Murphy, J Patrick, et al. “Improving MHC-I Ligand Identification by Incorporating Targeted Searches of Mass Spectrometry Data”. Bioinformatics for Cancer Immunotherapy, edited by Sebastian Boegel, Humana, 2020, pp. 161-7, https://doi.org/10.1007/978-1-0716-0327-7_11.

Genre

  • Book, Section
Contributors
Author: Murphy, J Patrick
Author: Prathyusha, Konda
Author: Gujar, Shashi
Date Issued
2020
Publisher
Humana
Place Published
New York, NY
Abstract

Effective immunotherapies rely on specific activation of immune cells. Class I major histocompatibility complex (MHC-I) bound peptide ligands play a major role in dictating the specificity and activation of CD8+ T cells and hence are important in developing T cell-based immunotherapies. Mass spectrometry-based approaches are most commonly used for identifying these MHC-bound peptides, wherein the MS/MS spectra are compared against a reference proteome database. Unfortunately, the effectiveness of matching the immunopeptide MS/MS spectra to a reference proteome database is hindered by inflated search spaces attributed to a lack of enzyme restriction in searches. These large search spaces limit the efficiency with which MHC-I peptides are identified. Here, we describe the implementation of a targeted database search approach and accompanying tool, SpectMHC, that is based on a priori-predicted MHC-I peptides. We have previously shown that this targeted search strategy improved peptide identifications for both mouse and human MHC ligands by greater than two-fold and is superior to traditional "no enzyme" search of reference proteomes (Murphy et al. J Res Proteome 16:1806-1816, 2017).

Language

  • English
Page range
161-171
Host Title
Bioinformatics for cancer immunotherapy
Host Contributors
Editor: Boegel, Sebastian
Series Title
Methods in molecular biology
Series Volume
2120

Department