by A groundbreaking study conducted by researchers at the Icahn School of Medicine at Mount Sinai has introduced an innovative computational tool, LoGoFunc, which can predict pathogenic gain and loss-of-function variants across the human genome. This tool, unlike current methods that primarily focus on loss of function, distinguishes different types of harmful mutations, providing valuable insights into various disease outcomes. The study’s findings were published in Genome Medicine.
Genetic variations have the ability to alter protein function, with some mutations enhancing activity or introducing new functions (gain of function), while others diminish or eliminate function (loss of function). These changes have significant implications for human health and disease treatment.
Existing tools fall short in distinguishing between gain and loss of function, prompting the development of LoGoFunc. The researchers aimed to address this gap in the field and create a practical tool for understanding the functional consequences of genetic variations on a larger scale. Co-senior corresponding author Yuval Itan, Ph.D., Associate Professor of Genetics and Genomic Sciences, and a core member of The Charles Bronfman Institute for Personalized Medicine at Icahn Mount Sinai, explained the significance of LoGoFunc’s innovation.
LoGoFunc utilizes machine learning algorithms trained on a comprehensive database of known pathogenic gain-of-function and loss-of-function mutations found in existing literature. The tool considers an extensive range of 474 biological features, including data from protein structures predicted by AlphaFold2 and network features reflecting human protein interactions. During testing on sets from the Human Gene Mutation Database and ClinVar, LoGoFunc demonstrated high accuracy in predicting gain-of-function, loss-of-function, and neutral variants.
In addition to personalized medicine, LoGoFunc has the potential to impact drug discovery, genetic counseling, and the acceleration of genetic research. Co-senior corresponding author Avner Schlessinger, Ph.D., Professor of Pharmacological Sciences and Associate Director of the Mount Sinai Center for Therapeutics Discovery, highlighted the tool’s accessibility, promoting collaboration and providing a comprehensive view of variant impact across the human genome.
LoGoFunc offers promise in the field of precision medicine, enabling the development of more tailored treatments based on an individual’s genetic makeup.
However, the researchers caution that while these findings represent a significant advancement, further validation and integration with other medical information are necessary for translating them into clinical applications. LoGoFunc’s predictions are based on training data and certain assumptions, making ongoing validation efforts integral to ensuring reliable outcomes. As genetic data continues to grow, refining LoGoFunc’s capabilities and expanding its scope are priorities for future research.
By bridging the existing gaps, this tool enhances the understanding of genetic variations contributing to diseases, paving the way for personalized treatment strategies and drug discovery. David Stein, the Ph.D. candidate at Icahn Mount Sinai and the study’s first author, expressed hope in LoGoFunc becoming a powerful tool for deciphering the functional consequences of genetic variations. While the potential applications of LoGoFunc are vast, ongoing validation efforts will ultimately determine its real-world impact.
Note:
1. Source: Coherent Market Insights, Public sources, Desk research
2. We have leveraged AI tools to mine information and compile it
