DTU Health Tech

Department of Health Technology

DistanceP - 1.0

Predicts protein distance constraints

The distanceP server predicts distance constraints between amino acids in proteins from the amino acid sequence. It is an update of the Sowhat server.

Submission


Sequence name

Sequence (All non standard amino acid symbols are discarded. However, "X" is a wildcard)

Customize the run: (hint: first try using the default values)

Sequence separation:    Min (2)       Max (50) 

Display graphics

Show sequence letters on the edges of the plot

Number of profiles sequences (max 40)  

Show performance for PDB entry chain and compare to true values.
(This option will ignore any sequence pasted in the textbox above.)

Confidentiality:
The sequences are kept confidential and will be deleted after processing.


CITATIONS

For publication of results, please cite:

Using Sequence Motifs for Enhanced Neural Network Prediction of Protein Distance Constraints.
J. Gorodkin, O. Lund, C. A. Andersen, and S. Brunak
In proceedings of the Seventh International Conference for Molecular Biology,
eds. T. Lengauer, R. Schneider, P. Bork, D. Brutlag, J. Glasgow, H-W. Mewes, and R. Zimmer
: 95-105, 1999.

View the article.

Introduction to distanceP



This server is an update and improvement of the sowhat server. For an overview consult the abstracts of the following papers (Abstract tab). Detailed background and introduction can be found in the papers:
  • Using Sequence Motifs for Enhanced Neural Network Prediction of Protein Distance Constraints. J. Gorodkin, O. Lund, C. A. Andersen, and S. Brunak. In proceedings of the seventh international conference for molecular biology, eds. T. Lengauer, R. Schneider, P. Bork, D. Brutlag, J. Glasgow, H-W. Mewes, and R. Zimmer, pp 95-105, 1999. (http://www.cbs.dtu.dk/services/distanceP/)

  • Protein distance constraints predicted by neural networks and probability density functions. O. Lund, K. Frimand, J. Gorodkin, H. Bohr, J. Bohr, J. Hansen, and S. Brunak. Protein Engineering, Volume 10, Issue 11: November 1997. 1241-1248. (http://www.cbs.dtu.dk/services/CPHmodels/)
  • Download the transparancies from the talk given at ISMB'99.

    Download the poster presented at BIOINFORMATICS'99.

    A description of distanceP is given on the Manual tab.

    For each sequence separation (residues) a threshold has been computed as the average physical distance (Angstrom) with sequence windows chosen from a non-redundant data set of proteins with known three-dimensional structure. Each of these thresholds serves as constraints for the neural network predictions. The networks predict whether the physical distance for two sequence separated residues is below or above the thresholds. The computed thresholds for each sequence separation can be downloaded here. The list of pdb entries that were used for training the neural networks in distanceP can be downloaded here.

    Paper to reference when reporting results

    Using Sequence Motifs for Enhanced Neural Network Prediction of Protein Distance Constraints. J. Gorodkin, O. Lund, C. A. Andersen, and S. Brunak. In proceedings of the seventh international conference for molecular biology, eds. T. Lengauer, R. Schneider, P. Bork, D. Brutlag, J. Glasgow, H-W. Mewes, and R. Zimmer, pp 95-105, 1999. (http://www.cbs.dtu.dk/services/distanceP/)

    *

    distanceP Manual


    The description is given for command line version. However the options are all available on the www interface.

