Generative biology is a revolutionary approach to drug discovery and development that leverages AI and machine learning to design novel protein therapeutics. It holds the potential to enhance the speed and efficiency of drug discovery.
In this four-part series, Ray Deshaies, senior vice president of Global Research at Amgen, discusses how generative biology is transforming drug discovery to make it more predictable, shorten timelines, and increase success rates of bringing life-saving medicines to patients who need them most.
Episode 1: Generative Biology: The Cresting Wave of Transformational Science
Intro: Welcome to The Generative Biology Revolution, a special edition podcast series produced by The Scientist's Creative Services Team.
This series is brought to you by Amgen, a pioneer in the science of using living cells to make biologic medicines. They helped invent the processes and tools that built the global biotech industry and have since reached millions of patients suffering from serious illnesses around the world with their medicines.
Generative biology is a revolutionary approach to drug discovery and development that leverages machine learning and AI to design novel protein therapeutics. It holds the potential to enhance the speed and efficiency of discovery. In this series, Ray Deshaies, senior vice president of Global Research at Amgen, discusses how generative biology is transforming drug discovery to make it more predictable, shorten timelines, and increase success rates of bringing life-saving medicines to patients who need them most.
Biologic drugs are revolutionizing disease treatment. They are made from living cells and include proteins such as antibodies. Identifying and optimizing biologics is a slow, iterative process, where scientists must constantly tweak potential therapeutics to improve their activity and safety. In 2021, the world changed for drug research and discovery when researchers published advances that used AI and machine learning to predict the structure of every human protein. With discoveries like this, scientists are launching the generative biology revolution where they strive to leave the guesswork behind and instead use computers to quickly tailor biological molecules for therapeutic purposes.
In this episode, I speak with Alan Russell, Vice President of Biologics at Amgen. Together, we review what generative biology is and how it helps scientists understand proteins from their amino acid building blocks to their folded, three-dimensional structures. We also discuss how this new field improves the quality and complexity of biologic drug candidates and the speed with which researchers generate them.
Ray: Hey, Alan, it's really great to be with you here today. I look forward to having a really stimulating conversation. Why don't you tell us a bit about your background?
Alan Russell: I'm a protein engineer by training—started out right at the very birth of the field learning about how to engineer the structure of proteins and ended up in academia. And I spent a long time in the university trying to push the edge of science forward in protein engineering. I was at Carnegie Mellon University, and at the beginning of COVID, Amgen called me and talked about whether it was time to think about taking all of that experience and using it to help patients and make drugs. And there was just something so phenomenally exciting about that that I took this leap of faith and came to join the team.
Ray: We're here today to discuss a new and exciting field that is revolutionizing drug discovery, but let me first set the stage.
Proteins are made by linking amino acids together into a chain. The chain can be short, say 50 amino acids, or as long as a few thousand amino acids. Once formed, the chain folds up into a specific shape that enables the protein to carry out a function or activity. Drugs act by binding to proteins and changing their activity. Let's say that there is a genetic variant that increases the activity of a protein and that increase in activity triggers a disease process. If we can make a drug that binds to that protein specifically and reduces its activity, that drug could be used to treat the disease.
The biologics that your team makes include proteins such as antibodies that influence the activity of other proteins that cause disease. Biologics represent a powerful class of therapies because they can be very potent and very specific, so they can treat many different serious illnesses such as cancer and asthma.
A big part of biologic drug research is developing proteins that bind their targets in optimal ways. We're now at the point where computational approaches are giving researchers insights into protein structure and function that they can leverage to make better drugs. These approaches are part of a field called generative biology. Alan, can you tell us exactly what generative biology means?
Alan: Generative biology is a field that seeks to extract generalized principles by which biological systems function. What we're trying to do is understand these general principles that allow you to figure out why a biological system actually works the way it works.
Ray: So generative biology then applies not just to protein design and protein folding, but across all functions that occur in biology from the cell to the molecule.
