Hybrid Computational Models for Membrane-Protein Interfaces

Trustees Of Boston University, Boston

Investigators

Abstract

WIth support from the Chemical Theory, Models and Computational Methods program in the Division of Chemistry, Professor Qiang Cui of Boston University is developing effective computational methods for tackling problems that involve collective behaviors of proteins/peptides at the lipid membrane interface. These problems are difficult to study using existing computational methodologies due to the involvement of multiple length and time scales. Taking advantage of recent progress in machine learning (ML) techniques, Dr. Cui will aim to overcome these challenges to establish efficient and reliable computational models that enable the mechanistic analysis of protein phase separation at cell membrane surface and protein mediated membrane porations, which are critical in important biological processes such as cell signaling, viral infection and synaptic transmission. Cui will also engage in various education and out-reach activities to inspire students of broad backgrounds to pursue a career at the boundary between physical chemistry, computational science, and biology. At the undergraduate level, Professor Cui will endeavor to enhance the integration of computation and basic programming concepts into the chemistry curriculum at Boston University. To accomplish the research goals, the Cui team will effectively integrate recent advances in simulation methodologies in unique biophysical contexts. In one problem, Cui and co-workers will aim to understand how protein-membrane interactions modify the conformational and interaction properties of proteins in the context of liquid-liquid phase separation. The unique angle will be to develop a hybrid ML/MM model in which the protein and its interaction with the membrane environment are described using ML, trained with atomistic simulation data and a reference coarse-grained model; the advantage of this hybrid model is that many-body effects at the coarse-grained level are captured, a feature expected to be essential to the proper description of collective behaviors of proteins in different environments, including phase separation at or wetting of the lipid membrane. In another problem, the challenge is to understand the mechanism by which multiple types of peptides or protein motifs regulate membrane pores. The Cui group will combine finite temperature string and an ML approach to expand the list of collective variables automatically and systematically for evaluating the underlying minimum free energy pathways. The fundamental strategy of combining the strengths of global (string) and local (PIB) enhanced sampling techniques will be potentially applicable to a broad range of problems in which a minimal set of global progress variables is known ahead of time, yet important local degrees of freedom remain obscure. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.