Proteins containing intrinsically disordered regions are integral components of the cellular signaling pathways and common components of
biological condensates. Point mutations in the
protein sequence, genetic at birth or acquired through aging, can alter the properties of the condensates, marking the onset of
neurodegenerative diseases such as ALS and
dementia. While all-atom molecular dynamics method can, in principle, elucidate the conformational changes responsible for the aging of the condensate, the applications of this method to
protein condensate systems is conditioned by the availability of molecular force fields that can accurately describe both structured and disordered regions of such
proteins. Using the special-purpose Anton 2 supercomputer, we benchmarked the efficacy of nine presently available molecular force fields in describing the structure and dynamics of a Fused in
sarcoma (
FUS) protein. Five-microsecond simulations of the full-length
FUS protein characterized the effect of the force field on the global conformation of the
protein, self-interactions among its side chains,
solvent accessible surface area and the diffusion constant. Using the results of dynamic light scattering as a benchmark for the FUS radius of gyration, we identified several force field that produced FUS conformations within the experimental range. Next, we used these force fields to perform ten-microsecond simulations of two structured RNA binding domains of FUS bound to their respective
RNA targets, finding the choice of the force field to affect stability of the
RNA-FUS complex. Taken together, our data suggest that a combination of
protein and
RNA force fields sharing a common four-point water model provides an optimal description of
proteins containing both disordered and structured regions and
RNA-
protein interactions. To make simulations of such systems available beyond the Anton 2 machines, we describe and validate implementation of the best performing force fields in a publicly available molecular dynamics program NAMD. Our NAMD implementation enables simulations of large (
tens of millions of atoms)
biological condensate systems and makes such simulations accessible to a broader scientific community.
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