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PTMsnp: A Web Server for the Identification of Driver Mutations That Affect Protein Post-translational Modification
Di Peng1, Huiqin Li1, Bosu Hu1
1Precision Medicine Institute, The First Affiliated Hospital, School of Life Sciences, Sun Yat-sen University, Guangzhou, China.
Frontiers in Cell and Developmental Biology
|November 26, 2020
Summary
PTMsnp identifies driver mutations impacting protein post-translational modification (PTM) sites. This tool aids in understanding disease pathogenesis and discovering novel therapeutic targets by analyzing genetic mutations alongside PTM data.
Area of Science:
- Genomics
- Proteomics
- Bioinformatics
Background:
- High-throughput sequencing reveals millions of genetic mutations in human diseases.
- Interpreting mutation pathogenesis and identifying driver genes remain significant challenges.
- Integrating protein post-translational modification (PTM) data with genetic mutations offers a promising approach.
Purpose of the Study:
- To develop PTMsnp, a web server for identifying driver genetic mutations that target PTM sites.
- To provide functional annotations for evaluating mutation impact on protein structure and function.
- To classify variants associated with Mendelian diseases and cancer.
Main Methods:
- Implemented a Bayesian hierarchical model to predict PTM-targeting mutations.
- Integrated 411,574 modification sites across 33 PTM types and 1,776,848 somatic mutations from TCGA.
- Developed interactive charts for visualizing PTM-related mutations and functional annotations.
Main Results:
- PTMsnp successfully identified candidate cancer driver genes from TCGA data.
- Analysis of a type 2 diabetes GWAS dataset revealed potential disease drivers.
- The tool demonstrated reliability in distinguishing disease-related mutations and known genes.
Conclusions:
- PTMsnp is a valuable resource for identifying driver mutations affecting PTMs.
- The findings highlight the potential of PTM-focused analysis in disease research.
- PTMsnp can aid in discovering molecular targets for therapeutic strategies.
