Computational expense has long been a challenge for 3D structure determination in electron cryomicroscopy (cryo-EM). This problem has become more pressing as resolutions have increased and the desire to separate conformational computational variations has required ever-larger datasets. This talk presents two new algorithmic developments which, when coupled with modern GPU hardware, dramatically reduces the computational requirements for cryo-EM. Given low-resolution starting structures, high resolution structures can now be obtained in as little as 10s of minutes on modest desktop workstations. Further, despite the severe non-convexity of the objective function, these new refinement algorithms have shown themselves to be robust to local minima, enabling ab initio structure determination and 3D classification. Together, these algorithms can produce ab initio high resolution structures in an hour or two. This talk will describe the underlying algorithmic advances (stochastic optimization and branch-and-bound search) along with results from their implementation in the recently released cryoSPARC software package.
Dr. Marcus Brubaker is an Assistant Professor of Computer Science at York University in Toronto, Canada. He received his Ph.D. in Computer Science from the University of Toronto in 2011 and, before joining York University, worked as a postdoctoral fellow at the Toyota Technological Institute at Chicago. His research interests span computer vision, machine learning and statistics and he has worked on a range of problems including video-based human motion estimation, physical models of human motion, Bayesian inference, ballistic forensics, electron cryomicroscopy and autonomous driving. He also consults with industry on machine learning and computer vision related projects and has been involved in a number of startups, including as the co-founder of Structura Biotechnology Inc.