Alan: Yes. Biology works by proteins acting as machines that do things. And we're trying to understand how the sequence of those proteins—if you think of a pearl necklace, and each of the beads—how the sequence of the beads causes the necklace to fold on itself and how that leads it to be able to do its job. So, if we can extract those generalized principles by which biology works and functions, we should then be able to connect the sequence to the structure and the function. That now lets us use what computational scientists call generative models to predict new sequences with functions that we want them to have.
Ray: You've intimated in that description that in going from sequence to structure to function, the idea would be to use methods of AI or machine learning to achieve that. I'm not a computer scientist. So, give me a brief layman's description of what type of AI or machine learning is used to link protein sequence to its structure, to its function, and how do the algorithms work?
Alan: Computer science was very good at doing what are called discriminative models. A discriminative model is something that says, if I know what x is, then I can predict y. If we think about important properties of drugs, one of them is viscosity. So, is it a liquid like water? Or does it flow like honey? That affects greatly whether it's going to be a really good drug. A discriminative model that might be used in artificial intelligence and in machine learning would allow you to take a huge set of data concerning that molecule and predict its viscosity, which would tell us whether or not, for instance, we could inject it easily.
Ray: So could you take us a little bit further under the hood of these generative algorithms. How do they differ from discriminative models and how do they enhance drug development?
Alan: A generative model deals with statistical challenges. So, it's much more challenging mathematically. The easiest way to get our mind around it might be to reflect back on viscosity and whether or not we can we can use it.
If I had a drug, a discriminative model would predict what its behavior would be like, what its viscosity might be like. A generative model will do something even more exciting. It will say, that's the drug you've got and that's the viscosity that it's likely to have. But if you want a different viscosity, this is how you do it. So, in other words, it generates new solutions
If you have x, you can predict y in a discriminative model. In a generative model, if you have x, you can now change the value of x in order to get a different y. That's what's really so powerful because in our world where we're looking for new therapeutics made out of proteins, nature gives us a set of proteins, which we get to engineer and change a little bit. We can change things like viscosity, how they interact with the immune system, but we're kind of stuck with what nature gave us. These generative models allow us to say, we're not limited to that anymore. We can start there, but then generate a whole host of new proteins with functions that we actually need.
Ray: Can you talk about advances, particularly in the area of protein structure determination, that mesh together with these AI algorithms to help drive the work that you do?
Alan: When I talk about these generative models, for them to work as fast as they possibly can today, we'd like to know what the structure of the protein is that we're thinking about. So, it's incredibly useful in order to generate alternatives to have a starting point.
Some time ago, cryo-EM, cryo electron microscopy, was this huge advance. What is that? Basically, a really powerful microscope that works on stuff that's really cold, because when something gets cold, it freezes its shape, it freezes it structure. And if you have a powerful enough microscope, you can look at that structure. Now, we can develop these models that will predict function from known sequence and structure and continue to learn. Just like an iPhone, it looks at your face in order to open the iPhone. Over time, it gets to know what you look like in the morning so that it recognizes you when you first wake up. It gets better and better at recognizing you. And that's the same here.
Ray: There's this analogy to think about enzymes and proteins and how they recognize things called the lock and key model. The protein is like a lock and the thing that it's acting on is like the key. And the two of them have this very precise shape complementarity that enables them to interact in a very specific way, just like one key will only fit into the lock that it's designed for. A lot of the models are oriented towards understanding how do you get the shape of the lock that fits the particular key that you have.
With a lock and key, the whole point is not just to put the key in the lock, but then to turn it. And when you turn it, there are things in the lock that actually move, the tumblers, and that's what unlocks the door. Just like with the key in the lock analogy, for anything meaningful to happen in biology, often the protein has to move, just like the tumblers in the lock move, so that it can have the desired effect. So how do you think about that in terms of AI?
