Literature DB >> 31378069

Improved Protein Inference from Multiple Protease Bottom-Up Mass Spectrometry Data.

Rachel M Miller1, Robert J Millikin1, Connor V Hoffmann1, Stefan K Solntsev1, Gloria M Sheynkman1, Michael R Shortreed1, Lloyd M Smith1.   

Abstract

Peptides detected by tandem mass spectrometry (MS/MS) in bottom-up proteomics serve as proxies for the proteins expressed in the sample. Protein inference is a process routinely applied to these peptides to generate a plausible list of candidate protein identifications. The use of multiple proteases for parallel protein digestions expands sequence coverage, provides additional peptide identifications, and increases the probability of identifying peptides that are unique to a single protein, which are all valuable for protein inference. We have developed and implemented a multi-protease protein inference algorithm in MetaMorpheus, a bottom-up search software program, which incorporates the calculation of protease-specific q-values and preserves the association of peptide sequences and their protease of origin. This integrated multi-protease protein inference algorithm provides more accurate results than either the aggregation of results from the separate analysis of the peptide identifications produced by each protease (separate approach) in MetaMorpheus, or results that are obtained using Fido, ProteinProphet, or DTASelect2. MetaMorpheus' integrated multi-protease data analysis decreases the ambiguity of the protein group list, reduces the frequency of erroneous identifications, and increases the number of post-translational modifications identified, while combining multi-protease search and protein inference into a single software program.

Entities:  

Keywords:  bottom-up; data-dependent acquisition; mass spectrometry; multiple proteases; protein inference

Year:  2019        PMID: 31378069      PMCID: PMC6733628          DOI: 10.1021/acs.jproteome.9b00330

Source DB:  PubMed          Journal:  J Proteome Res        ISSN: 1535-3893            Impact factor:   4.466


  21 in total

1.  Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search.

Authors:  Andrew Keller; Alexey I Nesvizhskii; Eugene Kolker; Ruedi Aebersold
Journal:  Anal Chem       Date:  2002-10-15       Impact factor: 6.986

2.  A statistical model for identifying proteins by tandem mass spectrometry.

Authors:  Alexey I Nesvizhskii; Andrew Keller; Eugene Kolker; Ruedi Aebersold
Journal:  Anal Chem       Date:  2003-09-01       Impact factor: 6.986

Review 3.  Protein inference: a review.

Authors:  Ting Huang; Jingjing Wang; Weichuan Yu; Zengyou He
Journal:  Brief Bioinform       Date:  2012-02-28       Impact factor: 11.622

4.  Interpretation of shotgun proteomic data: the protein inference problem.

Authors:  Alexey I Nesvizhskii; Ruedi Aebersold
Journal:  Mol Cell Proteomics       Date:  2005-07-11       Impact factor: 5.911

5.  DTASelect and Contrast: tools for assembling and comparing protein identifications from shotgun proteomics.

Authors:  David L Tabb; W Hayes McDonald; John R Yates
Journal:  J Proteome Res       Date:  2002 Jan-Feb       Impact factor: 4.466

6.  Improving sensitivity by probabilistically combining results from multiple MS/MS search methodologies.

Authors:  Brian C Searle; Mark Turner; Alexey I Nesvizhskii
Journal:  J Proteome Res       Date:  2008-01       Impact factor: 4.466

7.  Universal sample preparation method for proteome analysis.

Authors:  Jacek R Wiśniewski; Alexandre Zougman; Nagarjuna Nagaraj; Matthias Mann
Journal:  Nat Methods       Date:  2009-04-19       Impact factor: 28.547

8.  iProphet: multi-level integrative analysis of shotgun proteomic data improves peptide and protein identification rates and error estimates.

Authors:  David Shteynberg; Eric W Deutsch; Henry Lam; Jimmy K Eng; Zhi Sun; Natalie Tasman; Luis Mendoza; Robert L Moritz; Ruedi Aebersold; Alexey I Nesvizhskii
Journal:  Mol Cell Proteomics       Date:  2011-08-29       Impact factor: 5.911

9.  Value of using multiple proteases for large-scale mass spectrometry-based proteomics.

Authors:  Danielle L Swaney; Craig D Wenger; Joshua J Coon
Journal:  J Proteome Res       Date:  2010-03-05       Impact factor: 4.466

10.  Efficient marginalization to compute protein posterior probabilities from shotgun mass spectrometry data.

Authors:  Oliver Serang; Michael J MacCoss; William Stafford Noble
Journal:  J Proteome Res       Date:  2010-10-01       Impact factor: 4.466

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  12 in total

1.  Improving Proteoform Identifications in Complex Systems Through Integration of Bottom-Up and Top-Down Data.

Authors:  Leah V Schaffer; Robert J Millikin; Michael R Shortreed; Mark Scalf; Lloyd M Smith
Journal:  J Proteome Res       Date:  2020-07-10       Impact factor: 4.466

2.  Internal Fragment Ions Disambiguate and Increase Identifications in Top-Down Proteomics.

Authors:  Zach Rolfs; Lloyd M Smith
Journal:  J Proteome Res       Date:  2021-11-05       Impact factor: 4.466

3.  Data-Independent Acquisition Protease-Multiplexing Enables Increased Proteome Sequence Coverage Across Multiple Fragmentation Modes.

