Frequently Asked Questions
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Troubleshooting
How long are computed results available and stored?
All jobs computed on the Freiburg Tools webserver are stored for 30 days. Afterwards they are automatically removed. In order to preserve your job results you might want to use the job zipfile that is offered for each job on the according result page. This file contains all input information, call details and output files generated.
What publications to cite when using the server?
Each tool offered by this web server comes with a specific list of publications. Please cite them when using the web server.
Why do I get a warning saying that my browser has incompatibility issues with the web server?
The Freiburg Tools web server is known to have serious compatibility issues with Internet Explorer. If you are using this browser to access it, we highly recommend switching to Mozilla Firefox so that you don't experience lack of functionality.
The web server was also tested under Google Chrome, Opera, Safari and Konqueror and they are all known to work fine.
If you are not using Internet Explorer and you still get the warning message, then browser mimicry could be the reason. Please let us know and we will correct the problem as soon as possible.
Who to contact if I have further trouble or encounter problems not listed here?
Please
contact us as soon as you encounter any problems or difficulties. If you have problems with a specific tool please send as much detail as possible. In case your problems are related to a certain jobs, please provide the job ID etc. Thanks for your help and feedback!
I have created a cool new RNA Bioinformatics tool, is it possible to integrate it into the web server?
The Freiburg Tools web server is a very flexible and generic platform to integrate new tools. So please
contact us and we will happily discuss the possibilities of an integration of YOUR TOOL into our web server.
General questions
What are lattice proteins?
Lattice proteins represent a class of protein models that restrict the
conformation/structure space of the represented proteins. This is done
in two ways. First, all model atoms are confined to nodes of a discrete
lattice. Thus, a discretization of the structure space is achieved that
enables a full enumeration of all structures a modeled protein can
adopt. Furthermore, lattice protein models represent amino acids with
one or a few model atoms (monomers) instead of the real number of
different atoms like the C_alpha, H, etc. By that a further restriction
and simplication of the structure space is obtained.


Backbone

Sidechain

Two common forms of lattice proteins are
backbone and
side
chain models. The first represents each amino acid by one monomer
only. Thus a selfavoiding consecutive chain of monomers in the lattice
yields a valid structure. In side chain models each amino acid is
represented by two monomers: one for the backbone atoms and one for the
atoms that form the side chain. This yields a more realistic model at
the cost of increased computational complexity and structure space. For
an example see the picture below. It shows a side chain lattice protein
(thick balls and sticks) and the modeled full atom amino acids (thin
lines). A backbone model would consist of the blue balls and sticks
only. The monomers are placed in an FCClattice.
What is the HPmodel?
The HPmodel was invented by Kit F. Lau and Ken A. Dill to model
hydrophic forces that are known to be a driving force in a protein's
folding process. First defined on the 2Dsquare lattice it is applicable
and used in various lattices and even in offlattice models. In the easiest
form it is a backbone model (i.e. one monomer per amino acid) but also
side chain models are possible. The model only represents two groups of
amino acids : (H)ydrophobic and (P)olar ones. To determine the
energy of a protein structure hydrophobic contacts are considered only.
Thus the number of HHmonomer interactions are counted, excluding
consecutive ones along the chain. Two monomers interact if they occupy
neighboring positions in the lattice, adding an energy gain of 1.
For a 2D example including energy calculation see the following link.
What does 'unrestricted' lattice model mean?
Often lattice protein studies are restricted to compact
structures only. Such structures completely fill a cuboid in the lattice
and yield to a futher restriction of structure space. Unfortunatly,
such restrictions of structure space bias the studies while reducing
the computational complexity.
CPSPtools do not use this restriction and utilize the full
unrestricted structure space of the protein in the lattice
model. In short we use the term unrestricted lattice
model to distinguish these studies from e.g. the cuboid confined ones.
How are the lattice neighboring vectors defined?
The simplest 2D square lattice is defined by 4 rectangular neighboring
positions/vectors. Despite of its crude protein structure representation
it is widely used in HP lattice protein studies.
The 3D cubic lattice with 6 neighboring vectors is also widely used.
It shows, as the 2D square lattice, the
parity problem.
The 3D face centered cubic (FCC) lattice is defined by 12 neighboring
vectors.
The FCC lattice was shown to allow for the best protein structure
approximations in a lattice.
For the list of neighboring vectors see the
absolute move description.
What are 'absolute move' strings?
Absolute move strings are a compressed string representation of
structures in lattice protein models. Here the lattice specific
neighboring vectors between successive monomers are described instead
of their exact coordinates.
Thus all possible vectors are uniquely encoded. Their number
depends on the lattice. Encodings used by the CPSPtools:

