Issue 12, 2017

Evaluation and optimization of reduction and alkylation methods to maximize peptide identification with MS-based proteomics

Abstract

Mass spectrometry (MS) has become an increasingly important technique to analyze proteins. In popular bottom-up MS-based proteomics, reduction and alkylation are routine steps to facilitate peptide identification. However, incomplete reactions and side reactions may occur, which compromise the experimental results. In this work, we systematically evaluated the reduction step with commonly used reagents, i.e., dithiothreitol, 2-mercaptoethanol, tris(2-carboxyethyl)phosphine, or tris(3-hydroxypropyl)phosphine, and alkylation with iodoacetamide, acrylamide, N-ethylmaleimide, or 4-vinylpyridine. By using digested peptides from a yeast whole-cell lysate, the number of proteins and peptides identified were very similar using four different reducing reagents. The results from four alkylating reagents, however, were dramatically different with iodoacetamide giving the highest number of peptides with alkylated cysteine and the lowest number of peptides with incomplete cysteine alkylation and side reactions. Alkylation conditions with iodoacetamide were further optimized. To identify more peptides with cysteine, thiopropyl-sepharose 6B resins were used to enrich them, and the optimal conditions were employed for the reduction and alkylation. The enrichment resulted in over three times more cysteine-containing peptides than without enrichment. Systematic evaluation of the reduction and alkylation with different reagents can aid in a better design of bottom-up proteomic experiments.

Graphical abstract: Evaluation and optimization of reduction and alkylation methods to maximize peptide identification with MS-based proteomics

Supplementary files

Article information

Article type
Paper
Submitted
04 Jul 2017
Accepted
15 Sep 2017
First published
20 Sep 2017

Mol. BioSyst., 2017,13, 2574-2582

Evaluation and optimization of reduction and alkylation methods to maximize peptide identification with MS-based proteomics

S. Suttapitugsakul, H. Xiao, J. Smeekens and R. Wu, Mol. BioSyst., 2017, 13, 2574 DOI: 10.1039/C7MB00393E

To request permission to reproduce material from this article, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Spotlight

Advertisements