Accurate Coarse-Grained Simulations of Protein and RNA Phase Separation ft. Jianhan Chen, UMass Amherst

Physics Colloquium

Accurate Coarse-Grained Simulations of Protein and RNA Phase Separation

with Dr. Jianhan Chen
University of Massachusetts Amherst

Abstract: Biomolecular condensates are an important mechanism of subcellular compartmentalization and are closely associated with numerous cellular activities and human diseases. Formation of these membraneless organelles involves spontaneous phase transitions, driven by dynamic and multivalent biomolecules such as intrinsically disordered proteins (IDPs) and single-stranded RNAs. Efficient coarse-grained (CG) simulations have been instrumental in working hand-in-hand with theory and experiment to understand the determinant, mechanism and properties of biomolecular condensates. The widely used single-bead CG protein and RNA models, however, are severely limited in capturing the richness and complexity of the structure and interaction of dynamic protein and RNA molecules. To address these limitations, we recently developed a set of higher-resolution CG models, including the hybrid resolution (HyRes) protein model and intermediate-resolution models of condensates of RNA and DNA (iConRNA and iConDNA), to capture the interplay of various driving forces and transient structures in protein and nucleic acid phase separation. We believe that these new intermediate-resolution models represent important advances in molecular simulation of phase separation and will provide powerful tools for functional studies and engineering of biomolecular condensates.

Biography: Professor Chen’s research group focuses on the development of theoretical and computational methods and application of these methods to advance our understanding of biophysical, biochemical and biomedical problems. The lab has made significant contributions to the field of molecular modeling and simulation, including advanced conformational sampling algorithms, balanced implicit solvent protein force fields, GPU-accelerated simulation algorithms, and multi-scale protein and RNA modeling. We are also interested in physics-informed machine learning strategies to overcome data scarcity and to generate dynamic conformation ensembles. These developments have been distributed in the widely available CHARMM/OpenMM and MMTSB packages, as well as GitHub and the CCP-SAS website.

With refreshments from Panera Bread