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Abdul-Khalek N, Wimmer R, Overgaard MT, Gregersen Echers S. Insight on physicochemical properties governing peptide MS1 response in HPLC-ESI-MS/MS: A deep learning approach. Comput Struct Biotechnol J 2023; 21:3715-3727. [PMID: 37560124 PMCID: PMC10407266 DOI: 10.1016/j.csbj.2023.07.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 07/13/2023] [Accepted: 07/19/2023] [Indexed: 08/11/2023] Open
Abstract
Accurate and absolute quantification of peptides in complex mixtures using quantitative mass spectrometry (MS)-based methods requires foreground knowledge and isotopically labeled standards, thereby increasing analytical expenses, time consumption, and labor, thus limiting the number of peptides that can be accurately quantified. This originates from differential ionization efficiency between peptides and thus, understanding the physicochemical properties that influence the ionization and response in MS analysis is essential for developing less restrictive label-free quantitative methods. Here, we used equimolar peptide pool repository data to develop a deep learning model capable of identifying amino acids influencing the MS1 response. By using an encoder-decoder with an attention mechanism and correlating attention weights with amino acid physicochemical properties, we obtain insight on properties governing the peptide-level MS1 response within the datasets. While the problem cannot be described by one single set of amino acids and properties, distinct patterns were reproducibly obtained. Properties are grouped in three main categories related to peptide hydrophobicity, charge, and structural propensities. Moreover, our model can predict MS1 intensity output under defined conditions based solely on peptide sequence input. Using a refined training dataset, the model predicted log-transformed peptide MS1 intensities with an average error of 9.7 ± 0.5% based on 5-fold cross validation, and outperformed random forest and ridge regression models on both log-transformed and real scale data. This work demonstrates how deep learning can facilitate identification of physicochemical properties influencing peptide MS1 responses, but also illustrates how sequence-based response prediction and label-free peptide-level quantification may impact future workflows within quantitative proteomics.
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Affiliation(s)
- Naim Abdul-Khalek
- Department of Chemistry and Bioscience, Aalborg University, Aalborg 9220, Denmark
| | - Reinhard Wimmer
- Department of Chemistry and Bioscience, Aalborg University, Aalborg 9220, Denmark
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2
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Mallikarjun V, Richardson SM, Swift J. BayesENproteomics: Bayesian Elastic Nets for Quantification of Peptidoforms in Complex Samples. J Proteome Res 2020; 19:2167-2184. [PMID: 32319298 DOI: 10.1021/acs.jproteome.9b00468] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Multivariate regression modelling provides a statistically powerful means of quantifying the effects of a given treatment while compensating for sources of variation and noise, such as variability between human donors and the behavior of different peptides during mass spectrometry. However, methods to quantify endogenous post-translational modifications (PTMs) are typically reliant on summary statistical methods that fail to consider sources of variability such as changes in the levels of the parent protein. Here, we compare three multivariate regression methods, including a novel Bayesian elastic net algorithm (BayesENproteomics) that enables assessment of relative protein abundances while also quantifying identified PTMs for each protein. We tested the ability of these methods to accurately quantify expression of proteins in a mixed-species benchmark experiment and to quantify synthetic PTMs induced by stable isotope labelling. Finally, we extended our regression pipeline to calculate fold changes at the pathway level, providing a complement to commonly used enrichment analysis. Our results show that BayesENproteomics can quantify changes to protein levels across a broad dynamic range while also accurately quantifying PTM and pathway-level fold changes.