Alan: Motion is really hard to figure out and to predict. I like to think of motion from the perspective of ballet. I'm a big fan of ballet. And if you just for a moment put your mind watching a pas de deux between a ballerina and a ballet dancer on stage, they're moving all the time. When they come together and they intertwine, change shape and create that incredible beauty together, that's when magic happens, right? That's the same here. We have to remember the proteins as they move around inside cells and inside our body. All proteins are moving just like the ballerina and the ballet dancer. But once they find themselves, they can come together.
Cryo-EM, we freeze a structure, we lock it in place. Well, that's only one structure. What about all the other structures? Think of that ballerina moving around, there's all sorts of motion. There's a sort of average place where they be, and then there's a bunch of other movement.
We've always been focused on what that average is. If we could understand the motion and understand when that's important and what is moving in a protein, it opens up the door to a whole host of new approaches to create drugs that intervene.
Now, this has been accomplished to a large part already, in terms of thinking about the interaction of small molecules with proteins. That's because computationally using something called molecular dynamics, you can simulate how a small molecule moves around inside a large molecule. Even in those models, Ray, we can only predict maybe a few nanoseconds of time, maybe a few microseconds of motion. This is an area where we don't just need new software, we probably need new hardware, new types of computers. It's a very active area of biology, and it will open the door to new ways to create drugs.
Ray: When you arrived at Amgen, you kicked off this large effort you named Biologics NExT that's taking a lot of these fundamental principles we've been talking about, linking protein sequence to protein structure to protein function, including things like protein movement, and using those fundamental principles and applying them to making therapeutics that would have beneficial impact on human disease. Can you tell us a little bit more about how you think about Biologics NExT and how you see the integration of all of this knowledge to really help us make drugs?
Alan: We've talked a little bit about these incredible waves of science that are breaking: computational science, automation, robotics, biology, structural biology, molecular dynamics. And if you ever watched people surfing, you have a choice, right? You can either stand and watch the people surfing the waves, or you can just walk away. You can even look at the waves. That's kind of cool, right? You see these? That's what most people do in science is watch the waves breaking and they get excited. We can't just sit there watching these waves. We have to figure out what are the surfboards that will allow us to surf that wave and to harness the power and the energy behind this new science.
What the team did is they simply said, okay, for each one of these waves, what are the surfboards? How do we build them? What do we need to put in place? How can we do our experiments in different ways? How can we create the systems that will generate the data that will power the algorithms? How do we deploy the automation? And what's the foundation upon which we can build? For the last 10 years, we've been building the automation platforms to be ready, so that once the computers were able to use that data usefully, we'd be ready to produce the data. Bringing those all together, the team is getting on that board, on those massive waves, and we're having a heck of a lot of fun doing it.
Ray: When we're designing things, we might have a target structure we're aiming for, and we might end up predicting a sequence to make that structure. We're usually using a combination of biology and the computer. We're making hundreds, if not thousands of different structures and empirically testing them to find which one really achieves what we want it to achieve. When is it going to be the case that I walk into your office and say, Alan, I really want this structure, and you literally hit a button, and then in the next hour, day, the computer spits out and says, make this sequence? And if you make this sequence, you'll have that structure. Is that something that's going to happen in the next two years? Five years? 100 years?
Alan: Already today, the computer's good enough at including the right one amongst a whole bunch of wrong ones. I think what you're getting at is, when will it just be right? Five years sounds reasonable when you've got a very manageable number of predicted molecules. It depends a lot on the complexity of the molecule, the size of the molecules, but I think five years you're going to see a different world than you see today in this kind of science.
Ray: Well, Alan, this has just been fantastic. Your optimism is exceeded only by your intelligence. It's really a pleasure to talk to you. Thank you for being with us today. I look forward to the next time we can get together, sit down, have a beer, and chat about the future of biotechnology.
Alan: Sounds good, free tomorrow. Take care.
Episodes Coming Soon
The Protein Structure Prediction Problem with Mike Nohaile, Ph.D., Chief Scientific Officer, Generate Biomedicines
Protein Design with David Baker, Ph.D., Institute for Protein Design, University of Washington
Accelerating Drug Discovery with Suzanne Edavettal, Ph.D., Executive Director, Protein Engineering