Presenter
Abstract
With Google’s recent advance in protein folding using artificial intelligence, there is increased interest in understanding how data science can aid the study of molecular structure. I will present several examples of successful predictive machine learning models in structural biology, linking this past work to a current Laboratory Directed Research and Development project on the study of protein assembly using coevolutionary analyses.
About the Speaker:
Julie Mitchell is Director of the Biosciences Division. She has over 20 years of experience in working at the interface of quantitative and biological sciences. Mitchell’s research has focused on projects at the interface of biochemistry, data science, and high-performance computing. Her contributions to the field of computational biophysics emphasize the use of machine learning in predictive models for molecular interactions. Mitchell’s group has produced a widely utilized web server for protein-protein interaction hot spots (>80k jobs), many well-cited publications and two patents. She collaborates on ORNL projects related to protein intrinsic disorder, small molecule screening algorithms, and vaccine design.
About the Seminar:
This seminar will also be live-streamed via Blue Jeans https://bluejeans.com/680961319 .