Coisolation of Peptide Pairs for Peptide Identification and MS/MS-Based Quantification

Abstract

Stable-isotope labeling with amino acids in cell culture (SILAC)-based metabolic labeling is a widely adopted proteomics approach that enables quantitative comparisons among a variety of experimental conditions. Despite its quantitative capacity, SILAC experiments analyzed with data-dependent acquisition (DDA) do not fully leverage peptide pair information for identification and suffer from undersampling compared to label-free proteomic experiments. Herein, we developed a DDA strategy that coisolates and fragments SILAC peptide pairs and uses y-ions for their relative quantification. To facilitate the analysis of this type of data, we adapted the Comet sequence database search engine to make use of SILAC peptide paired fragments and developed a tool to annotate and quantify MS/ MS spectra of coisolated SILAC pairs. This peptide pair coisolation approach generally improved expectation scores compared to the traditional DDA approach. Fragment ion quantification performed similarly well to precursor quantification in the MS1 and achieved more quantifications. Lastly, our method enables reliable MS/MS quantification of SILAC proteome mixtures with overlapping isotopic distributions. This study shows the feasibility of the coisolation approach. Coupling this approach with intelligent acquisition strategies has the potential to improve SILAC peptide sampling and quantification.

Characterizations and molecular docking mechanism of the interactions between peptide FDGDF (Phe-Asp-Gly-Asp-Phe) and SOD enzyme

“…BPH was extracted from dry corned beef, which has 5 % salt during the curing process. The presence of buffer salt will cause damage to the column [ 29 ]: (1) column pressure increase; (2) column efficiency decrease; (3) the retention time of the same compound changes, taking an appropriate amount of sample and using a C18 desalting column to remove salt [ 30 ]. After peptide separation, salt and buffer were removed using the C18 column, which is the ideal substrate for trapping hydrophobic peptides.…”

Unified and Standardized Mass Spectrometry Data Processing in Python Using spectrum_utils

“…This dovetails with visualization functionality to produce publication-quality and interactive spectrum graphics. Since its introduction, spectrum_utils has been used in tools that perform spectral library searching and spectrum clustering, to preprocess MS/MS spectra prior to deep learning applications, , to plot MS/MS spectra from online data repositories, and to assist in MS/MS processing and visualization efforts for dozens of other projects. − …”