Advanced Computational Tool Paves Way for Diagnostic Tests to Detect Hidden Genetic Mutations

Identifying genetic mutations at the protein level has long been a barrier in the field of proteogenomics, limiting scientists’ ability to fully understand disease mechanisms such as cancer or neurodegeneration. Traditional proteomic tools often miss the diversity of protein variations, particularly complex changes, making it difficult to detect disease-related alterations. To address this gap, researchers have developed an advanced computational tool that enhances the identification of variant peptides, offering a more precise and comprehensive view of protein diversity.

Developed by scientists at UCLA (Los Angeles, CA, USA) and the University of Toronto (Toronto, ON, Canada), the tool, named moPepGen, is designed to detect previously invisible protein changes associated with genetic mutations. The tool, introduced in the journal Nature Biotechnology, aims to deepen understanding of how genetic changes impact protein structure and function across various diseases, including cancer and neurodegenerative disorders. moPepGen improves detection by using a graph-based computational framework capable of processing a wide range of genetic changes. Unlike existing tools that primarily detect basic mutations such as single amino acid substitutions, moPepGen can identify complex variations resulting from alternative splicing, circular RNAs, gene fusions, and RNA editing. By systematically modelling gene expression and protein translation, it expands the capacity to uncover hidden protein variants, significantly increasing analytical depth and accuracy.

The algorithm works rapidly, even when analyzing massive amounts of data, and is designed to function across multiple technologies and species. The researchers validated moPepGen using proteogenomic datasets from five prostate tumors, eight kidney tumors, and 376 cell lines. The tool successfully identified protein variants linked to genetic mutations and molecular alterations that previous methods failed to detect. It demonstrated four times greater sensitivity than older approaches, uncovering a higher number of unique variant peptides. These findings highlight moPepGen’s potential to improve the detection of disease-associated mutations and its value in translational research. One of the most promising applications of moPepGen is in immunotherapy. By identifying cancer-specific variant peptides, the tool can help pinpoint neoantigen candidates, which are essential for creating personalized cancer vaccines and cell-based therapies. moPepGen is freely available and designed to integrate into existing proteomics workflows, making it a scalable and accessible solution for research labs worldwide.

“By making it easier to analyze complex protein variations, moPepGen has the potential to advance research in cancer, neurodegenerative diseases, and other fields where understanding protein diversity is critical,” said Paul Boutros, PhD, co-senior author of the study. “It bridges the gap between genetic data and real-world protein expression, unlocking new possibilities in precision medicine and beyond.”