Session: COMP Poster Session:

Membraneless organelles play crucial roles in a wide range of biological processes and are frequently formed by the liquid-liquid phase separation of intrinsically disordered proteins. Characterizing the physical properties of such protein condensates would provide crucial insights into cellular functions, but the structural heterogeneity of IDPs makes their interactions difficult to examine experimentally. Computational modeling serves as an attractive compliment to experimental studies, and can help elucidate the set of molecular interactions that dictate condensate stability. However, the presence of both ordered and disordered domains in many of these proteins places an increased importance on techniques transferable to both ordered and disordered regions. Here, we present an algorithm to parameterize coarse-grained protein force fields. The maximum entropy optimized force field (MOFF) combines maximum entropy biasing, least-squares fitting, and basic principles of energy landscape theory to faithfully reproduce experimental radius of gyration data, while simultaneously ensuring the folded state of globular proteins are lower in energy than other possible conformations. To demonstrate the utility of such a model, we applied it to two homologs of heterochromatin protein 1 (HP1), an essential protein for the spatial organization of chromatin. In agreement with experimental data, MOFF identified structural differences between the two homologs, despite their high sequence similarity. Then, we applied MOFF to simulate hundreds of HP1 proteins and determined the critical temperature of HP1 phase separation. Our simulations reveal that homolog dependent multivalent interactions dictate the stability of the protein condensates. In all, our work makes significant methodological strides to provide a uniform model for studying ordered and disordered proteins, and provides an example of how such a model can study liquid-liquid phase separation at near-atomistic resolution.

Bin Zhang

Assistant Professor, Massachusetts Institute of Technology

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