Dr. Lucy Colwell, Assistant Professor, University of Cambridge, Google Research
Abstract:
Prediction of protein functional properties from sequence is a central challenge that would allow us to discover new proteins with specific functionality. Experimental breakthroughs allow data on the relationship between sequence and function to be rapidly acquired that can be used to train and validate machine learning models that predict protein function directly from sequence. However, the cost and latency of wet-lab experiments require methods that find good sequences in few experimental rounds, where each round contains large batches of sequence designs. In this setting, I will discuss model-based optimization approaches that allow us to take advantage of sample inefficient methods and find diverse optimal sequence candidates for experimental evaluation. The potential of this approach is illustrated through the design and experimental validation of viable AAV capsid protein variants for gene therapy applications in addition to the design and validation of peptides as potential therapeutics.
Bio:
Lucy Colwell is a researcher in the applied science group at Google and a faculty member in chemistry at the University of Cambridge. Her primary interests are in the application of machine learning approaches to better understand the relationship between the sequence and function of biological macromolecules. Before moving to Cambridge Lucy received her PhD from Harvard University and was a member at the Institute for Advanced Study in Princeton, NJ. In 2018 Lucy was appointed a Simons Investigator in Mathematical Modeling of Living Systems.