Thousands of desktop computers working together in their spare time have resolved a long-standing biological puzzle, in a breakthrough in data processing, the British journal Nature reported yesterday. A team led by Vijay Pande of Stanford University in California put out a call two years ago for PC owners to make idle computers available to one hell of of a problem: how the atoms of a protein cause it to fold into a 3D knot. Understanding this could help drugs designers come up with molecules to attack Alzheimer’s and the human form of mad-cow disease, both of which are caused by misfolding, rogue proteins.
ABSOLUTE COMPARISON OF SIMULATED AND EXPERIMENTAL PROTEIN-FOLDING DYNAMICS
CHRISTOPHER D. SNOW*†, HOUBI NGUYEN†‡, VIJAY S. PANDE* & MARTIN GRUEBELE‡
* Biophysics Program and Department of Chemistry, Stanford University, Stanford, California 94305-5080, USA
‡ Departments of Chemistry and Physics, and Center for Biophysics and Computational Biology, University of Illinois, Urbana, Illinois 61801, USA
† These authors contributed equally to this work
Protein folding is difficult to simulate with classical molecular dynamics. Secondary structure motifs such as -helices and -hairpins can form in 0.1–10 µs (ref. 1), whereas small proteins have been shown to fold completely in tens of microseconds. The longest folding simulation to date is a single 1-µs simulation of the villin headpiece; however, such single runs may miss many features of the folding process as it is a heterogeneous reaction involving an ensemble of transition states. Here, we have used a distributed computing implementation to produce tens of thousands of 5–20-ns trajectories (700 µs) to simulate mutants of the designed mini-protein BBA5. The fast relaxation dynamics these predict were compared with the results of laser temperature-jump experiments. Our computational predictions are in excellent agreement with the experimentally determined mean folding times and equilibrium constants. The rapid folding of BBA5 is due to the swift formation of secondary structure. The convergence of experimentally and computationally accessible timescales will allow the comparison of absolute quantities characterizing in vitro and in silico (computed) protein folding.
Or, for those of a less technical inclination:
Stanford University scientists have shown that distributed computing, using thousands of low-end PCs, can have real results Scientists at Stanford University have demonstrated tangible proof that scientific experiments can be conducted using thousands of low-end PCs wrangled together into loosely linked networks. A group of chemists -- including Stanford assistant professor Vijay Pande -- said they successfully predicted the folding rate of a protein using calculations worked out on a so-called distributed computing network. Their research, conducted last year, was published this week in the science journal Nature.
In an interview, Pande said the demonstration was an important proof of concept for the use of distributed computing in the lab.