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De novo design
Using AI and compute further down the central dogma is increasingly easier because of the super-exponential decrease in the cost of sequencing. There remain significant bottlenecks in RNA and protein analysis. So new companies can solve these new problems. Until the Illumina equivalent of mass spectrometry emerges (i.e. it could be InterVenn or Newomics) and the underlying circuitry of the cell becomes more predictable (i.e. hopefully Asimov with Cello can pull it off), there remain many niches to gain technical and business edges. One case of this is de novo design of proteins. The key advantage is mainly around specificity. Natural proteins have a tendency to interact with many targets. Depending on use that could be a feature or a bug. To solve this problem, one could search existing evolutionary designs or make new ones. Let’s talk about the latter. Although, evolution is probably the world’s greatest inventor.
De novo design doesn’t need to benefit from some sort of massive improvement in protein sequencing. Rather it just rides the increasing power of compute. Companies like Macromoltek, IgC, Neoleukin, and even DeepMind (probably the UK’s most important company right now) develop computational systems from a set…
