  Domain motions involved in the function of proteins can often be well
described as a combination of motions along a handfull of low-frequency modes,
that is, with the values of a few normal coordinates. This means that, when the
functional motion of a protein is unknown, it should prove possible to predict
it, since it amounts to guess a few values. However, without the help of
additional experimental data, using normal coordinates for generating accurate
conformers far away from the initial one is not so straightforward. To do so, a
new approach is proposed: instead of building conformers directly with the
values of a subset of normal coordinates, they are built in two steps, the
conformer built with normal coordinates being just used for defining a set of
distance constraints, the final conformer being built so as to match them. Note
that this approach amounts to transform the problem of generating accurate
protein conformers using normal coordinates into a better known one: the
distance-geometry problem, which is herein solved with the help of the ROSETTA
software. In the present study, this approach allowed to rebuild accurately six
large amplitude conformational changes, using at most six low-frequency normal
coordinates. As a consequence of the low-dimensionality of the corresponding
subspace, random exploration also proved enough for generating low-energy
conformers close to the known end-point of the conformational change of the LAO
binding protein, lysozyme T4 and adenylate kinase.
