Identifying Orientation-specific Lipid-protein Fingerprints using Deep Learning

TL;DR

This paper uses deep learning to predict protein orientation states in cell membranes from molecular dynamics simulations, revealing lipid-protein interactions relevant to cancer mechanisms.

Contribution

Introduces a deep learning approach to predict protein orientations relative to lipids, providing new insights into lipid-protein interactions in cancer-related proteins.

Findings

Predicts six protein states with over 80% accuracy

Reveals lipid modulation of protein behavior

Supports design of targeted cancer therapies

Abstract

Improved understanding of the relation between the behavior of RAS and RAF proteins and the local lipid environment in the cell membrane is critical for getting insights into the mechanisms underlying cancer formation. In this work, we employ deep learning (DL) to learn this relationship by predicting protein orientational states of RAS and RAS-RAF protein complexes with respect to the lipid membrane based on the lipid densities around the protein domains from coarse-grained (CG) molecular dynamics (MD) simulations. Our DL model can predict six protein states with an overall accuracy of over 80%. The findings of this work offer new insights into how the proteins modulate the lipid environment, which in turn may assist designing novel therapies to regulate such interactions in the mechanisms associated with cancer development.