OpenMM, Google Cell, and a marriage between informatics and simulation

Vijay Pande gave a talk today at the SimBIOS weekly seminar series here at Stanford. You may know him from such hits as “Folding@home“, which has gone almost triple platinum since it was first released. He is a major figure in the protein folding and molecular simulation world, so the talk was definitely well attended.

Vijay’s talk centered around a few major projects and themes, many of which I thought might be interesting to those in the Open Science world. The following are my summaries of those themes.

OpenMM – Open Molecular Mechanics
The molecular dynamics community is fragmented, with many different codes and software packages existing to do MD with overlapping functionality. A result of this is that different labs do things their own way, and new advances are adopted slowly because they must be ported into each set of codes. To address this, they are developing OpenMM, an extensible API for molecular mechanics that will, in principle, unify the MD community the way OpenGL did for graphics. If OpenMM is used as the back end to software applications, advances in theory or hardware will immediately translate to those applications.

Google Cell
Ok, this isn’t really what he called it, but it’s what I immediately thought of when he talked about their hopes to build a structural picture of an entire cell in atomic detail. I think he may have referred to the project under the name “AMOEBA”, since that is what they’re shooting for first. The idea is to use structural data from x-ray crystallography, cryoEM, and tomography, from which more and more high-quality data is being produced every day, and turn to physics-based simulation to fill in the details. This is a very high-level idea and I love it, if they can do it. And if they do… well, that made me think of a potential interface for it – Google Cell. Like Google Maps but for the cell instead of the Earth. Pan and zoom and click on interesting features, maybe even do searches! We’re definitely years away from it, but the possibilities are endless.

Simulation-aided drug design
Many approaches towards drug design involve docking of potential ligands to rigid crystal structures. But Jim Wells at UCSF has shown that proteins can undergo allosteric changes upon binding to different ligands. Simulation can allow docking with “induced fit”, providing a more realistic prediction of binding and possibly even affinity. There was a really cool animation that went along with that part of the talk, which will hopefully be posted soon.

Physics-based simulation is transferable
One of the good things about simulation is that it is transferable across many disciplines. Informatics, too, actually. But when data is scarce, it sometimes pays to borrow tools from other fields. The example Vijay used was that of the protein folding problem. We know relatively little about how proteins fold, but we do know a lot about chemistry and physics, which ultimately govern how proteins fold. So why not exploit our knowledge of chemistry and physics to try and learn more about protein folding? And that is precisely what molecular dynamics simulations do. More generally, transferability is a useful concept to keep in mind. In fact, one of the founding ideas behind SimBIOS is the idea that many diverse biological problems can be solved using the same tools and basic principles, one of them being physics-based simulation.

Informatics & Simulation, happily ever after?
An interesting observation Vijay made was that informaticians and simulation people have traditionally formed two separate, and sometimes antagonistic camps. Something like “physics-based simulation can’t teach us anything!” vs. “informatics is sloppy”. But in Vijay’s work, it is becoming evident that while both approaches have their strengths and weakness, much more can be accomplished when the two are combined. He used the analogy of trying to find a specific person in a large city. One could use a device that beeps when close to the target, but you could search all day without ever getting anywhere near the person. Instead, if you looked the person up in a phone book – used information, in other words – you could find the person’s house quite easily, and then the device would be extremely useful. Informatics is very good at getting in the ballpark, but sometimes suffers from being lower resolution. Simulation, on the other hand, can be very precise, but sometimes needs guidance or it will never find the solution. A partnership between informatics and simulation therefore seems not only powerful, but natural.

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