Machine Learning Modeling Generates Accurate Insights into RAS-membrane Biology

RAS is a signaling protein associated with the cell membrane that is mutated in up to 30% of human cancers. Investigating RAS activation events is challenging in that many of the proposed mechanisms involve temporal and spatial scales currently not possible with conventional computational or experimental techniques.

That said, scientists have created an unprecedented multiscale simulation platform, referred to as Multiscale Machine-learned Modeling Infrastructure (MuMMI), to generate accurate insights into RAS-membrane biology. Given its generalized design, MuMMI opens new avenues for potential research and expands the range of questions that can be addressed using biomolecular simulation. You may find the full article, “Machine learning-driven multiscale modeling reveals lipid-dependent dynamics of RAS signaling proteins,” in the scientific journal, Proceedings of the National Academy of Sciences.

This work was supported by the Joint Design of Advanced Computing Solutions for Cancer program, a cross-agency collaboration established by NCI and the U.S. Department of Energy as part of the Cancer MoonshotSM Initiative.

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