David Baker: The future of protein production

In this edition of Titans of Science, Chris Smith chats to David Baker, the Nobel Prize winner who used AI to design custom proteins...

Chris - People often say that it gets interesting when things break or don't work. So when you do this, are there any things that trip up these artificial intelligences, things that they consistently get wrong and shouldn't. Because often there might be something interesting lurking in there. Have you noticed anything like that?

David - In fact, in every problem we work on, we only work on problems which are kind of at the cutting edge of what's possible. Because the really easy problems we figure people in other places could do with the software we're releasing. And whenever you work on a hard problem, you only understand about 30, 40% of what's going on. And so one of the things, the really key thing, is you start working on a problem like targeting a tumour or breaking down plastic. And the first few designs you make don't work or they don't work very well, and then you have to look at what's going on that's wrong. Then that gives you ideas on what you need to improve about your design strategy or the methods to really solve those problems. And so that's really largely what science is about, is having some hypothesis about how to solve a problem, trying to solve it, and then it doesn't work as well as you thought. And then trying to figure out what the basis for that is and improving your method and approach accordingly.

Chris - Some branches of science are also now going down the synthetic route where, when we began this conversation, you explained a protein is something made from one of a combination of 20 different amino acids, but we can as clever chemists now make amino acids that don't exist in nature. So we can therefore do chemistry that may not exist in nature. Can the artificial intelligences be brought to bear using these novel chemicals though, because of course we won't have that vast database of proteins that use these new chemicals we're creating to train on.

David - Exactly. So this is where the previous methods that were based on modelling all the interactions between the atoms still are very useful. So we're trying to do exactly what you described, build catalysts now that incorporate unnatural amino acids and unnatural co-factors into our designs. It's like having our machine now has this kind of totally new powerful thing in it that will allow it to do more sophisticated chemistry. And this is where combining the new deep learning methods, which as you pointed out are really used to just seeing the natural 20 amino acids, with the previous methods that I described where we're modelling everything as just a collection of atoms using physical principles. That combination is powerful because those older physically-based methods have no problem modelling that unnatural amino acid or co-factor as just a collection of atoms connected by bonds.

Chris - It's so exciting all of this and you can really see how this is going to translate and quickly into really groundbreaking stuff, can't you? But when you go home at the end of the day, is your head spinning because of this or do you have crafty ways of managing to relax or sort of get away from it and not think about protein structures for a while?

David - What I should tell you about is a little bit about my work environment. Because this area is so exciting now, there are many, many brilliant, super motivated, energetic people at all career stages from around the world who are coming to my group to explore new areas like breaking down plastic or fixing CO2 and it's really an amazing place. We don't have very much space, so everyone's very close together and everyone's kind of talking and brainstorming about the next frontier. And because it's a big group, there's new exciting results popping up pretty much every day. And so it's this incredibly exhilarating environment. So I have to say at the end of the day, I spend my day just talking to people in my group sort of individually or in groups and it's super fun, but I would say at the end of the day, my head is spinning a little bit. So it's both the potential of the problem and there's so much activity and exciting progress. So I live in Seattle, which is fortunate because I love the mountains. So on the weekends, I try and get up skiing or hiking or climbing, pretty much every weekend. And, so right now in fact it is a little rainy in Seattle, but that means it's snowing in the mountains. So I'm excited to get out and ski this weekend.

Chris - Good for inspiration. I should think as Kary Mullis, who got the Nobel Prize a number of years ago for discovering and coming up with the idea of the PCR reaction to copy DNA, told me he came up with that concept while driving up to his mountain cabin at Montechino. So maybe your trips out into the great back of beyond are very inspirational.

David - Yeah, they certainly helped me preserve sanity, which is very good.

Chris - Well, thanks very much for telling us all about it, David. Congratulations once again on your Nobel Prize and I hope that with the Nobel Prize comes a bigger office because it sounds a bit cramped.

David - Well, so far I would say the Nobel Prize has been remarkably useless in trying to improve our research conditions or resources, but I'm still hopeful.