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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2011

Peptide identification

Peptides are identified from a list of candidates using collision-induced dissociation tandem mass spectrometry data. A probabilistic model for the occurrence of spectral peaks corresponding to frequently observed partial peptide fragment ions is applied. As part of the identification procedure, a probability score is produced that indicates the likelihood of any given candidate being the correct match. The statistical significance of the score is known without necessarily having reference to the actual identity of the peptide. In one form of the invention, a genetic algorithm is applied to candidate peptides using an objective function that takes into account the number of shifted peaks appearing in the candidate spectrum relative to the test spectrum.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2011

A Statistical Method for Assessing Peptide Identification Confidence in Accurate Mass and Time Tag Proteomics

High-throughput proteomics is rapidly evolving to require high mass measurement accuracy for a variety of different applications. Increased mass measurement accuracy in bottom-up proteomics specifically allows for an improved ability to distinguish and characterize detected MS features, which may in turn be identified by, e.g., matching to entries in a database for both precursor and fragmentation mass identification methods. Many tools exist with which to score the identification of peptides from LC-MS/MS measurements or to assess matches to an accurate mass and time (AMT) tag database, but these two calculations remain distinctly unrelated. Here we present a statistical method, Statistical Tools for AMT tag Confidence (STAC), which extends our previous work incorporating prior probabilities of correct sequence identification from LC-MS/MS, as well as the quality with which LC-MS features match AMT tags, to evaluate peptide identification confidence. Compared to existing tools, we are able to obtain significantly more high-confidence peptide identifications at a given false discovery rate and additionally assign confidence estimates to individual peptide identifications. Freely available software implementations of STAC are available in both command line and as a Windows graphical application.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Flying Blind, or Just Flying Under the Radar? The Underappreciated Power of De Novo Methods of Mass Spectrometric Peptide Identification

Mass spectrometry-based proteomics is a popular and powerful method for precise and highly multiplexed protein identification. The most common method of analyzing untargeted proteomics data is called database searching, where the database is simply a collection of protein sequences from the target organism, derived from genome sequencing. Experimental peptide tandem mass spectra are compared to simplified models of theoretical spectra calculated from the translated genomic sequences. However, in several interesting application areas, such as forensics, archaeology, venomics and others, a genome sequence may not be available, or the correct genome sequence to use is not known. In these cases, de novo peptide identification can play an important role. De novo peptide identification infers peptide sequence directly from the tandem mass spectrum without reference to a sequence database, usually using graph-based or machine learning algorithms. In this review, we provide a basic overview of de novo peptide identification methods and applications, briefly covering de novo algorithms and tools, and focusing in more depth on recent applications from venomics, metaproteomics, forensics, and characterization of antibody drugs.

proteomics, mass spectrometry, forensics, de novo ↗
DOE OSTI2011

Effectiveness of CID, HCD, and ETD with FT MS/MS for Degradomic-Peptidomic Analysis: Comparison of Peptide Identification Methods

We report on use of an Orbitrap Velos mass spectrometer for comparison of fragmentation methods namely CID-, HCD-, and ETD for FT MS/MS analysis of human blood plasma peptidomic peptides. The peptidomic peptides were able to be identified from CID, HCD, and ETD spectra on specific confidence levels (e.g., 1% false discovery rate) with use of conventional SEQUEST database search software, and the number of identified peptides was increased by ~50% using accurate fragments (e.g., with mass tolerance of 0.05Da) in comparison with traditional moderate accuracy fragments (e.g., with 1 Da mass tolerance) for database search. However, the peptide datasets identified with such decoy search strategy were found to be varied by ~25% in the dataset size and ~20% in the dataset content with type of decoy database and precursor mass tolerances used for database search. CID was evaluated as the largest contributor to the identified peptide datasets, and HCD, and ETD provided ~20% and ~22% respectively additional peptides with accurate fragments for peptide identification, in contrast to ~25% and ~13% respectively with use of moderate accuracy fragments. When long (typically ≥7 amino acids) sequences were used for identification of peptides from the previously published UStags and de novo sequencing methods, HCD was evaluated as the largest contributor, and CID and ETD provided ~26% and ~8% respectively additional peptides from the UStags method and ~26% and ~6% respectively additional peptides from the de novo sequencing method. The peptide datasets identified with the UStags method were little influenced by the decoy database and mass tolerance and 98-99% peptide overlaps could be achieved between these datasets. CID, HCD, and ETD contributed their identifications of various charge state peptides in the m/z range highly overlapped and complementary implementation of CID, HCD, and ETD should be applied to maximize the number of peptides identified. Finally, the investigation presented in this work suggests that selection of fragmentation methods benefits significantly from a careful match with data analysis method applied for identification of peptides and the analysis throughput desired for various purposes of peptidomic-degradomic analysis.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2011

