About four months ago, we started a new set of projects to understand the dynamics of some of the key proteins involved in vision. Now, we have about 600 microseconds of simulation and have begun some preliminary analysis of the data. Excitingly, it appears we may already have captured the conformational change we were targeting. More data will be needed to improve the statistical significance of our results, but we are increasingly confident that we’ll be able to begin understanding some of the conformational changes required for vision and, eventually , how mutations lead to various blinding diseases.
(Guest post by Kyle Beauchamp from the Chodera lab.)
In the Chodera lab, we’d like to understand how drugs bind to proteins, particularly for challenging diseases such as cancer or Alzheimer’s. To get to this point, however, will require a lot of hard work on simple systems—systems where we already “know the answer”.
T4 Lysozyme has been a key model system for understanding protein stability (Matthews, 2010). A version of T4 Lysozyme—with mutation L99A—binds a number of greasy molecules like benzene (see picture, PDBID 3DMX). Our hope is that a better understanding of how T4 Lysozyme L99A binds various molecules could lead us to better models for drug binding. (Mobley, 2007).
Project 10470 simulates T4 Lysozyme mutant L99A. These simulations will be used to improve models for ligand binding.
These are protein-modifying enzymes that catalyze the transfer of methyl groups to lysine or arginine residues as part of complex regulatory programs. While a number of cancers have alterations in protein methyltransferases, making them appealing targets for new anticancer therapeutics, it is not yet possible to fully understand their role in disease because of the current limited repertoire of compounds available to selectively inhibit these enzymes.
Spurred by recent encouraging results from the Luo lab in developing sinefungin scaffolds to selectively target key methyltransferases, we are working with them to better understand the origin of selectivity of these compounds, and to help them design new compounds that will allow researchers to better understand the roles these enzymes play in cancer and, eventually, develop potent new anticancer therapeutics.
Guest post by Sonya Hanson, postdoc in the Chodera lab.
We’re working our way through the kinase family here at the Chodera lab. You may have seen Danny’s post about EGFR earlier this year, and now we’ve started simulations of Abl kinase. Abl kinase has a special place in the history of cancer therapeutics, ‘dispelling the long-held myth that it was not feasible to develop selective inhibitors of key cell-signaling molecules as safe and effective medicines.’ Novartis’ development of the drug imatinib (or Gleevec commercially) to treat chronic myelogenous leukemia (CML) specifically targets a mutant Abl kinase that results from a chromosomal abnormality called ‘the Philadelphia chromosome’. There is even a book out recently that chronicles the development of Gleevec (The Philadelphia Chromosome).
While the success of imatinib was remarkable, many patients develop resistance to it and regress. A more recent drug targeting Abl kinase, ponatinib of Ariad Pharmaceuticals (Iclusig commercially), has been developed that overcomes some of these resistance mutations. However, now even ponatinib has been found to be susceptible to resistance mutants. With these simulations of Abl kinase, we are hoping to begin to understand a structural basis for the development of resistance mutants so we can develop drugs that anticipate and overcome them before the patient even has to experience regression. But to do this we will need many long timescale trajectories of Abl and later its mutants to achieve this deeper understanding of the development of resistance in CML. Additionally, this knowledge could inform models of resistance development in other cancers that result from kinase mutations or kinase up-regulation.
In a new NSF-funded collaboration, the Voelz Lab is working with the Roder Lab at Fox Chase Cancer Center to study early folding events in apomyoglobin.
Apomyoglobin (myoglobin without the heme group) is an extremely well-studied protein. In fact, mygolobin was the first protein to have its structure solved by x-ray crystallography (John Kendrew, 1958). At low pH, apomyoglobin assumes a “molten globule” state that is compact and only partially structured. Seminal experiments by Jennings and Wright (1993) showed that when apomyoglobin folds at normal pH, it goes through an early intermediate that closely corresponds to the low-pH molten globule state.
Now, more recent experiments from the Roder lab have revealed even more details of early folding events in myoglobin (Xu et al. 2012). Using Trp fluorescence spectroscopy in a continuous-flow fast mixer, the Roder lab have resolved the formation of up to four different conformational states, on timescales ranging from microseconds to milliseconds.
The Voelz Lab is working toward using molecular simulation to characterize these conformational states in atomic detail. Both the size of the protein (153 residues) and the timescale of early folding (~200 µs) make this a challenging problem to tackle, but we hope that simulations on Folding@home (coming soon!) combined with Markov State Model approaches will enable us to construct a highly detailed model of the early folding reaction, and new level of quantitative connection between simulations and experiments. In the years to come, this work will lead to new ways to combine computation and experiment to understand and fight human diseases.
The Voelz lab has been making progress on combining simulation and experiments to solve molecular structures.
Most molecules do not have a single rigid structure in solvent. Instead, they exist in a range of different conformations. Stable proteins exist mostly in the folded conformation, but there is always a small fraction of population that is unfolded. Other molecules may have a very heterogeneous set of conformations, which can make determining their structure in solvent difficult. NMR experiments, for example, can be used for this, but structural information often gets “washed out” due to motional averaging.
Our new method, called BICePs (Bayesian inference of conformational populations) is a robust method to infer the populations of conformational states, using a combination of high-resolution computer modeling and information from experiments. We think BICePs will be very useful for determining the extent to which proteins and other molecules are well-structured in solution. In the future we plan to use it as a tool for designing well-structured mimics of proteins, called peptidomimetics. Our paper describing the new BICePs algorithm has been published in the latest issue of the Journal of Computational Chemistry.
fah-web.stanford.edu going down at 1pm pacific time for planned maintenance. We expect it will be down for about 30 minutes.
The Native Client WUs are down, we’re working on it. This one may take a day or two for us to resolve. We’ll keep donors posted as we know more.
With the newer NVIDIA drivers, it looks like Core17 works well on Maxwell. We’ve released Core17 to Maxwell on adv (“Advanced Methods” setting). If you’re having problems, you can set from adv back to the regular fah setting, allowing donors to opt out if they’re having problems. The latest drivers are recommended.
Starting this week, I am adding an additional role to my work at Stanford (currently Director of Biophysics; Professor of Chemistry, and by courtesy, Computer Science, and Structural Biology; and Director of Folding@home) by doing consulting for Andreessen Horowitz (aka “a16z”), a venture capital firm with the goal of supporting companies with an emphasis on software making the difference. Just as the web browser has changed the world, they are looking to support new ideas with a similar transformative character. My role will be to be a conduit between academia and venture capital, to help good ideas at Universities to get the funding they need to create new products to help the world. I’d like to stress that the Folding@home team is continuing full steam and that this new role has the hope for improved funding for activities at Stanford and for academic researchers in general.