The goal of a domain analysis is a decomposition of the protein into regions with distinct dynamical properties. The kinds of regions that can be distinguished depend on the domain decomposition approach that is used; in general, the outcome of two domain decompositions using different approaches will not only be different, but not even directly comparable. It is thus important to understand the capabilities and limitations of each approach.
The techniques implemented in DomainFinder allow an identification of three types of regions in a protein:
The central parameter which allows a distinction between rigid and intermediate regions is the ``domain coarseness'' parameter. It specifies how similar the global motions in a region must be to be considered similar enough to form a domain (see Ref. 1 for details). However, this parameter does not have one specific ``best'' value which one should find in order to obtain the ``right'' domain decomposition. It is a parameter which should be varied, and the ensemble of results for several values of this parameter provides the information for identifying domains and intermediate regions.
In order to obtain the motion parameters necessary for the domain analysis, DomainFinder first divides the protein into small cubic regions containing on average six residues. For each cube, six motion parameters are calculated, three for translation and three for rotation. In case of a normal mode based analysis, there are six parameters per mode; this is one reason why a normal mode based analysis usually gives better results. The cubes are then grouped into domains according to the similarity of their motion parameters. For a single value of the domain coarseness, it is not possible to distinguish between rigid and intermediate regions; the word ``domain'' therefore refers to both type of regions in the program.
After choosing a value for the domain coarseness, select ``Show domains'' from the Domains menu. This causes a window to be opened which shows the domain decomposition for this coarseness level. In the top left, the protein structure is drawn with various regions indicated by colors. The list to the right of the structure display contains all these regions with their color and size. The order in the list is significant; the best-defined domains are listed first, and the last item(s) frequently contain cubes that do not really belong to any recognizable domain. The bottom picture shows a parallel-axis plot of the motion parameters for all cubes, color-coded by domain. In this plot, each line represents one cube, and each vertical axis one motion parameter. For well-defined domains, the lines belonging to the same domain (i.e. same color) should be very close, whereas lines belonging to different domains should be clearly separated. A wide band of lines indicates an intermediate region. The plot provides both a verification of the domain decomposition and a first impression of the nature of the domains. However, it should be interpreted with caution; the eye tends to consider two lines with small differences in all axes more similar than two lines which coincide in some axes but differ significantly in others, although from a mathematical point of view both situations are equivalent.
It should be noted that the residues shown in black do not belong to any domain, for one of the following reasons:
For more detailed information on a particular domain, click on that domain's entry in the domain list. This will open another window with information for this domain only. In the top left there is again the protein structure with just one domain highlighted. To its right, a list of all residues in the domain is shown. Below there is a parallel-axis plot showing only the cubes in this domain. Finally, there is an indication of the numerical similarity of the motion parameters within the domain. Two numerical similarity values are given, of which the first (larger) one is the similarity of the two most similar cubes, and the second (smaller) one is the similarity between this pair and the most different cube. The ratio between these two number is the domain coarseness which is necessary to consider the whole region as one domain. The small plot at the bottom shows one line per cube at the coarseness level required to keep that cube in the domain. You can use it to estimate the influence of a small change of coarseness on this domain: if there are many lines close to the current coarseness limit, the domain is likely to change significantly. Inversely, if the highest coarseness in the domain is clearly smaller than the current limit, a small variation will have no influence on the domain.
When you vary the domain coarseness limit, you will observe that some domains remain essentially the same, growing or shrinking only by small amounts and in response to significant coarseness variations, whereas others grow and shrink rapidly, or tend to break up into smaller parts as the coarseness limit is decreased. The first kind represents stable rigid regions, i.e. dynamical domains. The second kind represents intermediate regions. The parallel-axis plot at the bottom of the window helps in this classification by showing the variation of motion parameters within the domains at one glance.
Finally, DomainFinder lets you export the domain analysis results for
visualization and further computational analysis by using the
remaining entries in the Domains menu. ``Write domain list...''
writes a complete list of the domains and their residues to a text
file. ``Write PDB file...'' creates a PDB file of the protein
structure with the domain numbers coded in the ``occupancy'' field. A
value of zero indicates a residue outside any domain, other values
refer to the order of the domains in the domain list. ``Write VRML
file...'' provides a VRML version of the color-coded structure in the
domain window. ``Save in MMTK format...'' saves a Python dictionary in
the object format used by the Molecular Modeling Toolkit; this
dictionary contains entries for all variables of interest. The file
can be loaded with the MMTK function