Folding of Biopolymers in Crowded Environments

Accurately modeling folding and predicting native structures of biopolymers (e.g., proteins, RNAs, DNAs) are grand challenges. Theories of folding suggest that proteins explore metastable intermediate states before arriving at the global minimum energy state [1]. Furthermore, folding behavior in biological cells is profoundly influenced by macromolecular crowding. We perform molecular dynamics simulations of coarse-grained models of proteins that are predicted to follow either two-state or multi-state folding pathways and explore how conformational stability and folding pathways depend on crowder properties, e.g., sizes, concentrations, and interparticle interactions. From probability distributions of the eigenvalues of the gyration tensor in the principal axis reference frame, we determine the radius of gyration and shape of proteins in intermediate states, with and without crowding agents. The main concept is that the gyration tensor can serve as a predictor of the intermediate states of a protein and can distinguish different kinetic folding pathways. To validate our approach, we also simulate random-walk homopolymers in crowded solutions and compare against known shape distributions [2].

[1] S. W. Englander and L. Mayne, PNAS 111, 15873 (2014).

[2] W. J. Davis and A. R. Denton, J. Chem. Phys. 149, 124901 (2018).