectrometry was employed to discover and identify signatures based upon distinctive changes in abundance. Overall, 232 proteins were identified as either direct or indirect targets for PPP enzyme regulation. Most of the present identifications represent novel PPP enzyme targets at the level of both phosphorylation site and protein. These include phosphorylation sites within signaling proteins such as p120 Catenin, A Kinase Anchoring Protein 8, JunB, and Type II Phosphatidyl Inositol 4 Kinase. These data can be used to define underlying signaling pathways and events regulated by the PPP family of S/T phosphatases.

Whole proteome analysis of post-translational modifications: applications of mass-spectrometry for proteogenomic annotation.

While bacterial genome annotations have significantly improved in recent years, techniques for bacterial proteome annotation (including post-translational chemical modifications, signal peptides, proteolytic events, etc.) are still in their infancy. At the same time, the number of sequenced bacterial genomes is rising sharply, far outpacing our ability to validate the predicted genes, let alone annotate bacterial proteomes. In this study, we use tandem mass spectrometry (MS/MS) to annotate the proteome of Shewanella oneidensis MR-1, an important microbe for bioremediation. In particular, we provide the first comprehensive map of post-translational modifications in a bacterial genome, including a large number of chemical modifications, signal peptide cleavages, and cleavages of N-terminal methionine residues. We also detect multiple genes that were missed or assigned incorrect start positions by gene prediction programs, and suggest corrections to improve the gene annotation. This study demonstrates that complementing every genome sequencing project by an MS/MS project would significantly improve both genome and proteome annotations for a reasonable cost.

Capillary LC coupled with high-mass measurement accuracy mass spectrometry for metabolic profiling.

We have developed an efficient and robust high-pressure capillary LC-MS method for the identification of large numbers of metabolites in biological samples using both positive and negative ESI modes. Initial efforts focused on optimizing the separation conditions for metabolite extracts using various LC stationary phases in conjunction with multiple mobile-phase systems, as applied to the separation of 45 metabolite standards. The optimal mobile and stationary phases of those tested were determined experimentally (in terms of peak shapes, theoretical plates, retention of small, polar compounds, etc.), and both linear and exponential gradients were applied in the study of metabolite extracts from the cyanobacterium Cyanothece sp. ATCC 51142. Finally, an automated dual-capillary LC system was constructed and evaluated for the effectiveness and reproducibility of the chromatographic separations using the above samples. When coupled with a commercial LTQ-orbitrap MS, approximately 900 features were reproducibly detected from Cyanothece sp. ATCC 51142 metabolite extracts. In addition, 12 compounds were tentatively identified, based on accurate mass, isotopic distribution, and MS/MS information.

VIPER: an advanced software package to support high-throughput LC-MS peptide identification.

SUMMARY: The accurate mass and time (AMT) tag approach is used for analysis of large scale experiments by combining information generated over multiple datasets and instrument types. The VIPER software package is one of the key components of the data processing pipeline and implements automated algorithms to discover LC-MS features, align and match these LC-MS features to a database of peptides previously identified in LC-MS/MS analyses, and identify and quantify pairs of isotopically labeled peptides. AVAILABILITY: VIPER may be downloaded free of charge at http://ncrr.pnl.gov/software/