2022/07/09 by Konstantia Georgouli, Georgouli, Konstantia, Helgi I. Ingólfsson +11
Biochemistry, Genetics and Molecular Biology · Computer Science · #Cell Image Analysis Techniques #Computational Drug Discovery Methods #FOS: Biological sciences #FOS: Computer and information sciences #Machine Learning (cs.LG) #Protein Structure and Dynamics #Quantitative Methods (q-bio.QM)
paper · pdf · doi:10.48550/arxiv.2207.04333
openalex publication_date 2022/07/09 · openalex created_date 2022/07/13 · openalex updated_date 2026/07/28
Capturing intricate biological phenomena often requires multiscale modeling where coarse and inexpensive models are developed using limited components of expensive and high-fidelity models. Here, we consider such a multiscale framework in the context of cancer biology and address the challenge of evaluating the descriptive capabilities of a continuum model developed using 1-dimensional statistics from a molecular dynamics model. Using deep learning, we develop a highly predictive classification model that identifies complex and emergent behavior from the continuum model. With over 99.9% accuracy demonstrated for two simulations, our approach confirms the existence of protein-specific "lipid fingerprints", i.e. spatial rearrangements of lipids in response to proteins of interest. Through this demonstration, our model also provides external validation of the continuum model, affirms the value of such multiscale modeling, and can foster new insights through further analysis of these fingerprints.