Published on April 29, 2025 3:45 PM GMT
I made another biology-ML podcast! Two hours long, deeply technical, links below.
I posted about others ones I did here (machine learning in molecular dynamics) and here (machine learning in vaccine design). This one is over machine learning in protein design, interviewing perhaps one of the most well-known people in the field. This is my own field, so the podcast is very in the weeds, but hopefully interesting to those deeply curious about biology!
Substack: https://www.owlposting.com/p/what-could-alphafold-4-look-like
Youtube: https://youtu.be/6_RFXNxy62c
Spotify: https://open.spotify.com/episode/0wPs3rmp0zrfauqToozrcv?si=DCtRf-xQTPiVYwslo-b2rQ
Apple Podcasts: https://podcasts.apple.com/us/podcast/what-could-alphafold-4-look-like-sergey-ovchinnikov-3/id1758545538?i=1000704927828
Transcript: https://www.owlposting.com/p/what-could-alphafold-4-look-like?open=false#%C2%A7transcript
Summary: To those in the protein design space, Dr. Sergey Ovchinnikov is a very, very well-recognized name.
A recent MIT professor (circa early 2024), he has played a part in a staggering number of recent innovations in the field: ColabFold, RFDiffusion, Bindcraft, automated design of soluble proxies of membrane proteins, elucidating what protein language models are learning, conformational sampling via Alphafold2, and many more. And even beyond the research that have come from his lab in the last few years, the co-evolution work he did during his PhD/fellowship also laid some of the groundwork for the original Alphafold paper, being cited twice in it.
As a result, Sergey’s work has gained a reputation for being something that is worth reading. But nobody has ever interviewed him before! Which was shocking for someone who was so pivotally important for the field. So, obviously, I wanted to be the first one to do it. After an initial call, I took a train down to Boston, booked a studio, and chatted with him for a few hours, asking every question I could think of. We talk about his own journey into biology research, some issues he has with Alphafold3, what Alphafold4-and-beyond models may look like, what research he’d want to spend a hundred million dollars on, and lots more. Take a look at the timestamps to get an overview!
Timestamps:
[00:01:10] Introduction + Sergey's background and how he got into the field
[00:18:14] Is conservation all you need?
[00:23:26] Ambiguous vs non-ambiguous regions in proteins
[00:24:59] What will AlphaFold 4/5/6 look like?
[00:36:19] Diffusion vs. inversion for protein design
[00:44:52] A problem with Alphafold3
[00:53:41] MSA vs. single sequence models
[01:06:52] How Sergey picks research problems
[01:21:06] What are DNA models like Evo learning?
[01:29:11] The problem with train/test splits in biology
[01:49:07] What Sergey would do with $100 million
Discuss
