Recently, BenchSci had the great pleasure of hosting Nobel Laureate Dr. Venki Ramakrishnan for a fireside chat with our users. Dr. Ramakrishnan has worked as a biologist for over 30 years, with much of his research being focused on central problems in molecular biology. He was awarded the Nobel Prize alongside Thomas Steitz and Ada Yonath for the high-resolution atomic structure of the ribosome, which fundamentally changed our understanding of biology and opened up new possibilities in the development of antibiotics. He is also the author of the popular book, Gene Machine, which captures his first-hand account of the discovery.
I had the amazing opportunity to ask Dr. Ramakrishnan about how he got his start in biology, his current research, as well as some burning questions from our audience. Here’s just a snippet of his insightful thoughts.
DM: A common path for prospective scientists is to go from undergrad studies to graduate school in their field of interest, hoping to run their own lab someday. Your journey was a bit different, starting in physics before moving to the realm of molecular biology and chemistry. Could you tell us about that journey?
VR: When I was an undergrad in India, biology was taught like a rote subject. You had to memorize all these species, properties, and so on. It just didn’t appeal to me. I wanted to do mathematics, but this was before the computer revolution. People said there were no jobs in mathematics, and I had to do something practical. So, I chose physics as the second-best option. I love theoretical physics, but when I started doing research, I just found it very hard to make progress on fundamental problems. Physics is a very mature field, whereas there were big breakthroughs constantly being made in biology. And it seemed like you could be a mere mortal and make them—you didn’t have to be some sort of genius to make important discoveries.
After my Ph.D. in Physics, I decided to switch to biology, but I didn’t know anything about the subject. So, I went back to graduate school all over again at the University of California in San Diego. When I got to graduate school, I realized I didn’t understand anything that people were talking about, so I had to take undergraduate courses in my first year. It taught me that you might know a lot about one field, but if you want to get into a different one, you have to start at the beginning. You have to have some humility about it.
DM: Were there skills or mindsets that you picked up in your time in physics that transported well to biology?
VR: I think biology is going through a transformation—BenchSci is a great example. It’s going from a very descriptive field to something that is very computational and quantitative. Physics gave me mathematical and computing skills and a quantitative way of thinking, which really helped me in structural biology. Whether it’s crystallography or electron microscopy, we use physical techniques to visualize biological structures. A physics background also helps a lot when learning new algorithms, applying mathematics, and even computer coding, all of which are very useful skills for a biologist to have. I’m glad to see things changing to provide young biologists with this kind of expertise in the course of their biology training.
DM: In your book, Gene Machine, you tell a more human story about discovery and the doubts and the difficulties that come with it. Do you find that the human side of science is missing from education or even the public discourse?
VR: I think that’s partly true. What happens is, at the undergraduate level and even at the graduate level, you read textbooks and papers. You read science as though it’s a series of finished products. You don’t see the process—all the mistakes and the dead ends and the detours you took along the way to get the result. It’s almost like science is a series of results written in stone, and you just arrived at the answer by a series of logical steps, but that’s not the case at all. Experiments never tell you everything, they’re sometimes flawed, and scientists argue about what the results mean all the time.
Science is actually very messy—and that’s not even taking into account the fact that scientists are only human. We’re not completely rational. We have emotions. We’re competitive. We’re jealous, egotistic, ambitious, and so on. We don’t stop being human when we start doing science. It’s all part of what I call the sociology of science. It’s something I think young people need to be aware of—it can be a shock to get into a real science field and realize that human behavior still applies. Of course, we can try to make things better, but ultimately, we have human failings.
DM: I think scientific uncertainty is uncomfortable for the public. What sort of responsibility do scientists have to be a voice of reason in what sometimes feels like an unreasonable world?
VR: I think you raised a very important question. Scientists constantly live in uncertainty. Even when we prove a result, we’re never 100% sure it’s definitive. It’s very rare that science ever comes up with a completely unambiguous answer. I think it’s our job really to communicate to the public what this means.
When we talk about likelihoods and probabilities, the public often wants to know, “is it the answer A or B?” This is a big problem with climate change. Skeptics always point to that narrow window of uncertainty in the data and say, “Well, see? They don’t know what they’re talking about.” When actually, scientists live with that all the time—we know how to proceed with uncertainty because we have to. I think that’s something the public needs to understand.
DM: In keeping up with the literature and what’s happening in the scientific community, is there anything you’ve seen in the last two to three years that you’re predicting might be the next big thing?
VR: Maybe not in the last two or three years, but in the last decade, yes. Although the revolution is still going on, by which I mean advances in our ability to visualize molecules. I mentioned the ribosome, for example. It’s enormously complex, with half a million atoms in the human version. It was solved using X-ray crystallography, which meant you had to take this giant molecule, create crystals, then try to hit them with X-rays and analyze the data. It all took quite a long time, and getting crystals is still very difficult even today. One of the reasons that Ada Yonath won the Nobel Prize with Tom Steitz and me was because she produced the first crystals of a ribosome structure. Then a method called cryo-electron microscopy made a big leap about five to ten years ago. Using this method, large complexes are now routinely being solved without crystals or even complete purification. We can trap complexes in the act of doing their thing and even capture multiple states in a complicated process like DNA replication to make a sort of movie.
So, structural biology has advanced tremendously. The next big frontier is being able to see molecular structures in detail inside of cells. We think of cells as these bags with a random assortment of structures, but actually, cells are highly organized—every molecule has its place. With advances in technology, we’ll soon be able to investigate things like how these molecules function inside a cell or how a cell’s surface interacts with its neighbors in much greater detail. This dramatic revolution will help us better connect molecular information with cell biology and function.
Then there’s AI, which is now being used to predict the structure of proteins, among other things. You could argue it’s the early days and not 100% reliable, but the advancements that have been made in places like DeepMind with their AlphaFold program are really amazing. These systems are only going to continue increasing in power. Hopefully, soon, we’ll be able to use them to predict more complex things like how proteins interact with each other to bind small molecules like proteins and ligands. I see a big future for the application of computer methods in biology.
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