Vector 
Move 

Vector 
Move 

Vector 
Move 
2Dsquare 
(+1,0,0) 
F 
3Dcubic 
(+1,0,0) 
F 
3DFCC 
(+1,+1,0) 
FR 

(1,0,0) 
B 

(1,0,0) 
B 

(+1,1,0) 
FL 

(0,+1,0) 
R 

(0,+1,0) 
R 

(1,+1,0) 
BR 

(0,1,0) 
L 

(0,1,0) 
L 

(1,1,0) 
BL 




(0,0,+1) 
U 

(+1,0,+1) 
FU 




(0,0,1) 
D 

(+1,0,1) 
FD 







(1,0,+1) 
BU 







(1,0,1) 
BD 







(0,+1,+1) 
RU 







(0,+1,1) 
RD 







(0,1,+1) 
LU 







(0,1,1) 
LD 
Note that we use a twoletter encoding in the facecenteredcubic (FCC)
lattice. This was done to allow for an intuitive readable notation
since all neighboring vectors in FCC are a combination of two standard
3Dcubic directions. The encoding follows the description in the order
of X, Y, Zchanges to get a unique encoding.
What is the parity problem?
The 2D square and the 3D cubic lattice allow only for 180º or
90º angles between
successive monomers (see lattices). This leads to
a lattice based restriction of possible contacts between monomers of the
protein chain. Caused by the rightangles only monomers with different
parity in sequence position can make contacts. Monomers with equal
parity can never be neighbored even if they are at the opposite ends of
the chain. This is known as the
parity problem.
The figure below illustrates the problem. Lattice positions with even
coordinate sum are given in blue, odd ones in green. Due to the
selfavoidance and connectivity constraints on the chain it can only
be placed on iterating blue and green positions. As shown by the figure,
no two green or blue nodes are neighbored according to the
neighboring vectors in 2Dsquare or 3Dcubic.
What is the degeneracy of a lattice protein sequence?
The degeneracy of a lattice protein sequence is the number of optimal
structures the sequence can adopt. This number can be immense in the
HPmodel due to the simple energy function. Here, the Pmonomers have
no energy contribution and their placement is are not much constrained.
For example have a look at the sequence HHPPPP. All possible structures
are optimal structures with energy 0. But there are a lot (depending on
the underlying lattice) due to the long 'Ptail'.
What is the CPSPapproach and what does 'CPSP' stand for?
CPSP stands for 'Constraintbased Protein Structure Prediction' and is
the first complete and exact approach to predict all optimal structures
in the 3Dcubic HPmodel and was extended to the 3DFCC lattice as well.
It is based on the observation that optimal structures show an (almost)
optimal packing of their Hmonomers in 3Dlattices. Thus a database of
such (sub)optimal packings, so called
Hcores is
precalculated. These cores are used in the final step to formulate
CSPs, Constraint Satisfaction Problems. Utilizing the powerful methods
of Constraint Programming, the CPSPapproach solves these problems and
is capable of predicting all optimal structures of a given HPsequence.
The CPSP approach follows for an HP sequence with
k H monomers
a workflow as sketched in the following cartoon:
For a detailed description of the method please see the
publication by Backofen and Will (2006) or check the
introductory slides [pdf].
Is there an offline version of the CPSPtools?
The CPSPtools are available as an open source package for local installation and usage at
http://www.bioinf.unifreiburg.de/sw/cpsp/.
The package is provided as C++ source code package including standard
GNU automake and configure scripts.
What is an Hcore?
Within the CPSPapproach we use the term
Hcore to describe the
set of all Hmonomer positions an HPprotein structure adopts in the
lattice. Thus no sequencedependent connectivity information is present.
Structure vs. Hcore
An
optimal Hcore is a maximally compact set of lattice
positions allowing for the maximal number of contacts between these
points. For example, in the 2Dsquare lattice the optimal Hcore of
size 4 are the edges of a square. Here it is the only one, but usually
their number if (much) higher.
A
suboptimal Hcore is analogously a set of positions with a
number of contacts below the maximum. These core are usually still very
compact and connected.
Note: Hcores are
latticespecific!
We recursively define the
level of suboptimality of Hcores:
 level 0 : optimal Hcores with maximal number of contacts
 level i : Hcores with the maximal number of contacts that is
less than the contacts of cores in level (i1)
Note: The number of contacts represented by consecutive levels is not
necessarily consecutive as well!
The calculation of optimal and suboptimal Hcores (as needed for the
CPSPapproach) is a hard computational problem on its own and relates
to the densest packing of spheres in a lattice. It can be solved within
the 3Dcubic and 3DFCC lattice using Constraint Programming techniques.
(See Backofen and Will,
Optimally compact finite sphere packings  hydrophobic cores in the FCC, 2001).
HPstruct
Why does HPstruct predicts less structures than requested?
Usually the degeneracy of a sequence in the HPmodel
is very high. Therefore, we would like to restrict the number of
calculated optimal structures.
In case the degeneracy is below the given threshold, HPstruct will
calculate all optimal structures, displayed in the list. Thus, the list
is shorter but complete.
Why does HPstruct sometimes fail to predict optimal structures?
The HPstruct approach is based on a precalculated database of optimally
and suboptimally dense packed Hmonomer distributions, so called
Hcores (see FAQs).
Currently we have computed a large number of these Hcores
for several levels of suboptimality (see Hcores).
They can be used for up to about 60 Hmonomers.
The current set is sufficient for more than 90% of HPsequences up to
a length of a hundred or more monomers (depending on the number of Hs in the
sequence). Some rare sequences have, due to special sequence
properties, a sparse Hmonomer distribution in their optimal
structures. In such cases the current Hcore database is not sufficient
(not enough levels of suboptimality) to predict these structures. This
can be solved by extending the database for the problematic
Hmonomer number, with one or two additional levels of suboptimal Hcores.
Thus the failure to predict an optimal structure is not a bug nor
an inconsistency of the CPSPapproach. It is a limitation of
the currently available Hcore database.
Why HPstruct allows only for a restricted number of Hmonomers in the sequence?
As described above, the CPSP approach is based
on a precalculated database of Hcores.
Therefore, HPstruct can only handle sequences where corresponding
Hcores are in the available database. So we restrict the number of
Hmonomers in the sequence to the currently maximal Hcore size
available.
LatFit
What is an RMSD and how to calculate dRMSD and cRMSD?
To compare protein structures often the
root mean square deviation (RMSD)
is used. There are two types of distance measures used:
 cRMSD (coordinate RMSD) : measures the average displacement of
each structure monomer compared to the corresponding one in the
second structure. Thus, the measure depends on the superpositioning
of the two structures to each other to yield reasonable results.
 dRMSD (distance RMSD) : measures the average deviation of the
structure internal distances compared to the corresponding distances
within the second structure. Therefore, this measure is independent
from the relative positioning of the structures to each other.