Authors:  Alicia L Richards; Kuei-Ho Chen; Damien B Wilburn; Erica Stevenson; Benjamin J Polacco; Brian C Searle; Danielle L Swaney
Journal:  J Proteome Res       Date:  2022-03-02       Impact factor: 5.370

4.  Constructing Human Proteoform Families Using Intact-Mass and Top-Down Proteomics with a Multi-Protease Global Post-Translational Modification Discovery Database.

Authors:  Yunxiang Dai; Katherine E Buxton; Leah V Schaffer; Rachel M Miller; Robert J Millikin; Mark Scalf; Brian L Frey; Michael R Shortreed; Lloyd M Smith
Journal:  J Proteome Res       Date:  2019-09-18       Impact factor: 4.466

5.  Spritz: A Proteogenomic Database Engine.

Authors:  Anthony J Cesnik; Rachel M Miller; Khairina Ibrahim; Lei Lu; Robert J Millikin; Michael R Shortreed; Brian L Frey; Lloyd M Smith
Journal:  J Proteome Res       Date:  2020-10-07       Impact factor: 4.466

6.  ProteaseGuru: A Tool for Protease Selection in Bottom-Up Proteomics.

Authors:  Rachel M Miller; Khairina Ibrahim; Lloyd M Smith
Journal:  J Proteome Res       Date:  2021-03-04       Impact factor: 4.466

7.  Interlaboratory Study for Characterizing Monoclonal Antibodies by Top-Down and Middle-Down Mass Spectrometry.

Authors:  Kristina Srzentić; Luca Fornelli; Yury O Tsybin; Joseph A Loo; Henrique Seckler; Jeffrey N Agar; Lissa C Anderson; Dina L Bai; Alain Beck; Jennifer S Brodbelt; Yuri E M van der Burgt; Julia Chamot-Rooke; Sneha Chatterjee; Yunqiu Chen; David J Clarke; Paul O Danis; Jolene K Diedrich; Robert A D'Ippolito; Mathieu Dupré; Natalia Gasilova; Ying Ge; Young Ah Goo; David R Goodlett; Sylvester Greer; Kim F Haselmann; Lidong He; Christopher L Hendrickson; Joshua D Hinkle; Matthew V Holt; Sam Hughes; Donald F Hunt; Neil L Kelleher; Anton N Kozhinov; Ziqing Lin; Christian Malosse; Alan G Marshall; Laure Menin; Robert J Millikin; Konstantin O Nagornov; Simone Nicolardi; Ljiljana Paša-Tolić; Stuart Pengelley; Neil R Quebbemann; Anja Resemann; Wendy Sandoval; Richa Sarin; Nicholas D Schmitt; Jeffrey Shabanowitz; Jared B Shaw; Michael R Shortreed; Lloyd M Smith; Frank Sobott; Detlev Suckau; Timothy Toby; Chad R Weisbrod; Norelle C Wildburger; John R Yates; Sung Hwan Yoon; Nicolas L Young; Mowei Zhou
Journal:  J Am Soc Mass Spectrom       Date:  2020-08-19       Impact factor: 3.109

8.  Binary Classifier for Computing Posterior Error Probabilities in MetaMorpheus.

Authors:  Michael R Shortreed; Robert J Millikin; Lei Liu; Zach Rolfs; Rachel M Miller; Leah V Schaffer; Brian L Frey; Lloyd M Smith
Journal:  J Proteome Res       Date:  2021-03-08       Impact factor: 4.466

9.  Analysis of pancreatic extracellular matrix protein post-translational modifications via electrostatic repulsion-hydrophilic interaction chromatography coupled with mass spectrometry.

Authors:  Dylan Nicholas Tabang; Yusi Cui; Daniel M Tremmel; Megan Ford; Zihui Li; Sara Dutton Sackett; Jon S Odorico; Lingjun Li
Journal:  Mol Omics       Date:  2021-10-11

10.  Partial proteolysis improves the identification of the extracellular segments of transmembrane proteins by surface biotinylation.

Authors:  Tamás Langó; Zoltán Gergő Pataki; Lilla Turiák; András Ács; Julia Kornélia Varga; György Várady; Nóra Kucsma; László Drahos; Gábor E Tusnády
Journal:  Sci Rep       Date:  2020-06-01       Impact factor: 4.379

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