Topologically steered simulations and the role of geometric constraints in protein knotting

Simulations of knotting and unknotting in polymers or other filaments rely on random processes to facilitate topological changes. Here we introduce a method of "topological steering" to determine the optimal pathway by which a filament may knot or unknot while subject to a given set of physics. The method involves measuring the knotoid spectrum of a space curve projected onto many surfaces and computing the mean unravelling number of those projections. Several perturbations of a curve can be generated stochastically, e.g. using the Langevin equation or crankshaft moves, and a gradient can be followed that maximizes or minimizes the topological complexity. We apply this method to a polymer model based on a growing self-avoiding tangent-sphere chain, which can be made to model proteins by imposing a constraint that the bending and twisting angles between successive spheres must maintain the distribution found in naturally occurring protein structures. We show that without these protein-like geometric constraints, topologically optimized polymers typically form alternating torus knots and composites thereof, similar to the stochastic knots predicted for long DNA. However, when the geometric constraints are imposed on the system, the frequency of twist knots increases, similar to the observed abundance of twist knots in protein structures.