Literature DB >> 22095509

Peptide polarity and the position of arginine as sources of selectivity during positive electrospray ionisation mass spectrometry.

Daniel A Abaye1, Frank S Pullen, Birthe V Nielsen.   

Abstract

Electrospray ionisation (ESI) is a selective process and, for similar sized analytes, the intrinsic properties of the molecules affect the ionisation process and their response. This research sets out to determine the effect of some of these properties in peptides: peptide polarity and the presence of arginine at positions 1 and 4 in the amino acid sequence on the ESI response. Six peptides; molecular mass ranges 1.3-1.6 kDa; substance P (SP) and glutamate fibrinopeptide (GFP) and 3.2-3.7 kDa; calcitonin gene-related peptide (CGRP), vasoactive intestinal peptide (VIP), glucagon-like peptide 1 (GLP1) and defensin human neutropeptide 2 (DHNP2), were investigated. We have demonstrated that in positive ESI, for similar sized peptides and the same charge state, the responsiveness is in the order: Peptides with N or C terminal arginine > most non-polar peptides > least non-polar peptides. Therefore, arginine at the terminal position is a source of selectivity. Data from matrix-assisted laser desorption ionisation (MALDI) analysis supports that of the ESI experiments: Peptides with a terminal arginine residue generated higher signal intensities. Our observations extend our understanding of the ESI process and provide a rational approach to optimising sensitivity of electrospray conditions where a narrow mass range of peptides are poorly chromatographically resolved. This information will provide for a more effective method development process, especially during label-free quantitative determination of peptides extracted in solution.
Copyright © 2011 John Wiley & Sons, Ltd.

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Year:  2011        PMID: 22095509     DOI: 10.1002/rcm.5270

Source DB:  PubMed          Journal:  Rapid Commun Mass Spectrom        ISSN: 0951-4198            Impact factor:   2.419


  2 in total

1.  Peptide-level Robust Ridge Regression Improves Estimation, Sensitivity, and Specificity in Data-dependent Quantitative Label-free Shotgun Proteomics.

Authors:  Ludger J E Goeminne; Kris Gevaert; Lieven Clement
Journal:  Mol Cell Proteomics       Date:  2015-11-13       Impact factor: 5.911

2.  MS-EmpiRe Utilizes Peptide-level Noise Distributions for Ultra-sensitive Detection of Differentially Expressed Proteins.

Authors:  Constantin Ammar; Markus Gruber; Gergely Csaba; Ralf Zimmer
Journal:  Mol Cell Proteomics       Date:  2019-06-24       Impact factor: 5.911

  2 in total

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