Multi-scale Modeling of Macromolecular Liquids, and Macromolecules in Solution

University Of Oregon Eugene, Eugene OR

Investigators

Abstract

With support from the Chemical Theory, Models and Computational Methods (CTMC) program in the Division of Chemistry, Marina Guenza of the University of Oregon will design multiscale modeling simulations for macromolecular systems that combine advanced coarse-grained models with atomistic simulations. The Guenza group will develop coarse-grained models of macromolecular liquids and macromolecules in solution. Synthetic macromolecular liquids will play an essential role in the transition from fossil carbon-based to renewable energy sources. An emerging and impelling societal goal is to design eco-friendly polymeric materials that maintain specific, desirable macroscopic properties. Biological macromolecules, i.e. proteins and nucleic acids are the building blocks of super-molecular machines that guide life processes. Modeling biomacromolecular behavior is expected to provide invaluable information and potential provide insight on biomacromolecular dysfunction and its relation to disease. By adopting multiscale modeling procedures, Guenza and coworkers will study how the global macromolecular properties depend on their local molecular structure. These studies cannot be completed by atomistic simulations alone. In-house computer codes will be shared with the scientific community through GitHub and dedicated websites. As a part of her broader impact, Guenza will further expand the student-led peer mediator program called DuckREFS that she has initiated in the Chemistry Department at the University of Oregon. This program trains graduate students to act as neutral, third-party resources for fellow peers in alleviating stress and resolving conflict. The students learn active listening, conflict negotiation, and dispute resolution. The first part of this project will develop the Integral Equation Theory of Coarse-Graining (IECG) by studying enthalpy, entropy, free energy, and isothermal compressibility, predicted by IECG across multiple resolutions. The implementation of temperature transferability of the IECG potential, and the extension of IECG to fine resolution using machine learning will enhance the applicability of the method. Atomistic and IECG simulation will be combined in an Adaptive Resolution Simulation (AdResS). The proposed work involves the scientific collaboration with Prof. Luigi delle Site at the Freie Universität in Berlin who spearheaded AdResS. The second part of this research project aims to further extend the Langevin Equation for Protein Dynamics (LE4PD). LE4PD is an efficient and accurate method to describe protein dynamics because it accounts for local conformational barriers, as well as the hydrophobic core, and hydrodynamic interactions. The proposed research will extend LE4PD by including new terms that couple protein fluctuations with rotation, translation, and viscous forces. The calculated coupling terms should facilitate the application of these methods to protein binding and to the formation of protein-DNA complexes. 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.