Predicting protein-membrane interfaces using molecular simulations and ensemble machine learning.

Abstract

Abnormal protein-membrane attachment is involved in deregulated cellular pathways and in disease. The possibility to modulate protein-membrane interactions represents a new promising therapeutic strategy for membrane proteins. In this talk, we explore the free energy landscape of membrane protein dimerization using parallel tempering metadynamics simulations in the well-tempered ensemble and coarse-grained force fields and reproduce the structure and energetics of the dimerization process of membrane proteins and proteins in an aqueous solution in reasonable accuracy and throughput. We propose that the use of enhanced sampling simulations with a refined coarse-grained force field and appropriately defined collective variables is a robust approach for studying the protein dimerization process, although one should be cautious of the energy minima ranking. Moreover, we study oncogenes, including the H1047R and E545K hotspot mutants of PI3Kα, and KRAS-4B to understand the basis of protein overactivation. We calculate their allosteric pathways and show residues important in delivering communication signals between functional domains of each protein. Finally, we describe an ensemble machine learning methodology to predict protein-membrane interfaces of peripheral membrane proteins and present a drug design pipeline for drugging protein-membrane interfaces using the DREAMM web-server https://dreamm.ni4os.eu. Taking into account these results, we investigate opportunities for allosteric drug design.