Search

Background A few recent large efforts significantly expanded the collection of human-associated bacterial genomes, which now contains thousands of entities including reference complete/draft genomes and metagenome assembled genomes (MAGs). These genomes provide useful resource for studying the functionality of the human-associated microbiome and their relationship with human health and diseases. One application of these genomes is to provide a universal reference for database search in metaproteomic studies, when matched metagenomic/metatranscriptomic data are unavailable. However, a greater collection of reference genomes may not necessarily result in better peptide/protein identification because the increase of search space often leads to fewer spectrum-peptide matches, not to mention the drastic increase of computation time. Methods Here, we present a new approach that uses two steps to optimize the use of the reference genomes and MAGs as the universal reference for human gut metaproteomic MS/MS data analysis. The first step is to use only the high-abundance proteins (HAPs) (i.e., ribosomal proteins and elongation factors) for metaproteomic MS/MS database search and, based on the identification results, to derive the taxonomic composition of the underlying microbial community. The second step is to expand the search database by including all proteins from identified abundant species. We call our approach HAPiID (HAPs guided metaproteomics IDentification). Results We tested our approach using human gut metaproteomic datasets from a previous study and compared it to the state-of-the-art reference database search method MetaPro-IQ for metaproteomic identification in studying human gut microbiota. Our results show that our two-steps method not only performed significantly faster but also was able to identify more peptides. We further demonstrated the application of HAPiID to revealing protein profiles of individual human-associated bacterial species, one or a few species at a time, using metaproteomic data. Conclusions The HAP guided profiling approach presents a novel effective way for constructing target database for metaproteomic data analysis. The HAPiID pipeline built upon this approach provides a universal tool for analyzing human gut-associated metaproteomic data.

The development of reliable, mixed-culture biotechnological processes hinges on understanding how microbial ecosystems respond to disturbances. Here we reveal extensive phenotypic plasticity and niche complementarity in oleaginous microbial populations from a biological wastewater treatment plant. We perform meta-omics analyses (metagenomics, metatranscriptomics, metaproteomics and metabolomics) on in situ samples over 14 months at weekly intervals. Based on 1,364 de novo metagenome-assembled genomes, we uncover four distinct fundamental niche types. Throughout the time-series, we observe a major, transient shift in community structure, coinciding with substrate availability changes. Functional omics data reveals extensive variation in gene expression and substrate usage amongst community members. Ex situ bioreactor experiments confirm that responses occur within five hours of a pulse disturbance, demonstrating rapid adaptation by specific populations. Our results show that community resistance and resilience are a function of phenotypic plasticity and niche complementarity, and set the foundation for future ecological engineering efforts., Herold et al. present an integrated meta-omics framework to investigate how mixed microbial communities, such as oleaginous bacterial populations in biological wastewater treatment plants, respond with distinct adaptation strategies to disturbances. They show that community resistance and resilience are a function of phenotypic plasticity and niche complementarity.

With the accumulation of MS/MS spectra collected in spectral libraries, the spectral library searching approach emerges as an important approach for peptide identification in proteomics, complementary to the commonly used protein database searching approach, in particular for the proteomic analyses of well-studied model organisms, such as human. Existing spectral library searching algorithms compare a query MS/MS spectrum with each spectrum in the library with matched precursor mass and charge state, which may become computationally intensive with the rapidly growing library size. Here, the software msSLASH, which implements a fast spectral library searching algorithm based on the Locality-Sensitive Hashing (LSH) technique, is presented. The algorithm first converts the library and query spectra into bit-strings using LSH functions, and then computes the similarity between the spectra with highly similar bit-string. Using the spectral library searching of large real-world MS/MS spectra datasets, it is demonstrated that the algorithm significantly reduced the number of spectral comparisons, and as a result, achieved 2-9X speedup in comparison with existing spectral library searching algorithm SpectraST. The spectral searching algorithm is implemented in C/C++, and is ready to be used in proteomic data analyses.

Peptide amidination labeling using S-methyl thioacetimidate (SMTA) is investigated in an attempt to increase the number and types of peptides that can be detected in a bottom-up proteomics experiment. This derivatization method affects the basicity of lysine residues, and is shown here to significantly impact the idiosyncracies of peptide fragmentation and peptide detectability. The unique and highly reproducible fragmentation properties of SMTA-labeled peptides, such as the strong propensity for forming b1 fragment ions, can be further exploited to modify the scoring of peptide-spectrum pairs and improve peptide identification. To this end, we have developed a supervised post-processing algorithm to exploit these characteristics of peptides labeled by SMTA. Our experiments show that, although the overall number of identifications are similar, the SMTA modification enabled the detection of 16-26% peptides not previously observed in comparable CID/HCD tandem mass spectrometry experiments without SMTA labeling.

Chemical cross-linking combined with mass spectrometric analysis has become an important technique for probing protein three-dimensional structure and protein-protein interactions. A key step in this process is the accurate identification and validation of cross-linked peptides from tandem mass spectra. The identification of cross-linked peptides, however, presents challenges related to the expanded nature of the search space (all pairs of peptides in a sequence database) and the fact that some peptide-spectrum matches (PSMs) contain one correct and one incorrect peptide but often receive scores that are comparable to those in which both peptides are correctly identified. To address these problems and improve detection of cross-linked peptides, we propose a new database search algorithm, XLSearch, for identifying cross-linked peptides. Our approach is based on a data-driven scoring scheme that independently estimates the probability of correctly identifying each individual peptide in the cross-link given knowledge of the correct or incorrect identification of the other peptide. These conditional probabilities are subsequently used to estimate the joint posterior probability that both peptides are correctly identified. Using the data from two previous cross-link studies, we show the effectiveness of this scoring scheme, particularly in distinguishing between true identifications and those containing one incorrect peptide. We also provide evidence that XLSearch achieves more identifications than two alternative methods at the same false discovery rate (availability: https://github.com/COL-IU/XLSearch ).

Large-scale proteomics projects often generate massive and highly redundant tandem mass spectra. Spectral clustering algorithms can reduce the redundancy in these data sets and thus speed up database searching for peptide identification, a major bottleneck for proteomic data analysis. The key challenge of spectral clustering is to reduce the redundancy in the MS/MS spectra data while retaining sufficient sensitivity to identify peptides from the clustered spectra. We present the software msCRUSH, which implements a novel spectral clustering algorithm based on the locality sensitive hashing technique. When tested on a large-scale proteomic data set consisting of 23.6 million spectra (including 14.4 million spectra of charge 2+), msCRUSH runs 6.9-11.3 times faster than the state-of-the-art spectral clustering software, PRIDE Cluster, while achieving higher clustering sensitivity and comparable accuracy. Using the consensus spectra reported by msCRUSH, commonly used spectra search engines MSGF+ and Mascot can identify 3 and 1% more unique peptides, respectively, compared with the identification results from the raw MS/MS spectra at the same false discovery rate (1% FDR) of peptide level. msCRUSH is implemented in C++ and is released as open-source software.

Neoepitope peptides are newly formed antigens presented by major histocompatibility complex class I (MHC-I) on cell surfaces. The cells presenting neoepitope peptides are recognized and subsequently killed by cytotoxic T-cells. Immunopeptidomic approaches aim to characterize the peptide repertoire (including neoepitope) associated with the MHC-I molecules on the surface of tumor cells using proteomic technologies, providing critical information for designing effective immunotherapy strategies. We developed a novel constrained de novo sequencing algorithm to identify neo-epitope peptides from tandem mass spectra acquired in immunopeptidomic analyses. Our method incorporates prior probabilities to putative peptides according to position specific scoring matrices (PSSMs) representing the sequence preferences recognized by MHC-I molecules. We implemented a dynamic programming algorithm to determine the peptide sequences with an optimal posterior matching score for each given MS/MS spectrum. Similar to the de novo peptide sequencing, the dynamic programming algorithm allows an efficient searching in the entire peptide sequence space. On an LC-MS/MS dataset, we demonstrated the performance of our algorithm in detecting the neoepitope peptides bound by the HLA-C*0501 molecules that were superior to database search approaches and existing general purpose de novo peptide sequencing algorithms.

