AlphaFold: The making of a scientific breakthrough

We've discovered more about the world than any other civilization before us. But we have been stuck on this one problem. How do proteins fold up?

How do proteins go from a string of amino acids to a compact shape that acts as a machine and drives life? When you find out about proteins it is very exciting. You can think of them as little biological nanomachines.

They are essentially the fundamental building blocks that power everything living on this planet. If we can reliably predict protein structures using AI, that could change the way we understand the natural world. Protein folding is one of these holy grail type problems in biology.

We've always hypothesized that AI should be helpful to make these kinds of big scientific breakthroughs more quickly. And I'll probably be looking at little tunings that might make a difference. It should be creating a distagram one and a background distagram one.

We've been working on our system AlphaFold really hard now for over two years. Rather than having to do painstaking experiments, in the future biologists might be able to instead rely on AI methods to directly predict structures quickly and efficiently. Generally speaking, biologists tend to be quite skeptical of computational work.

And I think that skepticism is healthy and I respect it, but I feel very excited about what AlphaFold can achieve. CASP (Critical Assessment of Protein Structure Prediction) is when we say, look DeepMind is doing protein folding. This is how good we are.

Maybe it's better than everyone else, maybe it isn't. We decided to enter the CASP competition because it represented the Olympics of protein folding. CASP, we started to try and speed up the solution to the protein folding problem.

When we started CASP in 1994, I certainly was naive about how hard this was going to be. It was very cumbersome to do that because it took a long time. Let's see, what are we doing still to improve?

Typically a hundred different groups from around the world participate in CASP. And we take a set of a hundred proteins, and we ask the groups to send us what they think the structures look like. We can reach 57.

9 GDT on CASP12 ground truth. CASP has a metric on which you will be scored, which is this GDT metric. On a scale of zero to a hundred, you would expect a GDT over 90 to be a solution to the problem.

If we do achieve this, this has incredible medical relevance. The implications are immense. From how diseases progress.

How you can discover new drugs. It's endless. Well I wanted to make a really simple system, and the results have been surprisingly good.

The team got some results with a new technique. Not only is it more accurate, but it's much faster than the old system. I think we'll substantially exceed what we're doing right now.

Well, then it's a game changer, I think. In CASP13, something very significant had happened. For the first time we saw the effective application of artificial intelligence.

We've advanced the state of the art in the field, so that's fantastic, but we still got a long way to go before we've solved it. The shapes were now approximately correct for many of the proteins but the details, exactly where each atom sits, which is really what one would call a solution were not yet there. It doesn't help if you have the tallest ladder when you're going to the moon.

We hit a little bit of a brick wall, since we won CASP, then it was back to the drawing board and like what are our new ideas. And then it's taken them a little while, I would say for them to get to where they were but with the new ideas. And then now I think we're seeing the benefits of the new ideas.

They can go further, right? So that's a really important moment. I've seen that moment so many times now.

I know what that means now. And I know this is the time now to press. We need to double down and go as fast as possible from here.

I think we've got no time to lose. So the intention is to enter CASP again. CASP is deeply stressful.

There's something weird going on with the learning, because It is learning something that's correlated with GDT, but it's not calibrated. I feel slightly uncomfortable. We should be learning this you know in the blink of an eye.

The technology advancing outside DeepMind is also doing incredible work. There's always the possibility another team has come somewhere out of left field that we don't even know about. Someone asked me, "Well, should we panic now?

" Of course, we should have been panicking before. It does seem to do better, but still doesn't do quite as well as the best model. So it looks like there's room for improvement.

There's always a risk that you've missed something, and that's why blind assessments like CASP are so important to validate whether our results are real. Obviously, I'm excited to see how CASP14 goes. My expectation is we get our heads down, we focus on the full goal which is to solve the whole problem.

The Prime Minister has announced the most drastic limits to our lives that the UK has ever seen in living memory. I must give the British people a very simple instruction, you must stay at home. We were prepared for CASP to start on April 15th, because that's when it was originally scheduled to start.

And it's been delayed by a month due to coronavirus. I really miss everyone. And I struggled a little bit just kind of getting into a routine especially with my wife, she came down with the virus.

I mean, luckily it didn't turn out to be serious. CASP started on Monday. Can I just check this diagram you've got here, John.

This one where we ask ground truth. Is this one we've done badly on? We're actually quite good on this region.

If you imagine that we hadn't had said it came around this way, but had put it in the right spot. Yeah, there instead, yeah. One of the hardest proteins we've gotten in CASP thus far is a SARS-CoV-2 protein called ORF8.

ORF8 is a coronavirus protein. We tried really hard to improve our prediction. Like really, really hard.

Probably the most time we have ever spent on a single target. So we're about two-thirds of the way through CASP, and we've gotten three answers back. We now have a ground truth for ORF8, which is one of the coronavirus proteins.

And it turns out we did really well in predicting that. It's an amazing job everyone, the whole team. It's been an incredible effort.

Here what we saw in CASP14 was a group delivering atomic accuracy off the bat. Essentially solving what in our world is two problems. How do you look to find the right solution, and then how do you recognize you've got the right solution when you're there.

All right. Are we mostly here? I'm going to read an email.

I got this from John Moult. And I'll just read it. It says, "John, as I expect you know, your group has performed amazingly well in CASP14.

Both relative to other groups and in absolute model accuracy. Congratulations on this work. It is really outstanding.

" AlphaFold represents a huge leap forward that I hope will really accelerate drug discovery and help us to better understand disease. It's pretty mind blowing. You know these results were for me, having worked on this problem so long after many, many stops and starts and will this ever get there, suddenly this is a solution.

We've solved the problem. This gives you such excitement about the way science works. About how you can never see exactly or even approximately, what's going to happen next.

There are always these surprises. And that really as a scientist is what keeps you going. What's going to be the next surprise?