Parag Mallick is a Stanford Professor and Chief Scientist at Nautilus Biotechnology, a publicly traded biotech company. He’s also a professional magician and circus performer, which might sound random until you realize that his company does the closest thing to proteomics magic I’ve ever seen. The things that make you weird, he argues, are exactly what let you see data differently than anyone else.
Here’s what caught me off guard: we can only measure ~8% of human proteins with current mass spectrometry tools. The other 92%—what Parag calls the “dark proteome”—is essentially invisible to us.
Why is proteomics so much harder than genomics? Three reasons. First, protein concentration. DNA is basically uniform—roughly the same amount per cell. Proteins range from one copy to a billion copies in a single cell, and no analytical tool can handle that spread. Second, dynamics. Your genome is relatively static over your lifetime. Your proteome changes every second of every day. Third, there’s no equivalent of amplifying proteins like PCR can do. You get what you get.
Nautilus is tackling this with single-molecule detection. The technical approach is fascinating: they use DNA origami nanoparticles. Picture a Ritz cracker with a flagpole sticking out the top. That flagpole has exactly one attachment point. They coat sample proteins with one half of a click chemistry reagent (methyltetrazine), the nanoparticle has the other half (trans-cyclooctene), and they bind together. One protein per nanoparticle. These then self-assemble onto a flow cell with 100-nanometer landing pads, creating a hyperdense array of billions of individual protein molecules.
Now comes the really clever part: protein identification. Traditional proteomics tries to build one highly specific antibody per protein—an intractable problem when you’re dealing with millions of proteoforms. Nautilus does the opposite. They intentionally build cross-reactive affinity reagents. The system uses ~300 different affinity reagents, each one recognizing just a three-amino-acid epitope. That’s deliberately non-specific. Then they run 300 cycles of iterative mapping. The primary data looks like fluorescent NGS—you get a light-up at each location on the array or you don’t. Binary. Yes or no.
The median protein only needs ~12 epitopes to be uniquely identified, but each protein gets touched 10-30 times across the 300 cycles for high confidence. It’s exactly like playing Guess Who: “Do you have glasses? Brown hair? A hat?” Each question alone tells you almost nothing, but together they pinpoint exactly who you are. Same principle here: “Do you have this 3-amino-acid sequence? What about this one?”
The 300 binary measurements create a point in 300-dimensional space. Each protein has a characteristic signature in this space. The machine learning layer compares your observed pattern against the reference proteome and asks: what protein is compatible with this specific binding pattern? If you find a totally new protein, it’ll occupy a new point in that space—something not in the database.
But here’s the thing about building something this audacious: you can’t prove it works before you start. Parag shared what the early days were actually like—renting a single lab bench at Stanford’s StartX incubator, trying to convince people to join when he couldn’t demonstrate single-molecule deposition yet, couldn’t show them they could run 180 cycles because they didn’t have an instrument. The first automation system was literally called “Parag” because he was pipetting by hand. How do you hire people to believe in something impossible? You share the vision of what it could mean—bringing the proteome to everyone—and see if that resonates. Some people thrive in that uncertainty. Others are brilliant at early-stage innovation but different people excel at scaling and productization. That evolution isn’t failure, Parag argues. It’s healthy. It’s part of the journey.
We’re really just at the beginning of understanding biology. The genomics revolution, as transformative as it’s been, was the opening act. The era where we can actually see what proteins are doing—that’s what comes next.
And in case you’re short on time, here’s a quick teaser:
Watch on Youtube; listen on Apple Podcasts or Spotify.
Guest Information
Parag Mallick. Professor at Stanford University & Chief Scientist at Nautilus Biotechnology.
High-density and scalable protein arrays for single-molecule proteomic studies (bioRxiv, 2022)
