Molecular models built by researchers at the University of Pennsylvania's School of Engineering and Applied Science and its Perelman School of Medicine, the Children's Hospital of Philadelphia (CHOP), and Yale University's School of Medicine can help us understand kinase mutations' mechanism of cancerous progression.
The team compared observed mutations occurring in anaplastic lymphoma kinase with computationally generated test mutations.
Blending these real and synthetic sets into a pool with harmful and benign mutations equally distributed, the researchers used their model to anticipate which was which.
Perelman's Earl Joseph Jordan said the method overtook current methods in medical literature in forecasting the activating status of the curated mutational set.
CHOP's Yaël P. Mossé said, "We hope to integrate this methodology into an in silico platform that can allow clinicians and researchers to design treatment in the most scientifically grounded, quantitative, and patient-individualized fashion."
From Penn Engineering Today
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