We simulated the termination and anti-termination properties of our signal-terminator constructs with the Kinefold web server and used some standard estimations for diffusive terms. Our main goal was to prove that our constructs work and that termination is stopped efficiently, that is that the signal molecule binds and anti-terminations occurs before the RNA polymerase falls off.
The Kinefold webserver provides a web interface for stochastic folding simulations of nucleic acids and offers the choice of renaturation or co-transcriptional folding. The folding paths are simulated at the level of helix formation and dissociation as these stochastic formation and the removal of individual helices are known to be the limiting steps of RNA folding kinetics.
For our purposes co-transcriptional folding was the appropriate choice: Folding proceeds while the sequence is being synthesized from its 5' to 3' ends at a speed of 3ms per newly added base (for RNA polymerase T7 phage). Thus, the transcript starts to fold before the whole sequence is fully available.
Kinefold offers the possibility to include additional bases (X) which do not pair to model hybridization dynamics between two sequences. In order to simulate how the binding of the signal molecule prevents termination we linked the signal via a linker sequence consisting of 'X' bases to the sequence of the terminator.
For each signal-terminator pair we did batch simulations with various random seeds in order to guarantee accuracy.
We also varied signal length form two base pairs to full signal length which provides insight in how long the signal needs to be in order to bind to the terminator and how long this process takes at least.

The question whether anti-termination occurs is not only guided by the folding process of the signal-terminator pair, but also by how long the signal takes to diffuse to the terminator sequence. To account for the diffusion time, we estimated the hit rate τ (following 6.), which is the time until the signal meets the terminator sequence for the first time:

τ = 1/(3D*a/r³)

where D is the diffusion constant, a the radius of gyration of the signal molecule and r the radius of the cell.
For E.coli r is 1 μm. The radius of gyration acan be estimated using the worm-like-chain model by

a = (n*l)/3, <

where n is the length of the signal which is 0,3 nm/monomer, l is the persistency length which is following (5.) 2nm for single-stranded RNA. Thus, for a signal of length 32 nt, a = 6,4 nm.

The diffusion constant D was obtained by

D = k_B T/

to be 0,1518s. We first estimated the diffusion constant to be 3,4318 m²/s via the formula $D = farc{k_BT}{gamma}$, where $k_B$ is the constant, T is the temperature and $gamma$ equals $6 pi * 10*10^-3*a$ and a is the radius of gyration which is obtained by a formula following (5.): length of the sequence multiplied by the persistency length (2nm for single-stranded RNA) divided by three (0,3nm/Monomer) . Thus, with the worm-like chain model one gets that the gyration radius is 6,4 nm. <tau>= farc{1}{3D a/r^3}*number of molecules. As the folding time is one order of magnitude larger than the diffusion and thus much less relevant for modeling our signal-terminator constructs, we didn't employ more elaborate techniques to model diffusion.