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Affiliation(s)
- Venkatesh Mallikarjun
- Wellcome Centre for Cell-Matrix Research, University of Manchester, Oxford Road, Manchester M13 9PT, U.K.,Division of Cell Matrix Biology and Regenerative Medicine, School of Biological Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Oxford Road, Manchester M13 9PL, U.K
| | - Stephen M Richardson
- Division of Cell Matrix Biology and Regenerative Medicine, School of Biological Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Oxford Road, Manchester M13 9PL, U.K
| | - Joe Swift
- Wellcome Centre for Cell-Matrix Research, University of Manchester, Oxford Road, Manchester M13 9PT, U.K.,Division of Cell Matrix Biology and Regenerative Medicine, School of Biological Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Oxford Road, Manchester M13 9PL, U.K
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3
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Ammar C, Gruber M, Csaba G, Zimmer R. MS-EmpiRe Utilizes Peptide-level Noise Distributions for Ultra-sensitive Detection of Differentially Expressed Proteins. Mol Cell Proteomics 2019; 18:1880-1892. [PMID: 31235637 PMCID: PMC6731086 DOI: 10.1074/mcp.ra119.001509] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2019] [Revised: 06/12/2019] [Indexed: 11/06/2022] Open
Abstract
Mass spectrometry based proteomics is the method of choice for quantifying genome-wide differential changes of protein expression in a wide range of biological and biomedical applications. Protein expression changes need to be reliably derived from many measured peptide intensities and their corresponding peptide fold changes. These peptide fold changes vary considerably for a given protein. Numerous instrumental setups aim to reduce this variability, whereas current computational methods only implicitly account for this problem. We introduce a new method, MS-EmpiRe, which explicitly accounts for the noise underlying peptide fold changes. We derive data set-specific, intensity-dependent empirical error fold change distributions, which are used for individual weighing of peptide fold changes to detect differentially expressed proteins (DEPs).In a recently published proteome-wide benchmarking data set, MS-EmpiRe doubles the number of correctly identified DEPs at an estimated FDR cutoff compared with state-of-the-art tools. We additionally confirm the superior performance of MS-EmpiRe on simulated data. MS-EmpiRe requires only peptide intensities mapped to proteins and, thus, can be applied to any common quantitative proteomics setup. We apply our method to diverse MS data sets and observe consistent increases in sensitivity with more than 1000 additional significant proteins in deep data sets, including a clinical study over multiple patients. At the same time, we observe that even the proteins classified as most insignificant by other methods but significant by MS-EmpiRe show very clear regulation on the peptide intensity level. MS-EmpiRe provides rapid processing (< 2 min for 6 LC-MS/MS runs (3 h gradients)) and is publicly available under github.com/zimmerlab/MS-EmpiRe with a manual including examples.
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Affiliation(s)
- Constantin Ammar
- ‡Ludwig-Maximilians-Universität München, Department of Informatics, Amalienstrasse 17, 80333 München, Germany; §Graduate School of Quantitative Biosciences (QBM), Ludwig-Maximillians-Universität München, Feodor-Lynen-Strasse 25, 81377 Munich, Germany
| | - Markus Gruber
- ‡Ludwig-Maximilians-Universität München, Department of Informatics, Amalienstrasse 17, 80333 München, Germany
| | - Gergely Csaba
- ‡Ludwig-Maximilians-Universität München, Department of Informatics, Amalienstrasse 17, 80333 München, Germany
| | - Ralf Zimmer
- ‡Ludwig-Maximilians-Universität München, Department of Informatics, Amalienstrasse 17, 80333 München, Germany; §Graduate School of Quantitative Biosciences (QBM), Ludwig-Maximillians-Universität München, Feodor-Lynen-Strasse 25, 81377 Munich, Germany.
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4
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Harlow K, Taylor E, Casey T, Hedrick V, Sobreira T, Aryal UK, Lemenager RP, Funnell B, Stewart K. Diet Impacts Pre-implantation Histotroph Proteomes in Beef Cattle. J Proteome Res 2018; 17:2144-2155. [PMID: 29722258 DOI: 10.1021/acs.jproteome.8b00077] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
In ruminants, the period from fertilization to implantation is relatively prolonged, and the survival of embryos depends on uterine secretions known as histotroph. Our objective was to determine if the pre-breeding diet affected histotroph proteomes in beef cattle. Cows were assigned to one of four diets: a control diet (CON), a high-protein diet (PROT), a high-fat diet (OIL), or a high-protein and high-fat diet (PROT + OIL). After 185 days on these diets, an intravaginal progesterone implant (CIDR) was inserted for 7 days. At 9 days after CIDR removal, animals with a corpus luteum were selected ( n = 16; 4 per treatment). Proteins were isolated from the histotroph collected by uterine lavage and analyzed with liquid chromatography-tandem mass spectrometry. Over 2000 proteins were expressed ( n ≥ 3 cows per treatment), with 1239 proteins being common among all of the groups. There were 20, 37, 85, and 123 proteins unique to CON, PROT + OIL, PROT, and OIL, respectively. Relative to CON, 23, 14, and 51 proteins were differentially expressed in PROT + OIL, PROT, and OIL, respectively. Functional analysis found that 53% of histotroph proteins were categorized as extracellular exosome, 3.28% as cell-cell adhesion, and 17.4% in KEGG metabolic pathways. Differences in proteomes among treatments support the idea that pre-breeding diet affects histotroph. Understanding the impact of diet on histotroph proteins may help improve conception rates.
