scholarly journals Proteomic Database Search Engine for Two-Dimensional Partial Covariance Mass Spectrometry

2021 ◽  
Author(s):  
Taran Driver ◽  
Ruediger Pipkorn ◽  
Leszek Frasinski ◽  
Jon P. Marangos ◽  
Marina Edelson-Averbukh ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Search Engine ◽  
Database Search ◽  
Peptide Sequence ◽  
Two Dimensional ◽  
Protein Database ◽  
Intact Proteins ◽  
Individual Fragment ◽  
Decomposition Processes ◽  
Complementary Fragment

<div>We present a protein database search engine for the automatic identi?cation of peptide and protein sequences using the recently introduced method of two-dimensional partial covariance mass spectrometry (2D-PC-MS). Since 2D-PC-MS measurement reveals correlations between fragments stemming from the same or consecutive decomposition processes, the ?first-of-its-kind 2D-PC-MS search engine is based entirely on the direct matching of the pairs of theoretical and the experimentally detected correlating fragments, rather than of individual fragment signals or their series. We demonstrate that the high structural speci?city a?orded by 2D-PC-MS fragment correlations enables our search engine to reliably identify the correct peptide sequence, even from a spectrum with a large proportion of contaminant signals. While for peptides the 2D-PC-MS correlation matching procedure is based on complementary and internal ion correlations, the identi?cation of intact proteins is entirely based on the ability of 2D-PC-MS to spatially separate and resolve the experimental correlations between complementary fragment ions.</div>

scholarly journals Proteomic Database Search Engine for Two-Dimensional Partial Covariance Mass Spectrometry

2021 ◽  
Author(s):  
Taran Driver ◽  
Ruediger Pipkorn ◽  
Leszek Frasinski ◽  
Jon P. Marangos ◽  
Marina Edelson-Averbukh ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Search Engine ◽  
Database Search ◽  
Peptide Sequence ◽  
Two Dimensional ◽  
Protein Database ◽  
Intact Proteins ◽  
Individual Fragment ◽  
Decomposition Processes ◽  
Complementary Fragment

<div>We present a protein database search engine for the automatic identi?cation of peptide and protein sequences using the recently introduced method of two-dimensional partial covariance mass spectrometry (2D-PC-MS). Since 2D-PC-MS measurement reveals correlations between fragments stemming from the same or consecutive decomposition processes, the ?first-of-its-kind 2D-PC-MS search engine is based entirely on the direct matching of the pairs of theoretical and the experimentally detected correlating fragments, rather than of individual fragment signals or their series. We demonstrate that the high structural speci?city a?orded by 2D-PC-MS fragment correlations enables our search engine to reliably identify the correct peptide sequence, even from a spectrum with a large proportion of contaminant signals. While for peptides the 2D-PC-MS correlation matching procedure is based on complementary and internal ion correlations, the identi?cation of intact proteins is entirely based on the ability of 2D-PC-MS to spatially separate and resolve the experimental correlations between complementary fragment ions.</div>

scholarly journals Chimera Spectrum Diagnostics for Peptides Using Two-Dimensional Partial Covariance Mass Spectrometry

Molecules ◽  
2021 ◽  
Vol 26 (12) ◽  
pp. 3728
Author(s):  
Taran Driver ◽  
Nikhil Bachhawat ◽  
Leszek J. Frasinski ◽  
Jonathan P. Marangos ◽  
Vitali Averbukh ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Peptide Sequence ◽  
Two Dimensional ◽  
Peptide Ions ◽  
Finite Mass ◽  
Protein Database ◽  
Spectra Analysis ◽  
Fragment Ions ◽  
Single Precursor ◽  
The Common

The rate of successful identification of peptide sequences by tandem mass spectrometry (MS/MS) is adversely affected by the common occurrence of co-isolation and co-fragmentation of two or more isobaric or isomeric parent ions. This results in so-called `chimera spectra’, which feature peaks of the fragment ions from more than a single precursor ion. The totality of the fragment ion peaks in chimera spectra cannot be assigned to a single peptide sequence, which contradicts a fundamental assumption of the standard automated MS/MS spectra analysis tools, such as protein database search engines. This calls for a diagnostic method able to identify chimera spectra to single out the cases where this assumption is not valid. Here, we demonstrate that, within the recently developed two-dimensional partial covariance mass spectrometry (2D-PC-MS), it is possible to reliably identify chimera spectra directly from the two-dimensional fragment ion spectrum, irrespective of whether the co-isolated peptide ions are isobaric up to a finite mass accuracy or isomeric. We introduce ‘3-57 chimera tag’ technique for chimera spectrum diagnostics based on 2D-PC-MS and perform numerical simulations to examine its efficiency. We experimentally demonstrate the detection of a mixture of two isomeric parent ions, even under conditions when one isomeric peptide is at one five-hundredth of the molar concentration of the second isomer.

