Their reasoning was that it takes time to post, time needed to fix things. Would you rather get a note saying 'we are down' or the project up and running was their thinking. They said 'you KNOW we are down'!

As a quick first answer: Protein simulation is presently dominated by two approaches.

1) You learn/copy from nature (ROSETTA@HOME is a good example). Given a new sequence, one tries to copy structural fragments of known proteins and assembles them to good structures. Pros: Works for big proteins, gives excellent structures for high sequence similarity. Cons: Does not work, when there is no sequence similarity (new folds), does not work when there is no experimental data (transmembrane proteins, to which 40% of all known drugs bind), no kinetics

2) You simulate the folding process as it occurs in nature (Folding@Home) with a biophysical model. This is done with molecular dynamics (MD), an essentially sequential method with a time resolution of 10E-15 s/step. For a folding process in millisecond range you need A LOT of steps. Pros: full dynamics info, folding times, high accuracy structures, Cons: works only for small proteins, specific questions

POEM tries to interpolate between these two worlds. It uses an atomistic model for the protein free energy, i.e. is can work for new folds and applications in nanobiotechnology, where there is no experimental data. In contrast to MD, it exploits Anfinsons thermodynamic hypothesis (Nobel Prize in Chemistry 1972) that proteins in their biologically active state have a minimal free energy. The simulation process is thus replaced by an optimization process that is thousands of times faster than MD. Pros: Can do at least medium size proteins, gets the folding landscape, works for "new folds", Cons: still limited to proteins < 100 amino acids, no real kinetics (yet).

With POEM@HOME we will try to make progress on these two cons. Specifically we hope to make progress for


* "new fold" proteins with low sequence similarity to existing proteins
* proteins in nonphysiological environments (we just got a grant to develop implants, which are more biocompatible)
* understanding the folding process of more complex proteins that cannot be studied with direct kinetic simulation
* protein-protein interactions, where the partners change their conformation upon docking (biological signal process)
* refinement of model structures for transmembrane proteins