MapReduce Implementation of a Hybrid Spectral Library-Database Search Method for Large-Scale Peptide Identification

A MapReduce-based implementation called MR- MSPolygraph for parallelizing peptide identification from mass spectrometry data is presented. The underlying serial method, MSPolygraph, uses a novel hybrid approach to match an experimental spectrum against a combination of a protein sequence database and a spectral library. Our MapReduce implementation can run on any Hadoop cluster environment. Experimental results demonstrate that, relative to the serial version, MR-MSPolygraph reduces the time to solution from weeks to hours, for processing tens of thousands of experimental spectra. Speedup and other related performance studies are also reported on a 400-core Hadoop cluster using spectral datasets from environmental microbial communities as inputs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2013

STEPS: A Grid Search Methodology for Optimized Peptide Identification Filtering of MS/MS Database Search Results

For bottom-up proteomics there are a wide variety of database searching algorithms in use for matching peptide sequences to tandem MS spectra. Likewise, there are numerous strategies being employed to produce a confident list of peptide identifications from the different search algorithm outputs. Here we introduce a grid search approach for determining optimal database filtering criteria in shotgun proteomics data analyses that is easily adaptable to any search. Systematic Trial and Error Parameter Selection - referred to as STEPS - utilizes user-defined parameter ranges to test a wide array of parameter combinations to arrive at an optimal "parameter set" for data filtering, thus maximizing confident identifications. The benefits of this approach in terms of numbers of true positive identifications are demonstrated using datasets derived from immunoaffinity-depleted blood serum and a bacterial cell lysate, two common proteomics sample types.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2016

Multiplexed Post-Experimental Monoisotopic Mass Refinement ( m PE-MMR) to Increase Sensitivity and Accuracy in Peptide Identifications from Tandem Mass Spectra of Cofragmentation

Mass spectrometry (MS)-based proteomics, which uses high-resolution hybrid mass spectrometers such as the quadrupole-orbitrap mass spectrometer, can yield tens of thousands of tandem mass (MS/MS) spectra of high resolution during a routine bottom-up experiment. Despite being a fundamental and key step in MS-based proteomics, the accurate determination and assignment of precursor monoisotopic masses to the MS/MS spectra remains difficult. The difficulties stem from imperfect isotopic envelopes of precursor ions, inaccurate charge states for precursor ions, and cofragmentation. We describe a composite method of utilizing MS data to assign accurate monoisotopic masses to MS/MS spectra, including those subject to cofragmentation. The method, “multiplexed post-experiment monoisotopic mass refinement” (mPE-MMR), consists of the following: multiplexing of precursor masses to assign multiple monoisotopic masses of cofragmented peptides to the corresponding multiplexed MS/MS spectra, multiplexing of charge states to assign correct charges to the precursor ions of MS/ MS spectra with no charge information, and mass correction for inaccurate monoisotopic peak picking. When combined with MS-GF+, a database search algorithm based on fragment mass difference, mPE-MMR effectively increases both sensitivity and accuracy in peptide identification from complex high-throughput proteomics data compared to conventional methods.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2011

Large Improvements in MS/MS Based Peptide Identification Rates using a Hybrid Analysis

We have developed a hybrid method for identifying peptides from global proteomics studies that significantly increases sensitivity and specificity in matching peptides to tandem mass spectra using database searches. The method increased the number of spectra that can be assigned to a peptide in a global proteomics study by 57-147% at an estimated false discovery rate of 5%, with clear room for even greater improvements. The approach combines the general utility of using consensus model spectra typical of database search methods1-3 with the accuracy of the intensity information contained in spectral libraries4-6. This hybrid approach is made possible by recent developments that elucidated the statistical framework common to both data analysis and statistical thermodynamics, resulting in a chemically inspired approach to incorporating fragment intensity information into both database searches and spectral library searches. We applied this approach to proteomics analysis of Synechococcus sp. PCC 7002, a cyanobacterium that is a model organism for studies of photosynthetic carbon fixation and biofuels development. The increased specificity and sensitivity of this approach allowed us to identify many more peptides involved in the processes important for photoautotrophic growth.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2013