    distanceP

    NAME
    distanceP - distanceP predicts distance constraint probabilities between residues in a protein chain. distanceP is an update of the sowhat server.
    SYNOPSIS
    distanceP [options] datafile
    DESCRIPTION
    distanceP takes any file containing the data in any format. All characters that does not match those of the standard 20 letter protein alphabet are discarded. The letter "X" is a wild card though. The output produced by distanceP is generated in the Ma/trixPlot data format (mp format). distanceP consist of multiple C programs and a C shell script.
    OPTIONS
    -dmin <number>
    Number. Specify the minimum sequence separation (residues) for which the constraint probability should be calculated. Minimum and default value is 2.
    -dmax <number>
    Number. Specify the maximum sequence separation (residues) for which the constraint probability should be calculated. Maximum and default value is 50.
    -seqletters y|n
    Show sequence letters along the edges of the distance plot.
    -pdbentry <pdbname>
    Generates prediction of given PDB entry, and compares to the known 3D coordinates in the database. The default chain is the first one in the PDB entry. Any sequence submitted to the program will with this option be ignored.
    -pdbchain <pdbchain>
    Works only with the option -pdbentry, and is used to specify the chain identifier in that PDB entry.
    -nprof <number>
    Number of profile sequences generated from the query sequence. Maximum number of profile sequences is 40.
    EXAMPLE
    cat datafile | distanceP -dmin 2 -dmax 25 > dist.mp
    (You can then use MatrixPlot to generate a postscript file)
    AUTHOR
    Jan Gorodkin, gorodkin@cbs.dtu.dk, April 1999.
    REFERENCES
  • Using Sequence Motifs for Enhanced Neural Network Prediction of Protein Distance Constraints. J. Gorodkin, O. Lund, C. A. Andersen, and S. Brunak. In proceedings of the seventh international conference for molecular biology, eds. T. Lengauer, R. Schneider, P. Bork, D. Brutlag, J. Glasgow, H-W. Mewes, and R. Zimmer, pp 95-105, 1999. (http://www.cbs.dtu.dk/services/distanceP/)
  • Protein distance constraints predicted by neural networks and probability density functions. O. Lund, K. Frimand, J. Gorodkin, H. Bohr, J. Bohr, J. Hansen, and S. Brunak. Protein Engineering, Volume 10, Issue 11: November 1997. 1241-1248. (http://www.cbs.dtu.dk/services/CPHmodels/)
  • References


    Abstract for the paper:
    Using Sequence Motifs for Enhanced Neural Network Prediction of Protein Distance Constraints. J. Gorodkin, O. Lund, C. A. Andersen, and S. Brunak. ISMB99. In press.

    Correlations between sequence separation (in residues) and distance (in Angstrom) of any pair of amino acids in polypeptide chains are investigated. For each sequence separation we define a distance threshold. For pairs of amino acids where the distance between C-alpha atoms is smaller than the threshold, a characteristic sequence (logo) motif, is found. The motifs change as the sequence separation increases: for small separations they consist of one peak located in between the two residues, then additional peaks at these residues appear, and finally the center peak smears out for very large separations. We also find correlations between the residues in the center of the motif. This and other statistical analyses are used to design neural networks with enhanced performance compared to earlier work. Importantly, the statistical analysis explains why neural networks perform better than simple statistical data-driven approaches such as pair probability density functions. The statistical results also explain characteristics of the network performance for increasing sequence separation. The improvement of the new network design is significant in the sequence separation range 10--30 residues. Finally, we find that the performance curve for increasing sequence separation is directly correlated to the corresponding information content. A WWW server, distanceP, is available at http://services.healthtech.dtu.dk/service.php?distanceP-1.0.


    Abstract for the paper:
    Protein distance constraints predicted by neural networks and probability density functions. O. Lund, K. Frimand, J. Gorodkin, H. Bohr, J. Bohr, J. Hansen, and S. Brunak. Protein Engineering, Volume 10, Issue 11: November 1997. 1241-1248.

    We predict interatomic Calpha distances by two independent data driven methods. The first method uses statistically derived probability distributions of the pairwise distance between two amino acids, whilst the latter method consists of a neural network prediction approach equipped with windows taking the context of the two residues into account. These two methods are used to predict whether distances in independent test sets were above or below given thresholds. We investigate which distance thresholds produce the most information-rich constraints and, in turn, the optimal performance of the two methods. The predictions are based on a data set derived using a new threshold which defines when sequence similarity implies structural similarity. We show that distances in proteins are predicted more accurately by neural networks than by probability density functions. We show that the accuracy of the predictions can be further increased by using sequence profiles. A threading method based on the predicted distances is presented. A homepage with software, predictions and data related to this paper is available at http://services.healthtech.dtu.dk/service.php?CPHmodels-3.2.



    GETTING HELP

    If you need help regarding technical issues (e.g. errors or missing results) contact Technical Support. Please include the name of the service and version (e.g. NetPhos-4.0) and the options you have selected. If the error occurs after the job has started running, please include the JOB ID (the long code that you see while the job is running).

    If you have scientific questions (e.g. how the method works or how to interpret results), contact Correspondence.

    Correspondence: Technical Support: