From AlphaFold to MMseqs2-GPU: How AI is Accelerating Protein Science - Ep. 273

Hello, and welcome to the NVIDIA AI podcast. I'm your host, Noah Kravitz. Work being done on protein structures is amongst the most exciting and impactful work being assisted by AI right now. With us today are two of the leaders in the industry at the forefront of research and development on protein structures. They're here to talk about some exciting developments in the area, including the recent acceptance of a major paper to Nature Methods. So let's get right into it. Today with me are Chris Delago and Martin Steininger. Chris is research lead at NVIDIA and visiting professor at Duke University. He's been at the forefront of GPU accelerated machine learning, advancing the way we apply AI to complex problems in biology, and he's been working on several protein structure related activities that we're gonna talk about in a second. And with Chris is Martin Steiniger. Martin is an associate professor of biology at Seoul National University, joining us from South Korea this morning. This morning our time, evening your time, Martin. And he is the coauthor on the Nobel Prize winning alpha fold paper as well as developer of many foundational tools used in homology search and structure prediction and increasingly used for protein design. Here to unpack all of that and get into the excitement around this month's announcements and just generally speaking, all of the advancements in protein structure, Chris and Martin, thank you so much for joining the NVIDIA AI podcast. Thanks for having us now. Thank you. So let's start high level, and maybe we'll start with you, Martin. But, obviously, Chris, feel free to jump in. Why is this so important? What's the significance of proteins and their three d structures, and why are we talking about it on the NVIDIA AI podcast? Yes. So it's a very good question. Proteins are really these, like, small machineries that drive cells or drive effectively everything in in in life, around us. And so they are composed of amino acids. So you have, like, 20 of these amino acids, and you can imagine, like, putting these amino acids in a line as a string. And if you put the string into water, the solvent, and what happens is that this string turns into a three-dimensional structure. And this three-dimensional structure really implies the function of this of this machinery. And that is really what we care about in the end. And it's important for drug discovery, you know, understanding how this machinery work, how can we modify it for our purposes, how can we really, yeah, use it for for humanity's good in some way? And so the structure is really fundamental for that. And can you tell us then a little bit about the work that you're doing, your lab does at Seoul National University? And you got into it a little bit talking about the machinery. But why are these structures so central? Why have researchers been spending so much, you know, …