Basophile: Accurate Fragment Charge State Prediction Improves Peptide Identification Rates

In shotgun proteomics, database search algorithms rely on fragmentation models to predict fragment ions that should be observed for a given peptide sequence. The most widely used strategy (Naive model) is oversimplified, cleaving all peptide bonds with equal probability to produce fragments of all charges below that of the precursor ion. More accurate models, based on fragmentation simulation, are too computationally intensive for on-the-fly use in database search algorithms. We have created an ordinal-regression-based model called Basophile that takes fragment size and basic residue distribution into account when determining the charge retention during CID/higher-energy collision induced dissociation (HCD) of charged peptides. This model improves the accuracy of predictions by reducing the number of unnecessary fragments that are routinely predicted for highly-charged precursors. Basophile increased the identification rates by 26% (on average) over the Naive model, when analyzing triply-charged precursors from ion trap data. Basophile achieves simplicity and speed by solving the prediction problem with an ordinal regression equation, which can be incorporated into any database search software for shotgun proteomic identification.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2010

Combined Statistical Analyses of Peptide Intensities and Peptide Occurrences Improves Identification of Significant Peptides from MS-based Proteomics Data

Liquid chromatography-mass spectrometry-based (LC-MS) proteomics uses peak intensities of proteolytic peptides to infer the differential abundance of peptides/proteins. However, substantial run-to-run variability in peptide intensities and observations (presence/absence) of peptides makes data analysis quite challenging. The missing abundance values in LC-MS proteomics data are difficult to address with traditional imputation-based approaches because the mechanisms by which data are missing are unknown a priori. Data can be missing due to random mechanisms such as experimental error, or non-random mechanisms such as a true biological effect. We present a statistical approach that uses a test of independence known as a G-test to test the null hypothesis of independence between the number of missing values and the experimental groups. We pair the G-test results evaluating independence of missing data (IMD) with a standard analysis of variance (ANOVA) that uses only means and variances computed from the observed data. Each peptide is therefore represented by two statistical confidence metrics, one for qualitative differential observation and one for quantitative differential intensity. We use two simulated and two real LC-MS datasets to demonstrate the robustness and sensitivity of the ANOVA-IMD approach for assigning confidence to peptides with significant differential abundance among experimental groups.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2012

Improving Collision Induced Dissociation (CID), High Energy Collision Dissociation (HCD), and Electron Transfer Dissociation (ETD) Fourier Transform MS/MS Degradome-Peptidome Identifications Using High Accuracy Mass Information

Identification of peptides through analysis of peptide ion fragmentation mass spectra is key to proteomic/peptidomic studies. Advanced mass spectrometry has enabled measurement of peptide fragments with {approx}ppm mass error levels; however, mass accuracy has not been widely utilized in broad aspects of peptide identification. In this work we describe how peptide high-precision fragments measured with Fourier transform mass spectrometry can provide a powerful discriminative capability to distinguish true peptide 'hits' from possible incorrect peptides. For identification of peptides having multiple termini and carrying multiple charges, we show that the simple utilization of high-precision mass fragments information results in improved peptide analysis coverage and consistency, with coverage of >95% peptides identified totally from different spectral scoring and peptide statistic probability methods developed for moderate accuracy mass spectral peptide identification.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Short-Term Stable Isotope Probing of Proteins Reveals Taxa Incorporating Inorganic Carbon in a Hot Spring Microbial Mat

