Accurate Coarse-Grained Modeling of Dynamic RNA Structure and Phase Separation

About This Grant

Jianhan Chen of the University of Massachusetts at Amherst is supported by an award from the Chemical Theory, Models and Computational Methods program in the Division of Chemistry to to study the driving forces and molecular mechanisms of RNA phase transitions using coarse-grained models. Flexible biological macromolecules, such as disordered proteins and flexible RNAs, have recently been discovered to undergo spontaneous phase separation and form biomolecular condensates that play fundamental roles in a myriad of cellular functions from stress response to cellular signaling. Chen and his research group will develop a new intermediate resolution model for condensates of RNA (iConRNA) and collaborate with experimental labs to accurately calculate the phase diagram of RNAs and study how the interplay of various molecular forces control phase behaviors. These efforts will meet the urgent need for efficient computer models that can simulate the phase transitions of these dynamic molecules in studies of biomolecular condensates. Chen will integrate the latest advances in biomolecular condensates into courses at University of Massachusetts (UMass), train undergraduate and graduate students in interdisciplinary research, and contribute to broadening participation in STEM education and research through the Eureka! program at UMass. Jianhan Chen will develop a coarse-grained (CG) molecular modeling framework for efficient simulation of the spontaneous phase transitions of flexible RNAs as well as heterotypic protein/RNA phase separation. The proposed iConRNA model will represent each nucleotide using 6 or 7 beads, and includes explicit base stacking, base pairing, Debye–Hückel electrostatics, and Lennard-Jones potentials. Parameterized using atomistic simulations and experimental data, iConRNA model will be designed to recapitulate transient local and long-range structure features of model RNAs, fold several small RNAs, and correctly capture the length and sequence dependence of the phase separation of various RNAs. Specific research objectives of this proposal will be to: 1) accurately model the temperature and magnesium ion-dependence of RNA structure and phase separation; 2) elucidate how the complex interplay of intrinsic structural propensities and intermolecular interactions govern the mechanism of RNA phase separation and condensate material properties; and 3) develop a self-consistent CG framework for modeling heterotypic protein/RNA phase separation. Integrated with experimental studies, these efforts will uncover crucial new molecular details and mechanisms underlying the complex magnesium and temperature dependence of RNA phase transitions. In parallel, Chen will distribute the CG models through the open-source OpenMM package to enable the community to study biomolecular condensates in biology and materials engineering. The project will train several undergraduate and graduate students in the interdisciplinary field of computational biophysics and support efforts to broaden the participation of high-school students and the general public in STEM education and research. 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.