SuperLooper

FAQ


1. How can I cite this website?

Peter W. Hildebrand, Andrean Goede, Raphael Bauer, Elke Michalsky, Jochen Ismer, Bjoern Gruening and Robert Preissner:
SuperLooper - A prediction server for the modeling of loops in globular and membrane proteins

2. What is the method of SuperLooper?

SuperLooper is a comparative modeling method, specialized for the tertiary structure prediction of membrane protein loops. Proposed loop candidates are loops or segments taken from known membrane protein structures or water soluble globular protein structures, respectively.

3. How are the loop candidates scored?

Loops candidates taken from membrane protein loops (LIMP) are always listed first, even if the score for those taken from the much larger data base of segments of water soluble proteins (LIP) maybe significantly higher. Up to 50 loop candidates are ranked in both lists according to the score, that is a measure for the sequence and the geometrical fit of the candidate.

4. How reliable are the loop predictions?

Searching LIP, the average global RMSD of the respective top ranked loops to the original loops is benchmarked to be less than 2 Å, for loops up to 6 residues or less than 3 Å for loops shorter than 10 residues. Other suitable conformations may be selected and directly visualized on the web server from a top fifty list. For user guidance, the sequence homology between the template and the original sequence, proline or glycine exchanges or close contacts between a loop candidate and the remainder of the protein are denoted. Generally, the reliability of the predictions decreases with increasing loop length. Nevertheless, we decided to include loops up to 35 residues length in our databases, as otherwise modelling of many membrane proteins would not be possible at all. Regardless of the loop length, we strongly recommend to proceed with energy minimisation or molecular dynamics after inserting a loop, in order to optimise and refine the structure proposed by SuperLooper.

5. What purpose are the membrane planes useful for?

Membrane proteins loops are normally positioned within or above the polar head groups of the lipid bilayer. The membrane planes calculated with help of the algorithm TMDET kindly provided by [2] indicate that transition from the lipophilic to the polar milieu. We recommend the user of SuperLooper to select those loop conformations, where the majority of the hydrophobic or polar loop residues are placed below or above the membrane plane, respectively.If you decide to use this feature, please activate hydropathy in the display options, placed below the Jmol window. Hydrophobic residues are colored green.

6. Does the numbering of the amino acids change when a new loop is inserted?

The numbers of those amino acids outside the region treated with SuperLooper are not changed at all. If a built-in loop is longer than the original segment the terminal amino acid numbers will be supplemented with characters, resulting in amino acid numbers like 34A or 35B, for instance.

7. What can I do if no loop was found?

Several reasons are possible when no suitable loop candidate is found: Possibly, your loop sequence is too short to bridge the distance between the chosen stem residues. It might also be the case that the stem residues are too close, such that no loop can be inserted in a reasonable way.
In both cases you can use the buttons "enlarge N-term" and "enlarge C-term". Using these, the N-terminal and C-terminal stem residues, respectively, are automatically shifted (in N-terminal / C-terminal direction) and the previous stem residue is added to the loop sequence.

8. There are some benchmarks that rate the SuperLooper application. But when I try to reproduce the examples I get other results!

There are some differences between benchmarks and Web application:
  1. For the benchmarks, the original proteins (i.e. those proteins the test set loops were extracted from) were excluded from LIP
    --> the Web application will yield better results, as these proteins are included here and thus the original loop will be found as best candidate.
  2. The size of the Web version of LIP was reduced by sorting out candidates with identical sequences and similar backbone conformations (RMSD < 1.0 Å)
    --> as the case may be, the Web tool will yield slightly worse results than the benchmark, because the benchmark version considers more loop candidates.
  3. Clashing loops are without influence in the benchmark test, although they are listed by the Web tool.

9. What rules should I follow to select the right looop candidate?

Proper loops may be chosen by visual inspection of the candidates in the viewer, the alignment information provided in a box below the 3D view and by the considering the following guidelines:
  1. High scores (see: Interactive Search, SuperLooper, 3.) indicate good loop candidates.
  2. The less the RMSD_stem, the better the loop.
  3. Moreover, RMSD_stem should not exceed 0.3 Å, to ensure proper continuity of the polypeptide chain.
  4. The minimal distance should not fall below 2.4 Å, to avoid clashes.
  5. If possible, the maximal distance should not exceed the cutoff value given in the tutorial section, as the loop should not protrude too far from the protein surface.
  6. For membrane proteins, please check the position of the loop relative to the membrane planes.
  7. Avoid loop amino acid sequences with false prolines or glycines (see tutorial section) in order to avoid incorrect backbone torsion angles.

10. Is the availability of a homologous loop template a critical point in loop prediction?

The performance of knowledge based methods such as SuperLooper clearly depends on the availability of homologous loop templates and thus the size and actuality of the data base in use. This, however, is presently not always the case for longer loops (>10 residues). The sequence identities between the target and the template sequence are therefore denoted. Nevertheless, when highly homologous structures (sequence identity > 90%) are excluded SuperLooper still performs well, compared to four other state of the art methods [Rossi et al. (2007)] [7]. At 90% homology cut-off level, SuperLooper performs best for 9 of 14 eleven residue long loops, or for 3 of 10 twelve residue long loops. At 30% homology cut-off level, our method still yields best results for 4 of 14 eleven residue long loops. More detailed data on the benchmarks of SuperLooper are available from our website.