Help us reach one million folders on Folding@home!

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Our lab is a core member of the Folding@home Consortium, a research network of 11 laboratories around the world that use Folding@home to study the molecular mechanisms underlying cancer and other diseases and identify new routes toward therapies. Together, we are aiming to recruit one million volunteers donating compute cycles to help us! 

Please join us, especially if you have a GPU: Folding@home can harness the power of your GPU. 
It costs nothing (other than your electrical bill) and provides a way to donate your idle computer cycles to biomedical research.

Download Folding@home now

 

Other useful links:

Fourth Alchemical Free Energy Calculations in Drug Design meeting at Vertex

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The fourth Alchemical Free Energy Calculations in Drug Discovery workshop is already underway, held once again at the beautiful Vertex facility in Boston. We're livetweeting the meeting for those who can't make it, and have set up a new Slack team to keep the conversation going after the meeting. There's even a job postings page to keep track of the abundant new jobs in computational chemistry and alchemical free energy calculations in industry and academia this field has created. We're thrilled to see so much activity, how far the field is come, and certainly how far the field has left to go.

Slides from my talk can be found here in PDF format.

ICMS Edinburgh Workshop on multiscale methods for stochastic dynamical systems in biology

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The International Centre for Mathematical Sciences (ICMS) held a fantastic workshop over the last week here in Edinburgh, covering multiscale methods for stochastic dynamical systems in biology.  It's phenomenal that there are organizations that are strongly committed to supporting the exciting interface between mathematics and the biological sciences, and the enthusiastic discussions at this meeting were a reflection of the enormous potential that work at this interface holds for both fields. These workshops also attempt to engage the public to communicate the importance of this interdisciplinary work through a public lecture series, with Sarah A. Harris delivering a talk on the interface of physics and biology.

PDF slides from my talk are available online, and the talks were all recorded to be posted online shortly.

CECAM Workshop on developing interoperable and portable molecular simulation software libraries

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I'm excited to be participating in the CECAM Workshop on Developing Interoperable and Portable Molecular Simulation  Software Libraries this week at the Forschungszentrum Jülich, organized by Julien Michel, along with Christopher Woods, Peter Eastman, and Gareth Tribello.

Slides and materials used during the workshop can be found here:

  • Examples and materials: https://github.com/choderalab/cecam-2015-julich-workshop

Posters from the Computer Aided Drug Discovery (CADD) GRC

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Several members of the lab (John Chodera, Sonya Hanson, Patrick Grinaway, and Julie Behr) are here at Mount Snow, VT for the Computer Aided Drug Discovery Gordon Research Conference (CADD GRC).

A number of folks have asked for copies of our posters, so here they are!

 

Science Communication Boot Camp: The Experience

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Guest blog post from postdoc Sonya Hanson.

Earlier this month, I went to Science Communication Boot Camp. It was at the 'Alan Alda Center for Communicating Science' at Stony Brook University. We did not get to meet Alan Alda. That was disappointing. But everything else was really, super awesome. We played a lot of improv games, we did a lot of woodshedding explaining our own science, we learned about how to make stories, we learned about metaphors, and at the end we taped three-minute mock media interviews and talks to try and implement what we had learned throughout the week. It was exhausting, it was embarrassing, it was hard, but it was a blast.

Baseball

I first realized we were someplace special when we started talking about baseball. Now, I am not a baseball person, but that's okay: I know enough. But in the beginning of the first day, they asked us to explain the following to someone who knew nothing about baseball:

'In the bottom of the ninth, Jeter worked a one-out walk and stole second. But the Red Sox's ace reliever got Ellsbury and Teixeira to strike out swinging to end the game.'

And it was super hard. We started explaining baseball: there are three bases, there are these things called outs, you get a point by... That just wasn't working. And then... they put up this explanation:

'The game was almost over, and the home team was losing to its most hated rival. The beloved captain of the home team, playing in his last season, made a last-ditch effort to win. He took a big risk, and it looked like it might pay off. But when his teammates tried to help him score, a key player on the other team shut them down. The game ended, and the home team went down to bitter defeat.'

Suddenly you understand the stakes. Suddenly you understand why someone might care about baseball.

Yes, and...

Don't worry, the rest of the camp was not about baseball. Time-wise, the plurality of the boot camp was spent doing improv games. Why, you may ask? What do science and improv have in common? Why would you do improv, where the whole point is that you're just making stuff up, to become a better scientist, where the whole point is precisely that you do not just make stuff up? Because improv is about connecting. Improv is about 'Yes, and..'

