AI4Proteins: Machine learning for biological sequence design – Dr Lucy Colwell скачать в хорошем качестве

AI4Proteins: Machine learning for biological sequence design – Dr Lucy Colwell

This seminar forms part of the AI3SD Winter Seminar Series and is also the first in the AI3SD and RSC-CICAG AI 4 Proteins Series. This series is sponsored by Arctoris and Schrödinger. This video is the first talk in the first seminar of the series: AI 4 Proteins I. Machine learning for biological sequence design – Dr Lucy Colwell 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. Bio: Lucy Colwell is 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. With collaborators Lucy showed that graphical models built from aligned protein sequences can be used to predict protein tertiary structure and functional attributes. 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. Further details from this series can be found at: https://www.ai3sd.org/ai4proteins This video is an output from the AI3SD Network+ (Artificial Intelligence and Augmented Intelligence for Automated Investigations for Scientific Discovery) which is funded by EPSRC under Grant Number EP/S000356/1 DOI Link: https://doi.org/10.5258/SOTON/P0090