The upper green layer of the chlorophototrophic microbial mats associated with the alkaline siliceous hot springs of Yellowstone National Park consists of oxygenic cyanobacteria ( Synechococcus spp.), anoxygenic Roseiflexus spp., and several other anoxygenic chlorophototrophs. Synechococcus spp. are believed to be the main fixers of inorganic carbon (C i ), but some evidence indicates that Roseiflexus spp. also contribute to inorganic carbon fixation during low-light, anoxic morning periods. Contributions of other phototrophic taxa have not been investigated. In order to follow the pathway of C i incorporation into different taxa, mat samples were incubated with [ 13 C]bicarbonate for 3 h during the early-morning, low-light anoxic period. Extracted proteins were treated with trypsin and analyzed by mass spectrometry, leading to peptide identifications and peptide isotopic profile signatures containing evidence of 13 C label incorporation. A total of 25,483 peptides, corresponding to 7,221 proteins, were identified from spectral features and associated with mat taxa by comparison to metagenomic assembly sequences. A total of 1,417 peptides, derived from 720 proteins, were detectably labeled with 13 C. Most 13 C-labeled peptides were derived from proteins of Synechococcus spp. and Roseiflexus spp. Chaperones and proteins of carbohydrate metabolism were most abundantly labeled. Proteins associated with photosynthesis, C i fixation, and N 2 fixation were also labeled in Synechococcus spp. Importantly, most proteins of the 3-hydroxypropionate bi-cycle for C i fixation in Roseiflexus spp. were labeled, establishing that members of this taxocene contribute to C i fixation. Other taxa showed much lower [ 13 C]bicarbonate incorporation. IMPORTANCE Yellowstone hot spring mats have been studied as natural models for understanding microbial community ecology and as modern analogs of stromatolites, the earliest community fossils on Earth. Stable-isotope probing of proteins (Pro-SIP) permitted short-term interrogation of the taxa that are involved in the important process of light-driven C i fixation in this highly active community and will be useful in linking other metabolic processes to mat taxa. Here, evidence is presented that Roseiflexus spp., which use the 3-hydroxypropionate bi-cycle, are active in C i fixation. Because this pathway imparts a lower degree of selection of isotopically heavy C i than does the Calvin-Benson-Bassham cycle, the results suggest a mechanism to explain why the natural abundance of 13 C in mat biomass is greater than expected if only the latter pathway were involved. Understanding how mat community members influence the 13 C/ 12 C ratios of mat biomass will help geochemists interpret the 13 C/ 12 C ratios of organic carbon in the fossil record.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Optimizing metaproteomics database construction: lessons from a study of the vaginal microbiome

Metaproteomics, a method for untargeted, high-throughput identification of proteins in complex samples, provides functional information about microbial communities and can tie functions to specific taxa. Metaproteomics often generates less data than other omics techniques, but analytical workflows can be improved to increase usable data in metaproteomic outputs. Identification of peptides in the metaproteomic analysis is performed by comparing mass spectra of sample peptides to a reference database of protein sequences. Although these protein databases are an integral part of the metaproteomic analysis, few studies have explored how database composition impacts peptide identification. Here, we used cervicovaginal lavage (CVL) samples from a study of bacterial vaginosis (BV) to compare the performance of databases built using six different strategies. We evaluated broad versus sample-matched databases, as well as databases populated with proteins translated from metagenomic sequencing of the same samples versus sequences from public repositories. Smaller sample-matched databases performed significantly better, driven by the statistical constraints on large databases. Additionally, large databases attributed up to 34% of significant bacterial hits to taxa absent from the sample, as determined orthogonally by 16S rRNA gene sequencing. We also tested a set of hybrid databases which included bacterial proteins from NCBI RefSeq and translated bacterial genes from the samples. These hybrid databases had the best overall performance, identifying 1,068 unique human and 1,418 unique bacterial proteins, ~30% more than a database populated with proteins from typical vaginal bacteria and fungi. Our findings can help guide the optimal identification of proteins while maintaining statistical power for reaching biological conclusions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Subnanogram proteomics: Impact of LC column selection, MS instrumentation and data analysis strategy on proteome coverage for trace samples

One of the greatest challenges for mass spectrometry (MS)-based proteomics is the limited ability to analyze small samples. Here we investigate the relative contributions of liquid chromatography (LC), MS instrumentation and data analysis methods with the aim of improving proteome coverage for sample sizes ranging from 0.5 ng to 50 ng. We show that the LC separations utilizing 30-µm-i.d. columns increase signal intensity by >3-fold relative to those using 75-µm-i.d. columns, leading to 32% increase in peptide identifications. The Orbitrap Fusion Lumos mass spectrometer significantly boosted both sensitivity and sequencing speed relative to earlier generation Orbitraps (e.g., LTQ-Orbitrap), leading to a ~3× increase in peptide identifications and 1.7× increase in identified protein groups for 2 ng tryptic digests of bacterial lysate. The Match Between Runs algorithm of open-source MaxQuant software further increased proteome coverage by ~ 95% for 0.5 ng samples and by ~42% for 2 ng samples. The present platform is capable of identifying >3000 protein groups from tryptic digestion of cell lysates equivalent to 50 HeLa cells and 100 THP-1 cells (~10 ng total proteins), respectively, and >950 proteins from subnanogram bacterial and archaeal cell lysates. The present ultrasensitive LC-MS platform is expected to enable deep proteome coverage for subnanogram samples, including single mammalian cells.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2018

