.png){kind=icon}
.png){kind=icon}
Return
Enabling Variant Proteogenomic Assay Development Through a Peptide Target Selection Pipeline and Web Application
.png){kind=icon}
OA
AI
C
P
V
Y
C
DOI：10.1002/pmic.70139.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The quantification of key protein variants can guide precision oncology to enable better identification of driver mutations. Bottom-up assays infer parent protein levels through the quantification of surrogate peptides. Assay methods designed for absolute quantification of protein variants, such as multiple reaction monitoring mass spectrometry (MRM-MS), can achieve quantitation with high precision and specificity. Bottom-up approaches rely on proper selection of suitable surrogate proteogenotypic peptide targets to quantify protein variants of interest. To this end, we developed an R-based bioinformatics pipeline to predict and evaluate variant-specific peptides. The workflow generates variant protein sequences from wild-type sequences and mutations encoded in the Human Genome Variation Society (HGVS) recommended nomenclature, performs in-silico tryptic digestion, and identifies both variant and corresponding wild-type peptides. Each peptide is evaluated by 37 selection criteria to determine suitability as MRM targets. To assess the strictness of these criteria, we applied all protein-altering mutations from the NCI-Genomic Data Commons (GDC) and COSMIC to our pipeline. Of the peptides outputted, 5% satisfied all defined criteria, representing the highest confidence candidates for assay development. We developed a database and web application from NCI-GDC generated peptides for searching, filtering, and downloading. Another web application was developed to provide access to our pipeline.
Keywords:
bioinformatics tool
mass spectrometry
proteogenomics
variant peptide database
variant peptide prediction
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.9
Papers:
7.6K
Citations:
1.1W
Organization
Cited Papers