MotivationRapid advancement in high throughput genome and transcriptome sequencing (HTS) and mass spectrometry (MS) technologies has enabled the acquisition of the genomic, transcriptomic and proteomic data from the same tissue sample. In this paper we introduce a novel computational framework which can integratively analyze all three types of omics data to obtain a complete molecular profile of a tissue sample, in normal and disease conditions. Our framework includes MiStrVar, an algorithmic method we developed to identify micro structural variants (microSVs) on genomic HTS data. Coupled with deFuse, a popular gene fusion detection method we developed earlier, MiStrVar can provide an accurate profile of structurally aberrant transcripts in cancer samples. Given the breakpoints obtained by MiStrVar and deFuse, our framework can then identify all relevant peptides that span the breakpoint junctions and match them with unique proteomic signatures in the respective proteomics data sets. Our framework's ability to observe structural aberrations at three levels of omics data provides means of validating their presence.ResultsWe have applied our framework to all The Cancer Genome Atlas (TCGA) breast cancer Whole Genome Sequencing (WGS) and/or RNA-Seq data sets, spanning all four major subtypes, for which proteomics data from Clinical Proteomic Tumor Analysis Consortium (CPTAC) have been released. A recent study on this dataset focusing on SNVs has reported many that lead to novel peptides [1]. Complementing and significantly broadening this study, we detected 244 novel peptides from 432 candidate genomic or transcriptomic sequence aberrations. Many of the fusions and microSVs we discovered have not been reported in the literature. Interestingly, the vast majority of these translated aberrations (in particular, fusions) were private, demonstrating the extensive inter-genomic heterogeneity present in breast cancer. Many of these aberrations also have matching out-of-frame downstream peptides, potentially indicating novel protein sequence and structure. Moreover, the most significantly enriched genes involved in translated fusions are cancer-related. Furthermore a number of the somatic, translated microSVs are observed in tumor suppressor genes.Contactcenksahi@indiana.edu

Metaproteomic studies adopt the common bottom-up proteomics approach to investigate the protein composition and the dynamics of protein expression in microbial communities. When matched metagenomic and/or metatranscriptomic data of the microbial communities are available, metaproteomic data analyses often employ a metagenome-guided approach, in which complete or fragmental protein-coding genes are first directly predicted from metagenomic (and/or metatranscriptomic) sequences or from their assemblies, and the resulting protein sequences are then used as the reference database for peptide/protein identification from MS/MS spectra. This approach is often limited because protein coding genes predicted from metagenomes are incomplete and fragmental. In this paper, we present a graph-centric approach to improving metagenome-guided peptide and protein identification in metaproteomics. Our method exploits the de Bruijn graph structure reported by metagenome assembly algorithms to generate a comprehensive database of protein sequences encoded in the community. We tested our method using several public metaproteomic datasets with matched metagenomic and metatranscriptomic sequencing data acquired from complex microbial communities in a biological wastewater treatment plant. The results showed that many more peptides and proteins can be identified when assembly graphs were utilized, improving the characterization of the proteins expressed in the microbial communities. The additional proteins we identified contribute to the characterization of important pathways such as those involved in degradation of chemical hazards. Our tools are released as open-source software on github at https://github.com/COL-IU/Graph2Pro., Author Summary In recent years, meta-omic (including metatranscriptomic and metaproteomic) techniques have been adopted as complementary approaches to metagenomic sequencing to study functional characteristics and dynamics of microbial communities, aiming at a holistic understanding of a community to respond to the changes in the environment. Currently, metaproteomic data are largely analyzed using the bioinformatics tools originally designed in bottom-up proteomics. In particular, recent metaproteomic studies employed a metagenome-guided approach, in which complete or fragmental protein-coding genes were first predicted from metagenomic sequences (i.e., contigs or scaffolds), acquired from the matched community samples, and predicted protein sequences were then used in peptide identification. A key challenge of this approach is that the protein coding genes predicted from assembled metagenomic contigs can be incomplete and fragmented due to the complexity of metagenomic samples and the short reads length in metagenomic sequencing. To address this issue, in this paper, we present a graph-centric approach that exploits the de bruijn graph structure reported by metagenome assembly algorithms to improve metagenome-guided peptide and protein identification in metaproteomics. We show that our method can identify much more peptides and proteins, improving the characterization of the proteins expressed in the microbial communities.

