Computational design of novel Cas9 PAM-interacting domains using evolution-based modelling and structural quality assessment
Fig 5
Generative capacities of the SSL-RBM.
a: We tested the generative capacities of our models by generating 120 sequences with Constrained Langevin Dynamics using the trained SSL-RBM. We then use FoldX to compute the energy (displayed as ΔΔG, change in stability with respect to the wild-type) of the protein-DNA complex for the generated sequences and evaluate their quality. b: Distributions of FoldX energies of sequences generated with increasing values of γ. Distributions are drawn in gray using Gaussian Kernel Density Estimation, quantiles are also displayed for the different distributions. These quantiles and distributions show that overall, SSL-RBM trained with intermediate values of γ tend to generate sequences with better (lower) FoldX energies.