The researchers also used their technique to successfully model numerous proteins whose structures were known. For many of these, they combined their computational analysis with data from experimental techniques. In the most dramatic test, however, they accurately predicted the three-dimensional shape of a protein based only on its string-like 112-unit amino acid sequence.
That is probably the most spectacular result in the paper, said Baker. In that case all we knew was the sequence of the protein; we had no NMR data and no related structures to base a model on. So given the sequence alone, we built models, and then chose the lowest-energy models, and they were very accurate. That was the first time it has been possible to take a globular protein structure and solve it without any additional experimental information.
The overall lesson of this paper is that protein structure prediction, at least for smaller proteins, is now good enough to generate more accurate models from experimental data such as from NMR, and for generating more accurate models based on other protein structures, said Baker. And in favorable cases you can get very accurate models starting from the sequence alone.
Whats more, said Baker, the project proved the scientific value of using massive numbers of individual computers to contribute to such computational efforts. The Rosetta@home project was not only scientifically invaluable, but enabled us to build a science education activity around it, said Baker. People got very interested in the calculations their computers were doing and were prompted to learn more about proteins in particular and molecular biology in general, he said.
|Contact: Jim Keeley|
Howard Hughes Medical Institute