Scientists at UCLA and the University of Toronto have created moPepGen, a groundbreaking computational tool that identifies hidden genetic mutationsin proteins. This innovation holds promise for advancing cancer research and precision medicine by enhancing the detection of disease-associated protein variations.
Scientists at UCLA and the University of Toronto have developed a computational tool named moPepGen to uncover previously undetectable genetic mutations in proteins. This technological advancement opens new doors in cancer research, neurodegenerative diseases and much more.
The tool, detailed in a study published in the journal Nature Biotechnology, enables researchers to understand how DNA mutations impact proteins, providing insights into disease mechanisms and facilitating the creation of new diagnostic tests and treatments.
Proteogenomics, which integrates genomic and proteomic data to create comprehensive molecular profiles of diseases, has long been hampered by its inability to accurately detect variant peptides. Existing proteomic tools often miss the full array of protein variations essential for identifying mutations at the protein level.
“We developed moPepGen to help researchers determine which genetic variants are truly expressed at the protein level, addressing a long-standing challenge in the proteogenomic community,” co-first author Chenghao Zhu, a postdoctoral scholar in the department of human genetics at UCLA, said in a news release.
MoPepGen introduces a graph-based approach to efficiently process all types of genetic changes, significantly improving the detection of hidden protein variations, Zhu explained. This innovation offers a more comprehensive view of protein diversity and provides researchers with a precise understanding of how mutations influence diseases.
“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,” added co-senior author Paul Boutros, a professor at UCLA’s David Geffen School of Medicine.
Traditional methods primarily detect simple genetic changes such as single amino acid substitutions.
In contrast, moPepGen can identify a broad spectrum of protein variations caused by alternative splicing, circular RNAs, gene fusions, RNA editing and other complex genetic modifications. This expanded capability is crucial because proteins play essential roles in nearly all biological functions, and structural alterations can signal disease progression.
“Until now, there hasn’t been a practical way to handle the enormous complexity of genetic and transcriptomic variation,” Zhu added. “The algorithm works rapidly, even when analyzing massive amounts of data, and is designed to function across multiple technologies and species.”
The research team demonstrated moPepGen’s efficacy by analyzing proteogenomic data from five prostate tumors, eight kidney tumors and 376 cell lines.
The tool successfully identified previously undetectable protein variations associated with genetic mutations, gene fusions and other molecular changes. It outperformed older methods by detecting four times more unique protein variants.
One of the most promising applications of moPepGen is in immunotherapy. The tool can identify cancer-specific variant peptides that may serve as neoantigen candidates, which are crucial for developing personalized cancer vaccines and cell therapies.
MoPepGen is freely available for researchers and can be integrated with existing proteomics workflows, making it accessible to laboratories worldwide.
The research was a collaborative effort involving Lydia Liu and Thomas Kislinger from the University of Toronto, and other scientists whose contributions are listed in the study.
Source: UCLA Health