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5
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Rosen O, Feldberg L, Dor E, Gura S, Zichel R. Optimization of SNAP-25-derived peptide substrate for improved detection of botulinum A in the Endopep-MS assay. Anal Biochem 2017; 528:34-37. [PMID: 28450105 DOI: 10.1016/j.ab.2017.04.012] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2016] [Revised: 04/20/2017] [Accepted: 04/21/2017] [Indexed: 12/24/2022]
Abstract
Botulinum neurotoxins (BoNTs) are the most toxic proteins in nature. Endopeptidase-mass-spectrometry (Endopep-MS) is used as a specific and rapid in-vitro assay to detect BoNTs. In this assay, immunocaptured toxin cleaves a serotype-specific-peptide-substrate, and the cleavage products are then detected by MS. Here we describe the design of a new peptide substrate for improved detection of BoNT type A (BoNT/A). Our strategy was based on reported BoNT/A-SNAP-25 interactions integrated with analysis method efficiency considerations. Integration of the newly designed substrate led to a 10-fold increase in the assay sensitivity both in buffer and in clinically relevant samples.
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Affiliation(s)
- Osnat Rosen
- Department of Biotechnology, Israel Institute for Biological Research, Israel
| | - Liron Feldberg
- Department of Analytical Chemistry, Israel Institute for Biological Research, Israel
| | - Eyal Dor
- Department of Biotechnology, Israel Institute for Biological Research, Israel
| | - Sigalit Gura
- Department of Analytical Chemistry, Israel Institute for Biological Research, Israel
| | - Ran Zichel
- Department of Biotechnology, Israel Institute for Biological Research, Israel.
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Nišavić M, Hozić A, Hameršak Z, Radić M, Butorac A, Duvnjak M, Cindrić M. High-Efficiency Microflow and Nanoflow Negative Electrospray Ionization of Peptides Induced by Gas-Phase Proton Transfer Reactions. Anal Chem 2017; 89:4847-4854. [DOI: 10.1021/acs.analchem.6b04466] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Marija Nišavić
- Vinča
Institute of Nuclear Sciences, University of Belgrade, Belgrade, Serbia
| | - Amela Hozić
- Ruđer Bošković
Institute, Bijenička cesta 54, Zagreb, Croatia
| | - Zdenko Hameršak
- Ruđer Bošković
Institute, Bijenička cesta 54, Zagreb, Croatia
| | - Martina Radić
- Ruđer Bošković
Institute, Bijenička cesta 54, Zagreb, Croatia
| | - Ana Butorac
- BIOCentre, Central
Lab Services, Zagreb, Croatia
| | - Marija Duvnjak
- Faculty
of Agriculture, University of Zagreb, Zagreb, Croatia
| | - Mario Cindrić
- Ruđer Bošković
Institute, Bijenička cesta 54, Zagreb, Croatia
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7
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Goeminne LJE, Gevaert K, Clement L. Experimental design and data-analysis in label-free quantitative LC/MS proteomics: A tutorial with MSqRob. J Proteomics 2017; 171:23-36. [PMID: 28391044 DOI: 10.1016/j.jprot.2017.04.004] [Citation(s) in RCA: 51] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2017] [Revised: 03/29/2017] [Accepted: 04/01/2017] [Indexed: 12/14/2022]
Abstract
Label-free shotgun proteomics is routinely used to assess proteomes. However, extracting relevant information from the massive amounts of generated data remains difficult. This tutorial provides a strong foundation on analysis of quantitative proteomics data. We provide key statistical concepts that help researchers to design proteomics experiments and we showcase how to analyze quantitative proteomics data using our recent free and open-source R package MSqRob, which was developed to implement the peptide-level robust ridge regression method for relative protein quantification described by Goeminne et al. MSqRob can handle virtually any experimental proteomics design and outputs proteins ordered by statistical significance. Moreover, its graphical user interface and interactive diagnostic plots provide easy inspection and also detection of anomalies in the data and flaws in the data analysis, allowing deeper assessment of the validity of results and a critical review of the experimental design. Our tutorial discusses interactive preprocessing, data analysis and visualization of label-free MS-based quantitative proteomics experiments with simple and more complex designs. We provide well-documented scripts to run analyses in bash mode on GitHub, enabling the integration of MSqRob in automated pipelines on cluster environments (https://github.com/statOmics/MSqRob). SIGNIFICANCE The concepts outlined in this tutorial aid in designing better experiments and analyzing the resulting data more appropriately. The two case studies using the MSqRob graphical user interface will contribute to a wider adaptation of advanced peptide-based models, resulting in higher quality data analysis workflows and more reproducible results in the proteomics community. We also provide well-documented scripts for experienced users that aim at automating MSqRob on cluster environments.