Proteomic Database Search Engine for Two-Dimensional Partial Covariance Mass Spectrometry

2021 ◽  
Author(s):  
Taran Driver ◽  
Nikhil Bachhawat ◽  
Rüdiger Pipkorn ◽  
Leszek J. Frasiński ◽  
Jon P. Marangos ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Search Engine ◽  
Database Search ◽  
Two Dimensional

FEATURE SELECTION IN VALIDATING MASS SPECTROMETRY DATABASE SEARCH RESULTS

2008 ◽  
Vol 06 (01) ◽  
pp. 223-240 ◽  
Author(s):  
JIANWEN FANG ◽  
YINGHUA DONG ◽  
TODD D. WILLIAMS ◽  
GERALD H. LUSHINGTON
Keyword(s):  
Mass Spectrometry ◽  
Support Vector Machine ◽  
Feature Selection ◽  
Search Engine ◽  
Peptide Sequencing ◽  
False Positives ◽  
Database Search ◽  
Support Vector ◽  
Protein Database ◽  
Model Based

Tandem mass spectrometry (MS/MS) combined with protein database searching has been widely used in protein identification. A validation procedure is generally required to reduce the number of false positives. Advanced tools using statistical and machine learning approaches may provide faster and more accurate validation than manual inspection and empirical filtering criteria. In this study, we use two feature selection algorithms based on random forest and support vector machine to identify peptide properties that can be used to improve validation models. We demonstrate that an improved model based on an optimized set of features reduces the number of false positives by 58% relative to the model which used only search engine scores, at the same sensitivity score of 0.8. In addition, we develop classification models based on the physicochemical properties and protein sequence environment of these peptides without using search engine scores. The performance of the best model based on the support vector machine algorithm is at 0.8 AUC, 0.78 accuracy, and 0.7 specificity, suggesting a reasonably accurate classification. The identified properties important to fragmentation and ionization can be either used in independent validation tools or incorporated into peptide sequencing and database search algorithms to improve existing software programs.

scholarly journals Ariadne: a database search engine for identification and chemical analysis of RNA using tandem mass spectrometry data

2009 ◽  
Vol 37 (6) ◽  
pp. e47-e47 ◽  
Author(s):  
Hiroshi Nakayama ◽  
Misaki Akiyama ◽  
Masato Taoka ◽  
Yoshio Yamauchi ◽  
Yuko Nobe ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Chemical Analysis ◽  
Tandem Mass Spectrometry ◽  
Search Engine ◽  
Database Search ◽  
Mass Spectrometry Data ◽  
Tandem Mass ◽  
Tandem Mass Spectrometry Data

Abstract 613: A Mass Spectrometric Analysis Of Embryonic Zebrafish Proteins

Circulation ◽  
2007 ◽  
Vol 116 (suppl_16) ◽  
Author(s):  
Margaret B Lucitt ◽  
Tom S Price ◽  
Angel Pizarro ◽  
Weichen Wu ◽  
Anastasia Yocum Yocum ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Protein Expression ◽  
Large Scale ◽  
Molecular Mechanisms ◽  
Model Organism ◽  
Mass Spectrometric ◽  
Peptide Sequence ◽  
Spectrometric Analysis ◽  
Cardiovascular Development ◽  
Two Dimensional

Zebrafish is an attractive vertebrate model organism for studies into the molecular mechanisms of cardiovascular development, pathology and pharmacology. Studies into the genetics of protein expression are largely constrained by the availability of specific antibodies. Mass spectrometry based proteomics methods have the potential to overcome these hurdles. This requires firstly an accurate characterization of proteins accessible to targeted quantitative analysis. We applied mass spectrometric proteomic methodology and statistical analysis to create profiles of proteins expressed during zebrafish embryonic development. We detected 1307 proteins from 327,906 peptide sequence identifications at 72 hpf and 120 hpf with false identification rates of less than 1% using two dimensional chromatography tandem mass spectrometry. Close to two thirds of all detected proteins were derived from hypothetical or predicted gene models or were entirely unannotated. Comparison of protein expression in embryos by two dimensional gel electrophoresis differential in gel analysis (DIGE) revealed that proteins involved in energy production and transcription/ translation were relatively more abundant at 72 hpf consistent with the faster synthesis of cellular proteins during organismal growth. Pathway analysis revealed similar expression of proteins at both stages that relate to calcium, insulin receptor, ERK/MAP kinase, vascular epithelial growth factor signaling, and WNT/b-Catenin. Similarly both stages expressed proteins of the complement and coagulation cascades, GM-CSF, PTEN, and sonic hedgehog signaling and inflammatory signals. The data are accessible in a fully searchable database (http://bioinf.itmat.upenn.edu/zebrafish) that links protein identifications to existing resources including the Zebrafish Model Organism Database. This new resource should facilitate the selection of candidate proteins for targeted quantitation and may refine systematic genetic network analysis in vertebrate development and biology. This is the first large-scale proteome analysis of embryonic zebrafish tissue to reveal previously uncharacterized proteins and detect regulated proteins with relevance for cardiovascular function and development.

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