Scientist Stories: Chenwei Zhang, Visualizing DNA hybridization trajectories with biophysics @axialxyz

Uploaded July 2024 | Updated September 2026, 2 weeks ago

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.

arxiv.org/abs/2311.03411

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Scientist Stories: Chenwei Zhang, Visualizing DNA hybridization trajectories with biophysics

$Scientist Stories: Joe Marsh, Deep Mutational Scanning Most known Mendelian genetic disorders are caused by changes in protein-coding regions of DNA, yet clinically relevant variants account for only a tiny fraction of those seen in humans. We are interested in understanding the molecular mechanisms by which protein variants can cause disease. While past work has often focused on how sequence changes can cause a loss of protein function, we are especially interested in protein mutations that cause disease via gain-of-function or dominant-negative effects. We believe that through better understanding of the molecular mechanisms, we can improve our ability to predict which variants of uncertain significance are most likely to be pathogenic. Moreover, understanding molecular mechanisms can open the door to future treatment possibilities. To address this, we use three complementary strategies. Structural bioinformatics can provide great insight into the molecular mechanisms underlying disease mutations, but has historically been less useful for identifying deleterious mutations. In contrast, computational variant effect predictors are very good at identifying pathogenic mutations in certain genes, but tell us nothing about why they are damaging. Finally, deep mutational scanning (DMS) experiments, performed in collaboration with the Kudla lab, enable direct high-throughput measurement of variant effects, and are proving tremendously valuable for identifying disease mutations and explaining molecular mechanisms. We also have a strong interest in protein complexes. The emergence of new experimental and computational techniques, along with the increasing availability of diverse structural, proteomic and genomic datasets, have created huge potential for investigating protein complex structure and assembly on a large scale. Consideration of protein quaternary structure is often tremendously useful for understanding the molecular mechanisms underlying disease mutations. We are also interested in the biology of protein complex assembly, seeking to understand how assembly occurs within cells, how it is regulated, how it contributes to normal biological function, and how it has evolved. https://scholar.google.com/citations?user=jdDpvE4AAAAJ&hl=en #biotechnology #science #vc #startup #startups #startupideas #biology #biotech #chemistry #business #speech #speeches #investing #investment #lifescience #casestudy #scientist #scientists #inventors #inventor #inventions #invention #research #innovation #siliconvalley #entrepreneur #entrepreneurship #entrepreneurs #healthcare #genomics #genetics #history #biologia #technology #tech #founderstories #founder #founders #founderstory #scienceandtechnology #sciencestory #stories #bioengineering #engineering #engineer #biochemistry #discovery #discover #technical #health #education #educational #learning #learn #podcast #podcasts #podcasting #podcaster #technews Axial Newsletter: https://axial.substack.com/ Build with Axial: https://build.axialvc.com/ https://linktr.ee/axialxyz$

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