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Affiliation(s)
- Ludger J E Goeminne
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Belgium; VIB-UGent Center for Medical Biotechnology, VIB, Belgium; Department of Biochemistry, Ghent University, Belgium; Bioinformatics Institute Ghent, Ghent University, Belgium.
| | - Kris Gevaert
- VIB-UGent Center for Medical Biotechnology, VIB, Belgium; Department of Biochemistry, Ghent University, Belgium; Bioinformatics Institute Ghent, Ghent University, Belgium.
| | - Lieven Clement
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Belgium; Bioinformatics Institute Ghent, Ghent University, Belgium.
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8
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Jarnuczak AF, Lee DCH, Lawless C, Holman SW, Eyers CE, Hubbard SJ. Analysis of Intrinsic Peptide Detectability via Integrated Label-Free and SRM-Based Absolute Quantitative Proteomics. J Proteome Res 2016; 15:2945-59. [PMID: 27454336 DOI: 10.1021/acs.jproteome.6b00048] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Quantitative mass spectrometry-based proteomics of complex biological samples remains challenging in part due to the variability and charge competition arising during electrospray ionization (ESI) of peptides and the subsequent transfer and detection of ions. These issues preclude direct quantification from signal intensity alone in the absence of a standard. A deeper understanding of the governing principles of peptide ionization and exploitation of the inherent ionization and detection parameters of individual peptides is thus of great value. Here, using the yeast proteome as a model system, we establish the concept of peptide F-factor as a measure of detectability, closely related to ionization efficiency. F-factor is calculated by normalizing peptide precursor ion intensity by absolute abundance of the parent protein. We investigated F-factor characteristics in different shotgun proteomics experiments, including across multiple ESI-based LC-MS platforms. We show that F-factors mirror previously observed physicochemical predictors as peptide detectability but demonstrate a nonlinear relationship between hydrophobicity and peptide detectability. Similarly, we use F-factors to show how peptide ion coelution adversely affects detectability and ionization. We suggest that F-factors have great utility for understanding peptide detectability and gas-phase ion chemistry in complex peptide mixtures, selection of surrogate peptides in targeted MS studies, and for calibration of peptide ion signal in label-free workflows. Data are available via ProteomeXchange with identifier PXD003472.
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Affiliation(s)
- Andrew F Jarnuczak
- Faculty of Biology, Medicine and Health, University of Manchester , Michael Smith Building, Oxford Road, Manchester M13 9PT, United Kingdom
| | - Dave C H Lee
- Faculty of Biology, Medicine and Health, University of Manchester , Second Floor, Wolfson Molecular Imaging Centre, 27 Palatine Road, Withington, Manchester, M20 3JL, United Kingdom
| | - Craig Lawless
- Faculty of Biology, Medicine and Health, University of Manchester , Michael Smith Building, Oxford Road, Manchester M13 9PT, United Kingdom
| | - Stephen W Holman
- Centre for Proteome Research, University of Liverpool , Department of Biochemistry, Institute of Integrative Biology, Crown Street, Liverpool, L69 7ZB, United Kingdom
| | - Claire E Eyers
- Centre for Proteome Research, University of Liverpool , Department of Biochemistry, Institute of Integrative Biology, Crown Street, Liverpool, L69 7ZB, United Kingdom
| | - Simon J Hubbard
- Faculty of Biology, Medicine and Health, University of Manchester , Michael Smith Building, Oxford Road, Manchester M13 9PT, United Kingdom
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9
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Goeminne LJE, Gevaert K, Clement L. Peptide-level Robust Ridge Regression Improves Estimation, Sensitivity, and Specificity in Data-dependent Quantitative Label-free Shotgun Proteomics. Mol Cell Proteomics 2015; 15:657-68. [PMID: 26566788 DOI: 10.1074/mcp.m115.055897] [Citation(s) in RCA: 58] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2015] [Indexed: 01/22/2023] Open
Abstract
Peptide intensities from mass spectra are increasingly used for relative quantitation of proteins in complex samples. However, numerous issues inherent to the mass spectrometry workflow turn quantitative proteomic data analysis into a crucial challenge. We and others have shown that modeling at the peptide level outperforms classical summarization-based approaches, which typically also discard a lot of proteins at the data preprocessing step. Peptide-based linear regression models, however, still suffer from unbalanced datasets due to missing peptide intensities, outlying peptide intensities and overfitting. Here, we further improve upon peptide-based models by three modular extensions: ridge regression, improved variance estimation by borrowing information across proteins with empirical Bayes and M-estimation with Huber weights. We illustrate our method on the CPTAC spike-in study and on a study comparing wild-type and ArgP knock-out Francisella tularensis proteomes. We show that the fold change estimates of our robust approach are more precise and more accurate than those from state-of-the-art summarization-based methods and peptide-based regression models, which leads to an improved sensitivity and specificity. We also demonstrate that ionization competition effects come already into play at very low spike-in concentrations and confirm that analyses with peptide-based regression methods on peptide intensity values aggregated by charge state and modification status (e.g. MaxQuant's peptides.txt file) are slightly superior to analyses on raw peptide intensity values (e.g. MaxQuant's evidence.txt file).
