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  • New Computational Tool Predicts Variations with Impressive Accuracy

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    LoGoFunc identifies harmful (left) and harmless (right) genetic variations in the Vasopressin V2 receptor protein using a structure predicted by AlphaFold2. This helps explain how genetic changes affect proteins. (Image credit: Stein et al., Genome Medicine)


    Innovative Computational Approach in Genetics
    Researchers from the Icahn School of Medicine at Mount Sinai have developed LoGoFunc, a sophisticated computational tool designed to predict pathogenic gain- and loss-of-function variants across the genome. This innovative tool marks significant progress in genetic research, addressing a critical gap in the field by distinguishing between different types of harmful mutations. The study detailing these findings was published in the November 30 online issue of Genome Medicine.

    A New Tool in Understanding Genetic Variations
    Genetic variations can lead to altered protein functions, which in turn can significantly affect human health and disease treatment. Existing tools struggle to differentiate between gain and loss of function in these variations. LoGoFunc, however, excels in this area, offering insights into the varied impacts these mutations can have on protein activity and, consequently, disease outcomes.

    “Tools presently available fall short in differentiating between gain and loss of function, which motivated us to develop LoGoFunc. This matters because these variants impact protein activity differently, influencing disease outcomes. We created an innovative tool that addresses a critical gap in the field, providing a practical way to understand the functional consequences of genetic variations on a larger scale,” stated Yuval Itan, Ph.D., Associate Professor of Genetics and Genomic Sciences at Icahn Mount Sinai and co-senior corresponding author of the study.

    Technological Backbone of LoGoFunc
    LoGoFunc employs machine learning, trained on a database of known pathogenic gain-of-function and loss-of-function mutations from literature. The tool considers an extensive array of 474 biological features, including data from protein structures predicted by AlphaFold2 and network features reflecting human protein interactions. Tested on sets from the Human Gene Mutation Database and ClinVar, LoGoFunc has demonstrated high accuracy in predicting various types of genetic variants.

    Implications and Future Prospects
    “Beyond personalized medicine, LoGoFunc has implications for drug discovery, genetic counseling, and accelerating genetic research. Its accessibility promotes collaboration and offers a comprehensive view of variant impact across the human genome,” explained co-senior corresponding author Avner Schlessinger, Ph.D.

    Despite these advancements, the researchers caution that further validation and integration with other medical information are essential for clinical application. The tool's predictions, though promising, are based on existing training data and inherent assumptions. Therefore, ongoing validation and refinements are critical for ensuring its reliability.

    David Stein, a Ph.D. candidate at Icahn Mount Sinai and the study's first author, underscored the tool's potential to enhance our understanding of genetic variations and their contribution to diseases. However, he also emphasized the importance of ongoing efforts to validate LoGoFunc's predictions for real-world impact.

    Access and Future Research
    LoGoFunc's predictions for missense variants across the entire genome are accessible for non-commercial use and analysis at the Icahn Mount Sinai's dedicated portal. The researchers are committed to refining LoGoFunc's capabilities and extending its scope in future research endeavors.

    Read more from the original publication at:
    Stein, D., Kars, M.E., Wu, Y. et al. Genome-wide prediction of pathogenic gain- and loss-of-function variants from ensemble learning of a diverse feature set. Genome Med 15, 103 (2023). https://doi.org/10.1186/s13073-023-01261-9

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