Last week we had a really nice meeting in Madison, WI on combining simulations and machine learning to understand protein dynamics.
This meeting was hosted through CECAM (Centre Européen de Calcul Atomique et Moléculaire), which aims to promote fundamental research on advanced computational methods and their application to important problems in frontier areas of science and technology. As you can guess from the name, CECAM originzted in Europe. However, their meetings have been so successful over the past decades that they have started expanding globally. The first node in the US opened this year at the University of Chicago, in collaboration with Northwestern University, University of Wisconsin-Madison, University of Illinois Urbana-Champaign, University of Notre Dame, Purdue University, and Argonne National Laboratory. Scientists from the US can now apply to this node to get funding and organizational support to support conferences on topics related to computation in science.
Xuhui Haung led the effort to organize a meeting in Madison that brought together a really wonderful group of people. We realized that a good number of us were alum’s of the Pande lab or otherwise involved in Folding@home, so we got a nice group photo, below.
The meeting was a great opportunity to learn about recent work in the Huang lab on how to build maps of conformational space from simulation data, as well as methods from the Voelz lab on using Bayesian statistics to optimize models. The Hanson lab presented new work on using simulations to understand what effect rapidly freezing proteins has on inferences the field draws from experiments conducted at very low temperatures. Meanwhile, the Shukla lab gave an update on their efforts to help make crops more resilient to climate change and pests. I gave an update on our work to exploit cryptic pockets for drug discovery, and the Chong lab gave an update on their software for managing simulations and analysis. The Wayment-Steele lab shared an update on their efforts to use machine learning to extract more insight from NMR data.
As always, we are thankful for the role you play in making our work possible!