ViDa: Visualizing DNA hybridization trajectories with biophysics-informed deep graph embeddings
This work addresses a domain-specific problem for synthetic biologists and molecular programmers by providing enhanced visualization of DNA reaction mechanisms, though it is incremental as it builds on existing methods with domain-specific augmentations.
The authors tackled the problem of visualizing complex DNA hybridization reaction trajectories by developing ViDa, a biophysics-informed deep graph embedding method that integrates scattering transforms, variational autoencoders, and nonlinear dimensionality reduction, resulting in significant quality improvements over state-of-the-art visualization tools by successfully separating different folding pathways.
Visualization tools can help synthetic biologists and molecular programmers understand the complex reactive pathways of nucleic acid reactions, which can be designed for many potential applications and can be modelled using a continuous-time Markov chain (CTMC). Here we present ViDa, a new visualization approach for DNA reaction trajectories that uses a 2D embedding of the secondary structure state space underlying the CTMC model. To this end, we integrate a scattering transform of the secondary structure adjacency, a variational autoencoder, and a nonlinear dimensionality reduction method. We augment the training loss with domain-specific supervised terms that capture both thermodynamic and kinetic features. We assess ViDa on two well-studied DNA hybridization reactions. Our results demonstrate that the domain-specific features lead to significant quality improvements over the state-of-the-art in DNA state space visualization, successfully separating different folding pathways and thus providing useful insights into dominant reaction mechanisms.