Running and further enhancing FAH is quite involved these days. We’ve come a long way from the early days in 2000 where it was mainly Prof. Pande and just 2-3 other people. Right now, there are about 20 people in the Pande lab who are involved in one way or another. But even that isn’t enough to make progress in certain key areas. To help, we have started collaborating with other labs to form a consortium of labs involved with running, improving, and applying Folding@home to do even greater research than we’ve done before.
BowMan LAB, Washington University in St. Louis
The Bowman lab combines computer simulations and experiments to understand the mechanisms of allostery (i.e. long-range communication between different parts of a protein) and to exploit this insight to control proteins’ functions with drugs and mutations. Examples of ongoing projects include (1) understanding how mutations give rise to antibiotic resistance, (2) designing allosteric drugs to combat antibiotic resistant infections, (3) understanding allosteric networks in G proteins and designing allosteric anti-cancer drugs, and (4) understanding and interfering with the mechanisms of Ebola infection. To rapidly converge on predictive models, we iterate between using simulations to gain mechanistic insight, conducting our own experimental tests of our models, and refining our simulations/analysis based on feedback from experiments. We also develop enhanced sampling algorithms for modeling rare events that are beyond the reach of existing simulation methodologies.
Chodera lab, Memorial Sloan-Kettering Cancer Center
The Chodera lab at the Sloan-Kettering Institute uses Folding@home to better understand how we can design more effective therapies for cancer and other diseases.
Their mission is to completely redesign the way that therapeutics—especially anticancer drugs—are designed using computers, graphics processors (GPUs), distributed computing, robots, and whatever technology we can get our hands on. They are striving to make the design of new cancer drugs much more of an engineering science, where state-of-the-art computer models quantitatively and accurately predict many aspects of drug behavior before they are synthesized. Chodera Lab certainly won’t get there overnight—lots of hard work is needed to improve algorithms, force fields, and theory. But by tapping into the enormous computing resources of F@h, they can more rapidly make predictions and then test them in the laboratory (with robots!) to quickly make improvements through learning from each cycle of prediction and validation.
Huang Lab, HKUST
Xuhui Huang’s lab at HKUST is interested in conformational change, which is crucial for a wide range of biological processes including biomolecular folding and the operation of key cellular machinery.
Izaguirre Lab, Notre Dame
The Izaguirre lab is interested in problems at the interface between biology, computer science, and applied mathematics. Their lab has developed the Protomol MD package, and we have integrated that package into Folding@home. Protomol is a great package for testing and developing new algorithms. Particularly, Protomol implements the Normal Mode Langevin method for doing Long Timestep Molecular Dynamics (LTMD). This allows timesteps hundreds to thousands of times longer than conventional molecular dynamics. We are running Folding@home projects using LTMD to explore the effect of mutations on protein folding. Future projects will explore the effects of conformational dynamics in function and protein aggregation. Also, in the future, LTMD will be fully incorporated into OpenMM to benefit from hundred-fold speedups from GPU acceleration.
Kasson Lab, University of Virginia
Kasson Lab at U. Va works on lipid membrane biophysics, especially as it has a role in viral infection. Dr. Kasson has worked extensively on the SMP core for Folding@home.
Lindahl Lab, Stockholm University
Erik Lindahl’s group is well known as a primary developer of Gromacs, a powerful MD code that is frequently used in Folding@home. He also has interests in lipid membrane biophysics and viral fusion, leading to many collaborations on that front as well.
Shirts lab, University of Virginia
Michael Shirts’s group at the University of Virginia develops new simulation methodology and uses simulations to predict thermodynamic properties for small molecules. They are collaborating with us to develop new versions of the Gromacs core and general Gromacs core development.
Snow lab, Colorado State University
The Snow lab at Colorado State has developed a new scientific core to run SHARPEN on Folding@home. These upcoming projects will test the performance of new algorithms for high-resolution protein structure prediction and also for the design of libraries of enzymes.
Sorin lab, CSULB
Eric Sorin’s group at CSULB uses simulation to study protein folding and other related areas. They are collaborating with us to develop new force field ports for Gromacs.
Voelz lab, Temple University
Vincent Voelz lab at Temple University’s Chemistry Department focuses on using transferrable, all-atom simulations for prediction and design of biomolecular dynamics and function. In particular, their interests include in silico prediction and design of proteins, peptide mimetics (e.g. peptoids), and binding sequences for cell signaling peptides.
Zagrovic lab, Mediterranean Institute for Life Sciences
Bojan Zagrovic’s lab at the Mediterranean Institute for Life Sciences in Croatia is interested in many related areas, including unstructured proteins and experimental structure refinement. His group is helping out Folding@home with new client development.