In today’s episode we have two short segments, both on bioscience topics:
[0:00] Moderna has started clinical trials for a flu vaccine, called mRNA-1010, that is based on the same mRNA technology that Moderna and Pfizer used for their COVID vaccines, and that several other companies including Sanofi and Glaxo all are actively working on for the influenza use case. Our experts are general partners Vineeta Agarwala and Jorge Conde of the a16z bio team, who have joined us on many of our vaccine-related episodes, which you can find at a16z.com/vaccines. They discuss what comes next for the clinical trials of this mRNA-based flu vaccines, why companies aren’t planning to use the faster and more-targeted mRNA technology for COVID’s Delta variant, and how mRNA vaccines will change not only our approach to flu shots but to other respiratory viruses.
[9:53] Google’s DeepMind AlphaFold, in partnership with the European Molecular Biology Laboratory, is publicly sharing its entire protein structure database — with predicted protein structure models for ~20,000 proteins expressed by the human genome — meaning that all its data will be freely and openly available to the scientific community. (We previously discussed DeepMind’s AlphaFold protein-folding AI on this show in episode #48.) General partner Vijay Pande of the a16z bio team joins a16z editor Zoran Basich to help us answer a key question — why does it matter that a huge database of very accurate predicted protein structures is now freely available? — and explains why AI is here to stay in structural biology.
Vineeta Agarwala is a General Partner on the Bio + Health team, focused on biotech, digital health, and life sciences tools/diagnostics.
Jorge Conde is a General Partner on the Bio + Health team, focused on therapeutics, diagnostics, life sciences tools, and software.
Vijay Pande is the founding General Partner of a16z Bio + Health, focused on the cross-section of biology and computer science.
16 minutes is a short news podcast covering the top headlines of the week, separating what’s real from what’s hype.