This chapter introduces computational methods used in mass spectrometry-based proteomics, including those for addressing the critical problems such as peptide identification and protein inference, peptide and protein quantification, characterization of posttranslational modifications (PTMs), and data-independent acquisitions (DIA). The chapter concludes with emerging applications of proteomic techniques, such as metaproteomics, glycoproteomics, and proteogenomics.

Recently, body fluids have widely become an important target for proteomic research and proteomic study has produced more and more body fluid related protein data. A database is needed to collect and analyze these proteome data. Thus, we developed this web-based body fluid proteome database Sys-BodyFluid. It contains eleven kinds of body fluid proteomes, including plasma/serum, urine, cerebrospinal fluid, saliva, bronchoalveolar lavage fluid, synovial fluid, nipple aspirate fluid, tear fluid, seminal fluid, human milk and amniotic fluid. Over 10 000 proteins are presented in the Sys-BodyFluid. Sys-BodyFluid provides the detailed protein annotations, including protein description, Gene Ontology, domain information, protein sequence and involved pathways. These proteome data can be retrieved by using protein name, protein accession number and sequence similarity. In addition, users can query between these different body fluids to get the different proteins identification information. Sys-BodyFluid database can facilitate the body fluid proteomics and disease proteomics research as a reference database. It is available at http://www.biosino.org/bodyfluid/.

This chapter introduces computational methods used in mass spectrometry-based proteomics, including those for addressing the critical problems such as peptide identification and protein inference, peptide and protein quantification, characterization of posttranslational modifications (PTMs), and data-independent acquisitions (DIA). The chapter concludes with emerging applications of proteomic techniques, such as metaproteomics, glycoproteomics, and proteogenomics.

In order to find evidence for translation of alternatively spliced transcripts, especially those that result in a change in reading frame, we collected exon-skipping cases previously found by RNA-Seq and applied a computational approach to screen millions of mass spectra. These spectra came from seven human and six mouse tissues, five of which are the same between the two organisms: liver, kidney, lung, heart, and brain. Overall, we detected 4 percent of all exon-skipping events found in RNA-seq data, regardless of their effect on reading frame. The fraction of alternative isoforms detected did not differ between out-of-frame and in-frame events. Moreover, the fraction of identified alternative exon-exon junctions and constitutive junctions were similar. Together, our results suggest that both in-frame and out-of-frame translation may be actively used to regulate protein activity or localization.

Label-free absolute protein quantification refers to a process of estimating protein abundances in complex biological samples based on the data acquired from a liquid chromatography mass spectrometry (LC-MS) analysis. Most approaches to label-free quantification rely on measuring peak areas from an extracted-ion chromatogram. However, because of the differences in physicochemical properties associated with different peptide ions, observed peak areas in a single experiment are determined not only by peptide abundances, but also the intrinsic biases of analytical platforms. Therefore, accurate modeling of these biases provides an opportunity to developing new computational methods for precise absolute protein quantification. In this work, we developed a new algorithm for absolute quantification of proteins. The approach is based on the concept of peptide response rate, which characterizes the peptide-specific signal detection bias in an LC-MS experiment. We argue that peptide response rate is an intrinsic and reproducible property of peptide ions that can be reliably predicted using non-linear regression and features extracted from the sequence of the parent protein. Protein abundances are estimated using a maximum likelihood model in which the observed peak areas of peptide ions are adjusted using predicted peptide response rates. We evaluate our approach on a large LC-MS dataset as well as simulated data and provide evidence that the accuracy of absolute protein quantification is improved when peptide-specific response rates are taken into account.

