Colloquium – John Jumper

John Jumper, Google DeepMind

Highly Accurate Protein Structure Prediction and Its Applications

Our work on deep learning for biology, specifically the AlphaFold system, has demonstrated that neural networks are capable of highly accurate modeling of both protein structure and protein-protein interactions. In particular, the system shows a remarkable ability to extract chemical and evolutionary principles from experimental structural data. This computational tool has repeatedly shown the ability to not only predict accurate structures for novel sequences and novel folds but also to do unexpected tasks such as selecting stable protein designs or detecting protein disorder. In this lecture, I will discuss the context of this breakthrough in the machine learning principles, the diverse data and rigorous evaluation environment that enabled it to occur, and the many innovative ways in which the community is using these tools to do new types of science. I will also reflect on some surprising limitations — insensitivity to mutations and the lack of context about the chemical environment of the proteins — and how this may be traced back to the essential features of the training process. Finally, I will conclude with a discussion of some ideas on the future of machine learning in structure biology and how the experimental and computational communities can think about organizing their research and data to enable many more such breakthroughs in the future.

Aug 31, 2023 @ 4:00pm Central in Stevenson 4327; reception beforehand at 3:30pm in Stevenson 6333

Host: S. Hutson

To join via Zoom, please contact Reina Beach (reina.beach@vanderbilt.edu) to request the Zoom link.
 

John Jumper received a B.S. in Physics and Mathematics from Vanderbilt University in 2007. He also holds a PhD in Chemistry from the University of Chicago, where he developed machine learning methods to simulate protein dynamics. Prior to that, he worked at D.E. Shaw Research on molecular dynamics simulations of protein dynamics and supercooled liquids. At Google DeepMind, John is leading the development of new methods to apply machine learning to protein biology and is the research lead for the AlphaFold project. John has won numerous awards for his work on AlphaFold including the 2022 Wiley Prize, the 2023 Breakthrough Prize in Life Sciences, and the 2023 Canada Gairdner International Award.