Emerging Patterns in the Continuum Representation of Protein-Lipid Fingerprints

TL;DR

This paper introduces a deep learning approach to validate and analyze protein-lipid interaction patterns in a multiscale biological model, achieving high accuracy in identifying emergent lipid fingerprints related to proteins.

Contribution

The study presents a novel deep learning classification model that validates continuum models of protein-lipid interactions and reveals emergent lipid fingerprints with high accuracy.

Findings

Over 99.9% accuracy in classification

Validation of continuum models through deep learning

Identification of protein-specific lipid fingerprints

Abstract

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.