Not 'No, but...', not 'Yes, but...', but 'Yes, and...'. Agree and add something. Find how to connect to someone and then add to that. At the beginning the improv games were not science related. Zip-zap-zop, the mirror game, the ball game, the positive side of ranting, etc. My hypothesis is that they're to get us talking. To get us comfortable with talking about anything, anything at all. To find our own rhythm, and try to connect that rhythm to whoever you're communicating with. In one of the most powerful games, we were told to take a blank piece of paper and describe a picture. It was intense. Almost everyone talked about something deeply personal. A picture of an important family space or pet that got you through hard times. There were no instructions to 'do your best to make everyone cry', but somehow that's what happened. And this was all without any preparation. Somehow, we already had these stories inside of us, but how could we use them *dun dun dun* FOR SCIENCE?

STORIES, DISTILLING, AND METAPHORS

The first night, guest speaker Carl Safina told us about (among other things) his 'Spray can theory of science communication': he used to be pissed off that you buy a can of spray paint and it's only 2% paint, but then he realized that the paint is no good without the 98% propellant. The story is the 98% propellant. The science is the 2%. Sorry, guys. That means it pays to find that 2% of your science that is really what you want to communicate. Unsurprisingly a lot of what we learned when we weren't doing improv was how to 'distill our message'. We would go around in small break-out sessions and have one minute to describe our work. One of the first things I learned was, "bring cancer up front". Apparently in my first run-through I left it to the very last sentence. Another was "tell them what you're going to tell them, tell them, then tell them what you told them". I think this is more powerful than it seems. It forces you to decide what you're going to say, and decide your goal instead of just rambling off a laundry list of facts that probably don't mean anything to the person you're talking to, anyway.  Relatedly, getting rid of jargon was surprisingly difficult. In retrospect, it shouldn't have been surprising, but also I now feel bad for all the people I have explained my science to in the past that had to deal with all those meaningless (to them) words. If anything came out of this camp, I hope I am now better at recognizing when this when it happens.

One of the things that helped a lot in explaining more difficult concepts was using any kind of comparison to a real life thing. Why do we care about semiconductors? How small is an atom? What analogies can you make to other complex, but more commonplace things? Coming up with these kinds of comparisons, I think, is often scary for scientists: we don't want to lose reality in an imperfect analogy. But actually, having any kind of reality to compare to is surprisingly helpful when your other option is just an abstract concept... and you only have three minutes to get your point across. We even did a game to explore how easy it could be to find everyday things to relate to scientific topics: everyone writes down a scientific topic on a piece of paper and puts it in a pile; everyone puts a random object from their backpack or purse in the pile; pick four of each. Surprisingly, after mixing and matching, it is not hard to find reasonable pairs: swiss army knife and adaptive evolution, broken retractable badge holder and the RNA folding problem, sunglasses case and protecting DNA in epigenetics, headphones and being desensitized to the song 'Happy' for antibiotic resistance, etc. The hardest part, it turned out, was not pairing a scientific concept to an every day object, but telling a story around it.

The media interview

So that was a lot of fun. We got a bit better at improv and finding our rhythm. We got a bit better at distilling our scientific message. But then they got out the big guns: time to record it. This was definitely invigorating, partly because it was actually at the Stony Brook journalism school where they had real lighting and real cameras, and the Alda Center brought in some totally legit interviewers (they were kind of a big deal: googling Marcy McGinnis or Rory O'Connor is a good way to misplace a few hours of your day). It was also pretty nerve-wracking. I for one felt like an idiot because I was wearing a black shirt in front of a black background. What a noob! See my little interview below for your viewing pleasure:

Eh? If you liked that, it is probably just because I've heard my boss say those same things over and over and over again... Also I did do a bit of (extremely professional) editing and cut out some parts I messed up. Anyway, it's certainly not perfect, but maybe I managed to put into practice the bits and bobs mentioned above. Hopefully, now you know more about what the Chodera lab does!

Closing remarks

One of my favorite things about all this is that from these short (1-3 minute) talks, I now understand the science of other boot campers  better than the work of scientists I've seen talk for 15 minutes or more at conferences! While this is great, there are still things that bother me about a lot of these techniques of communicating science. What if what you want to communicate is the history of the material of the bases and how that has had an impact on the game of baseball. Something smaller, something that is harder to make seem important. I think most of us were able to make our science personal by talking about how it effects human health, and I would have loved to see more diverse ways of making science personal. I would have loved to take a crack at explaining why we care about the Higgs boson.

This hits on another issue we ran into several times during the course: we don't want to oversimplify. And I think we didn't cover how far is too far very well. On the first day we picked an abstract of someone in the course (Hi, Tali!) to explain to a lay audience: a study on Archer fish (the link is a sweet NYTimes ScienceTake video) that seemed to indicate that fish could do conflict resolution even though they don't have a brain with a prefrontal cortex, which is where humans and mammals do conflict resolution (while this particular abstract is not published yet, here's a link to a related study from the same lab/author). The resulting impression the audience had was: "This research says that an injury to the decision-making section of the brain, may be curable." This made the abstract's author cringe. To me this is exactly what we want to avoid, and I think I am still a little scared of this, and as a result might still fall into jargon sometimes when I explain my research because my instinct is that it is better to communicate poorly than to communicate wrongly. Ideally, we wouldn't do either. 

These are some of the more complex nuances that I don't think we quite got to cover and clarify in the class. But that's fine. No one said this was going to be easy. One of the most important things we learned, I think, is that we all have our own rhythm and our own stories and that tapping into those is all we need to communicate science effectively. We don't all need to be Bill Nye.

Automatic equilibration detection manuscript on bioRxiv

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In molecular simulations---especially simulations of complex systems like biomolecules---it's incredibly difficult to start the simulation close enough to equilibrium to avoid initial transients in properties of interest.  As a result, it is almost universally recommended that some initial portion of the simulation be discarded to "equilibration".  Unfortunately, there hasn't been a simple, automated, and generally applicable way to do this that is standard practice in the field.

In a new manuscript draft posted to bioRxiv this morning, I show how an amazingly simple approach---simply maximizing the number of statistically uncorrelated samples in the latter part of the simulation---can lead to a surprisingly robust and useful algorithm for equilibration detection.  This is very much a work in progress, so comments and feedback is very much appreciated!

DOI: http://dx.doi.org/10.1101/021659

All code needed to grab the exact versions of the tools I used (using the conda package installer and the omnia molecular simulation suite), generate the simulation data, analyze it, and generate the figures for the paper is available on GitHub: You simply need to run

./reproduce.sh

to regenerate everything---which is exactly what I did to generate the figures in the posted version of the manuscript.  There are still a few improvements I hope to make the scripts easier to read and the data easier to deal with, but hopefully we can try to attain this level of ultra-simple reproducibility in future work as well.

Update [5 July 2015]: The manuscript has been updated based on valuable feedback I've already received!  Thanks to everyone who has made comments!

Postdoc Sonya Hanson participates in inaugural Alan Alda Center for Communicating Science Summer Bootcamp

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We are beyond thrilled to congratulate Chodera lab postdoc Sonya Hanson for being accepted into the inaugural Scientific Communication Summer Bootcamp at the Alan Alda Center for Communicating Science!  The Center, founded by the veteran director/actor/writer Alan Alda (M*A*S*H, The West Wing, QED, and PBS' Scientific American Frontiers), strives to enhance public understanding of science by working with young scientists and health professionals to develop effective skills for disseminating and communicating science.

The Bootcamp is an intensive week-long program designed to aid scientists in honing their ability to communicate clearly with the public in a multitude of forums, training that is essential for supporting a healthy scientific enterprise in the United State, but which is far too often overlooked in science training programs.  For those interested in what an intensive training program in science communication can look like, the Alda Center has posted the full Bootcamp program agenda online. 

Sonya has been engaged in scientific communication and outreach activities throughout her career.  She is a former editor of the Oxbridge Biotech Roundtable, maintains an active twitter feed and science blog covering a newsworthy science-related topics, blogs about her research into anticancer therapeutics on the Folding@home blog, was recently a featured guest blogger for the Biophysical Society, and a champion of open source salads.

Stay tuned for more from Sonya on her experiences at the Science Communication Summer Bootcamp.

Automated forcefield benchmarking manuscript on arXiv

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Molecular mechanics forcefields are an integral part of molecular simulation.  The quality of any properties computed from molecular simulations is wholly dependent on the quality of the underlying forcefield.  Quantifying how well the forcefields we use can reproduce various physical properties provides insight into expected accuracy in other properties of interest, deficiencies in the forcefield parameters or functional form, and strategies for making systematic improvements.

In a new manuscript posted to arXiv ahead of submission, postdoc Kyle Beauchamp tackles one of the most critical issues in forcefield validation: Most of the physical property information one would like to benchmark against is tied up, inaccessible, in paper databases (also known as "books" or "journal articles").  Using the ThermoML Archive from NIST TRC headed by Kenneth Kroenlein (a coauthor on the paper), Kyle is able to show that this data the computer-readable data stored in this archive in the IUPAC-standard XML-based ThermoML format contains a wealth of information useful for automated validation (and eventually parameterization) of molecular mechanics forcefields.

As usual, all code used in the production of this manuscript is made available through GitHub. The code make use of the excellent OpenEye Toolkit, which is available free for academic use that will generate data for the public domain; the GPU-accelerated OpenMM toolkit, and the free AmberTools distribution.

Kyle A. Beauchamp, Julie M. Behr, Ariën S. Rustenburg, Christopher I. Bayly, Kenneth Kroenlein, and John D. Chodera.
Preprint ahead of submission: [arXiv] [PDF] [GitHub]

OpenEye JCUP VI talk slides

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I'm thrilled to have the opportunity to speak to a distinguished collection of scientists from pharma and academia here in Tokyo at the OpenEye JCUP VI drug discovery meeting.

I thought it might help to post a PDF copy of my slides where I discuss how experiments and theory can work together to gain insight into how to improve quantitative predictive models for drug discovery.