Gallbladder bile, one of the most important body fluids, is composed of water, inorganic ions, conjugated bile salts, phospholipids, cholesterol, bilirubin, mucin and proteins. The separation and identification of bile proteins remain difficult due to the complexity of this matrix. In the present study, human gallbladder bile was obtained from a cholesterol stone patient, and the proteins were isolated and purified by dialysis, precipitation and delipidation procedures. The resulting proteins were divided into several aliquots. One aliquot was subjected to two-dimensional gel electrophoresis (2DE). The protein spots were then in-gel digested and analyzed by liquid chromatography coupled with tandem mass spectrometry (LC/MS/MS). Another aliquot was directly digested and analyzed by a combination of strong cation-exchange (SCX) and reversed-phase (RP) chromatography prior to tandem mass spectrometry (2D-LC/MS/MS). Eventually, 48 and 218 unique proteins were identified from 2DE/MS and 2D-LC/MS/MS, respectively, resulting in a total of 222 unique identified proteins. Of the 218 proteins identified by 2D-LC/MS/MS, 92 were identified based on more than one unique tryptic peptide, and, of the total 222 proteins, 98 were identified based on more than one unique tryptic peptide.

A resurgence of interest in the human plasma proteome has occurred in recent years because it holds great promise of revolution in disease diagnosis and therapeutic monitoring. As one of the most powerful separation techniques, multidimensional liquid chromatography has attracted extensive attention, but most published works have focused on the fractionation of tryptic peptides. In this study, proteins from human plasma were prefractionated by online sequential strong cation exchange chromatography and reversed-phase chromatography. The resulting 30 samples were individually digested by trypsin, and analyzed by capillary reversed-phase liquid chromatography coupled with linear ion trap mass spectrometry. After meeting stringent criteria, a total of 1292 distinct proteins were successfully identified in our work, among which, some proteins known to be present in serum in

Four fractions from rat liver (a crude mitochondria (CM) and cytosol (C) fraction obtained with differential centrifugation, a purified mitochondrial (PM) fraction obtained with nycodenz density gradient centrifugation, and a total liver (TL) fraction) were analyzed with two-dimensional liquid chromatography tandem mass spectrometry analysis. A total of 564 rat proteins were identified and were bioinformatically annotated according to their physicochemical characteristics and functions. While most extreme alkaline ribosomal proteins were identified in the TL fraction, the C fraction mainly included neutral enzymes and the PM fraction enriched alkaline proteins and proteins with electron transfer activity or oxygen binding activity. Such characteristics were more apparent in proteins identified only in the TL, C, or PM fraction. The Swiss-Prot annotation and the bioinformatic prediction results proved that the C and PM fractions had enriched cytoplasmic or mitochondrial proteins, respectively. Combination usage of subcellular fractionation with two-dimensional liquid chromatography tandem mass spectrometry was proved to be a high-throughput, sensitive, and effective analytical approach for subcellular proteomics research. Using such a strategy, we have constructed the largest proteome database to date for rat liver (564 rat proteins) and its cytosol (222 rat proteins) and mitochondrial fractions (227 rat proteins). Moreover, the 352 proteins with Swiss-Prot subcellular location annotation in the 564 identified proteins were used as an actual subcellular proteome dataset to evaluate the widely used bioinformatics tools such as PSORT, TargetP, TMHMM, and GRAVY.

One of the important objectives in mass spectrometry-based proteomics is the identification of post-translationally modified sites in cellular and extracellular proteomes. Proteomics techniques have been particularly effective in studying protein phosphorylation, where tens of thousands of new sites have been recently discovered in all domains of life. Such massive discovery of new sites has been facilitated by progress in affinity enrichment techniques, high-throughput analytical platforms that couple liquid chromatography (LC) and tandem mass spectrometry (MS/MS), and also powerful computational tools that assign peptides to tandem mass spectra. In this work we focus on computational protocols for identifying phosphoproteins, phosphopeptides, and phosphosites. Although the current tools already provide solid results, most methods have not been tuned to exploit particular sequence and physicochemical properties of phosphopeptides or the peculiarities of their fragment spectra. Therefore, novel algorithms can be designed to increase the sensitivity of phosphosite identification. Here we describe a machine learning-based method that improves the identification of phosphopeptides in LC-MS/MS experiments. Our algorithm is applied as a post-processing step to a standard database search. It assigns a probability score to each peptide-spectrum match (PSM) corresponding to a phosphopeptide, based on the sequence and spectral features of the peptide and its assigned fragment spectra as well as the biological propensity of particular residues in the peptide to be phosphorylated. The algorithm is based on a simple but robust logistic regression model and is used together with a conventional search engine (here, MASCOT) to filter out the PSMs with the lowest probability of being correctly identified. Our protocol was tested on two large phosphoproteomics data sets on which it increased the number of identified phosphopeptides by 10-15% compared to the conventional scoring algorithms at the same false discovery rate threshold of 1%.

The tumor necrosis factor (TNF)-alpha/NF-kappaB-signaling pathway plays a pivotal role in various processes including apoptosis, cellular differentiation, host defense, inflammation, autoimmunity and organogenesis. The complexity of the TNF-alpha/NF-kappaB signaling is in part due to the dynamic protein behaviors of key players in this pathway. In this present work, a dynamic and global view of the signaling components in the nucleus at the early stages of TNF-alpha/NF-kappaB signaling was obtained in HEK293 cells, by a combination of subcellular fractionation and stable isotope labeling by amino acids in cell culture (SILAC). The dynamic profile patterns of 547 TNF-alpha-induced nuclei-associated proteins were quantified in our studies. The functional characters of all the profiles were further analyzed using that Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotation. Additionally, many previously unknown effectors of TNF-alpha/NF-kappaB signaling were identified, quantified and clustered into differential activation profiles. Interestingly, levels of Fanconi anemia group D2 protein (FANCD2), one of the Fanconi anemia family proteins, was found to be increased in the nucleus by SILAC quantitation upon TNF-alpha stimulation, which was further verified by western blotting and immunofluorescence analysis. This indicates that FANCD2 might be involved in TNF-alpha/NF-kappaB signaling through its accumulation in the nucleus. In summary, the combination of subcellular proteomics with quantitative analysis not only allowed for a dissection of the nuclear TNF-alpha/NF-kappaB-signaling pathway, but also provided a systematic strategy for monitoring temporal and spatial changes in cell signaling.

Nowadays Proteomics becomes more and more popular in life science. Protein quantification, especially based on mass spectrometry (short for MS) method, is perceived as an essential part of research on proteomics. There have been some algorithms and software for protein quantification based on MS. But they have difficulties on portability, applicability and longtime running. To solve these problems, we developed a new domestic parallel software package called QuantWiz for high performance Liquid Chromatography (short for LC)-MS-based label-free protein quantification. In this paper, we described the framework design and prototype development of this high performance software package firstly. Also, user interface developed for the visualization of QuantWiz is introduced. Finally, we showed implementation of the parallelization version and performance of some experiments on this software package.

Hepatocellular carcinoma (HCC) is a malignancy of both underdeveloped and developing countries. Proteomes of ten pairs of clinical hepatitis B virus associated HCC tissue samples were obtained by high resolution two-dimensional gel electrophoresis. Comprehensive analyses of proteins associated with B-type HCC were focused on total differentially expressed proteins (> or = two-fold increase or decrease, Student's t-test, p < 0.05) from one pair of samples. Protein identification was done by peptide mass fingerprinting with matrix assisted laser desorption/ionization-time of flight mass spectrometry and liquid chromatography-tandem mass spectrometry. Comparative analyses of proteins associated with B-type HCC included repeat statistics in ten cases. A total of 100 protein spots, corresponding to 80 different gene products, were identified. Proteins whose expression levels were different by more than 2-fold in at least 50% of the cases (five of ten cases) were further analyzed and 45 proteins were selected out as candidates for HCC-associated proteins. Western blotting further validated up-regulated expressions of two candidate proteins in tumor tissues: proliferating cell antigen and stathmin 1. This comprehensive and comparative analyses of proteins associated with B-type HCC could provide useful molecular markers for diagnostics and prognostics and for therapeutic targets. The physiological significance of the differential expressions for several candidate proteins are discussed.

BACKGROUND: Recent advances in proteomics have shed light to discover serum proteins or peptides as biomarkers for tracking the progression of diabetes as well as understanding molecular mechanisms of the disease. RESULTS: In this work, human serum of non-diabetic and diabetic cohorts was analyzed by proteomic approach. To analyze total 1377 high-confident serum-proteins, we developed a computing strategy called localized statistics of protein abundance distribution (LSPAD) to calculate a significant bias of a particular protein-abundance between these two cohorts. As a result, 68 proteins were found significantly over-represented in the diabetic serum (p