Identification of peptide based B-cell epitopes in Zika virus NS1

Highlights: • B-cell epitope mapping in ZIKV NS1. • Identification of the ZIKV NS1 specific antibody binding site. • Tool for design of diagnostics and structure-based vaccine against ZIKV. Zika virus (ZIKV), a mosquito-borne flavivirus that has recently emerged globally, poses a major threat to public health. To control this emerging disease, accurate diagnostics are required for monitoring current ZIKV outbreaks. Owing to the high nucleotide sequence similarity and cross-reactivity of ZIKV with other members of the Flaviviridae family, discrimination from other flavivirus infections is often difficult in endemic areas. ZIKV NS1 induces major virus-specific antibodies and is therefore utilized as a serological marker for ZIKV diagnosis. To identify ZIKV specific epitopes for clinical application, 33 NS1 peptides that are 15–30 amino acid in length covering whole NS1 were synthesized and analyzed linear B-cell epitopes with 38 human serum samples (20 ZIKV-positive and 18 ZIKV-negative). As a result of screening, eight epitope regions were identified. In particular, the Z8 and Z14 peptides located in the β-ladder surface region showed higher levels of binding activity in ZIKV-positive sera without cross-reactivity to other flaviviruses. These identified sensitive and specific epitopes provide a tool for design of diagnostics and structure-based vaccine antigens for ZIKV infection.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2010

Improved LC-MS/MS Spectral Counting Statistics by Recovering Low Scoring Spectra Matched to Confidently Identified Peptide Sequences

Spectral counting has become a popular semi-quantitative method for LC-MS/MS based proteome quantification; however, this methodology is often not reliable when proteins are identified by a small number of spectra. Here we present a simple strategy to improve spectral counting based quantification for low abundance proteins by recovering low quality or low scoring spectra for confidently identified peptides. In this approach, stringent data filtering criteria were initially applied to achieve confident peptide identifications with low false discovery rate (e.g., <1%) after LC-MS/MS analysis and database search by SEQUEST. Then, all low scoring MS/MS spectra that match to this set of confidently identified peptides were recovered, leading to more than 20% increase of total identified spectra. The validity of these recovered spectra was assessed by the parent ion mass measurement error distribution, retention time distribution, and by comparing the individual low score and high score spectra that correspond to the same peptides. The results support that the recovered low scoring spectra have similar confidence levels in peptide identifications as the spectra passing the initial stringent filter. The application of this strategy of recovering low scoring spectra significantly improved the spectral count quantification statistics for low abundance proteins, as illustrated in the identification of mouse brain region specific proteins.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2010

Integrated Post-Experiment Monoisotopic Mass Refinement: An Integrated Approach to Accurately Assign Monoisotopic Precursor Masses to Tandem Mass Spectrometric Data

Accurate assignment of monoisotopic precursor masses to tandem mass spectrometric (MS/MS) data is a fundamental and critically important step for successful peptide identifications in mass spectrometry based proteomics. Here we describe an integrated approach that combines three previously reported methods of treating MS/MS data for precursor mass refinement. This combined method, “integrated Post-Experiment Monoisotopic Mass Refinement” (iPE MMR), integrates steps: 1) generation of refined MS/MS data by DeconMSn, 2) additional refinement of the resultant MS/MS data by a modified version of PE-MMR, and 3) elimination of systematic errors of precursor masses using DtaRefinery. iPE-MMR is the first method that utilizes all MS information from multiple MS scans of a precursor ion and multiple charge states of it in an MS scan to determine precursor mass. By combining the synergistic features of each of method, iPE MMR increases sensitivity in peptide identification and provides increased accuracy when applied to complex high-throughput proteomics data. iPE MMR also allows incorporating additional data processing step(s) or skipping step(s), if necessary, to enable new developments or applications of the tools, as each step of iPE MMR produces output data in a common and conventional format used in proteomics data processing.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