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Affiliation(s)
- Ludger J E Goeminne
- From the ‡Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Belgium; §VIB Medical Biotechnology Center, Ghent University, Belgium; ¶Department of Biochemistry, Ghent University, Belgium
| | - Kris Gevaert
- §VIB Medical Biotechnology Center, Ghent University, Belgium; ¶Department of Biochemistry, Ghent University, Belgium
| | - Lieven Clement
- From the ‡Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Belgium;
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Goeminne LJE, Argentini A, Martens L, Clement L. Summarization vs Peptide-Based Models in Label-Free Quantitative Proteomics: Performance, Pitfalls, and Data Analysis Guidelines. J Proteome Res 2015; 14:2457-65. [PMID: 25827922 DOI: 10.1021/pr501223t] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Quantitative label-free mass spectrometry is increasingly used to analyze the proteomes of complex biological samples. However, the choice of appropriate data analysis methods remains a major challenge. We therefore provide a rigorous comparison between peptide-based models and peptide-summarization-based pipelines. We show that peptide-based models outperform summarization-based pipelines in terms of sensitivity, specificity, accuracy, and precision. We also demonstrate that the predefined FDR cutoffs for the detection of differentially regulated proteins can become problematic when differentially expressed (DE) proteins are highly abundant in one or more samples. Care should therefore be taken when data are interpreted from samples with spiked-in internal controls and from samples that contain a few very highly abundant proteins. We do, however, show that specific diagnostic plots can be used for assessing differentially expressed proteins and the overall quality of the obtained fold change estimates. Finally, our study also illustrates that imputation under the "missing by low abundance" assumption is beneficial for the detection of differential expression in proteins with low abundance, but it negatively affects moderately to highly abundant proteins. Hence, imputation strategies that are commonly implemented in standard proteomics software should be used with care.
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Affiliation(s)
- Ludger J E Goeminne
- ∥Department of Plant Systems Biology, VIB, Ghent University, 9052 Ghent, Belgium
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Rosen O, Feldberg L, Gura S, Zichel R. Improved detection of botulinum type E by rational design of a new peptide substrate for endopeptidase-mass spectrometry assay. Anal Biochem 2014; 456:50-2. [PMID: 24721293 DOI: 10.1016/j.ab.2014.03.024] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2014] [Revised: 03/26/2014] [Accepted: 03/29/2014] [Indexed: 12/01/2022]
Abstract
Botulinum neurotoxins (BoNTs) are the most toxic substances known to humans. Endopeptidase-mass spectrometry (Endopep-MS) is used as a specific and rapid in vitro assay to detect BoNTs. In this assay, immunocaptured toxin cleaves a serotype-specific peptide substrate, and the cleavage products are then detected by MS. To further improve the sensitivity of the assay, we report here the rational design of a new substrate peptide for the detection of botulinum neurotoxin type E (BoNT/E). Our strategy was based on previously reported structural interactions integrated with analysis method efficiency considerations. Integration of the newly designed substrate has led to a more than one order of magnitude increased sensitivity of the assay.
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Affiliation(s)
- Osnat Rosen
- Department of Biotechnology, Israel Institute for Biological Research, Ness Ziona 7410001, Israel
| | - Liron Feldberg
- Department of Analytical Chemistry, Israel Institute for Biological Research, Ness Ziona 7410001, Israel
| | - Sigalit Gura
- Department of Analytical Chemistry, Israel Institute for Biological Research, Ness Ziona 7410001, Israel
| | - Ran Zichel
- Department of Biotechnology, Israel Institute for Biological Research, Ness Ziona 7410001, Israel.
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