I've admired Prof. Ken Dill's work on molecular modeling and protein folding since the beginning of my PhD, so I went into his Saturday talk "Some cell behavior is encoded in proteome physics" with pretty high expectations. It surpassed those expectations by a wide margin!
He raised compelling arguments that the sequencing revolution calls for reinvigorating thermodynamic approaches to modeling molecular biophysics and protein science, along the lines of Tanford and Edsall. Thermodynamic models never went away, of course, and structural and molecular models offer insights not available by other methods. Nevertheless, I think it's significant for a pioneer in molecular modeling to advocate biophysical models that do not place protein structure near center stage.
To provide a little more detail, Prof. Dill's lab has focused in recent years on developing models that can predict protein properties across whole genomes. His talk addressed models for two specific influences on cell behavior, temperature and oxidation damage. In both sections, he illustrated that models based on thermodynamic properties (such as heat capacity) do well at explaining cell behavior and organism development, when applied to whole proteomes.
The key point: the talk wasn't about the proteome. It was about the properties of proteins--not as tabulated for large sets of specific, model proteins, but as revealed in distributions taken over proteomes. Ken highlighted this crucial change in perspective with a disarmingly fun metaphor. In describing his lab's work on modeling protein stability, he showed the formula they use, noting its simplicity and its use of a mere handful of parameters. "With this model, your iPhone can calculate a whole proteome in a second."
This stands in sharp contrast to the millions of supercomputer hours allocated via competitive grant applications, many proposing atomistic and quantum-mechanical studies of biomolecular processes. Many of us use these kinds of high-performance computing (HPC) facilities extensively, just as most research fields today do. For example, United States funding agencies such as the Department of Energy are committed to the path to exascale computing (10^18 floating point operations per second), which will provide simulation capabilities vital for national security. Massive calculations provide important insights and will continue to grow in importance and accuracy. Despite this fantastic scale, however, understanding the effects of climate change on the biosphere is not feasible using exhaustive molecular modeling, and never will be.
In this respect, Prof. Dill's talk has implications that are at least as important as his call for a renewed emphasis on non-structure-based thermodynamic models in molecular biophysics. Even relatively small temperature changes can be catastrophic for organisms (as he noted, 4 K is almost irrelevant in statistical physics, but it is everything in biology). Although large multicellular organisms have robust mechanisms to maintain homeostasis, the story might be very different for single-cell organisms, and thus for ecosystem response to climate change.
- Jay Bardhan