Discotope - 2.0

Prediction of discontinuous B-cell epitopes from protein 3D-structure

The DiscoTope server predicts discontinuous B cell epitopes from protein three dimensional structures. The method utilizes calculation of surface accessibility (estimated in terms of contact numbers) and a novel epitope propensity amino acid score. The final scores are calculated by combining the propensity scores of residues in spatial proximity and the contact numbers.

New in the DiscoTope version 2.0: Novel definition of the spatial neighborhood used to sum propensity scores and half-sphere exposure as a surface measure.

Note: The DiscoTope server has been up-dated to improve the user-friendliness. The server now predicts epitopes in complexes of multiple chains. Also, DiscoTope output files are now easily downloaded and imported in spreadsheets. Futhermore, we have facilitated the visualization of prediction results.

NOTE: This is not the newest version of DiscoTope. To use the current version, please go to the main DiscoTope site!


Please choose one of the following three submission methods:

  1. Chain(s) in an existing PDB entry. Use comma for separation of chain ids. If this box is unspecified,
    the prediction will be done using all chains in the pdb file.

    PDB code:     Chain(s):      

  2. A file from your local disk containing a list of existing PDB entries with specified chain ID, one per line,
    in the format 'entryname_chain' e.g. 1zz6_B:

    File name:

  3. A file from your local disk containing your own structure in PDB format (not necessarily present in PDB):

    File name:  

    Chain(s):          (if chain id is unspecified, all chains in the structure will be used for prediction.)

Specify the threshold for epitope identification:     (see the Instructions)

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


For publication of results, please cite:

Reliable B Cell Epitope Predictions: Impacts of Method Development and Improved Benchmarking
Jens Vindahl Kringelum, Claus Lundegaard, Ole Lund, and Morten Nielsen
Plos Computational Biology, 2012
Link to Paper


In order to use the DiscoTope server for prediction of discontinuous B cell epitopes:
  1. Use one of the following three ways of submission:

    1. Write a PDB entry name and chain id(s) into the corresponding windows.

    2. Specify a file from your local disk containing a list of existing PDB entries with specified chain IDs, one per line, in the format 'entryname_chain' e.g. 1zz6_B

    3. Specify a file from your local disk, in PDB format, and chain id(s).

    Note: Chain ids should be separated by comma (,). If no chain id is specified, all chains in the file will be used for prediction.

  2. Optional: Select DiscoTope threshold score for epitope identification.
    Higher values correspond to higher specificity.

    Different thresholds for the DiscoTope score can be translated into sensitivity/specificity values. In a benchmark containing more than 75 antigen/antibody complexes, the following relations were found:

    Score Sensitivity Specificity
    > 1.9 0.17 0.95
    > 0.5 0.23 0.90
    > -1.0 0.30 0.85
    > -2.5 0.39 0.80
    > -3.7 0.47 0.75

    A specificity of 0.75 means that 25% of the nonepitope residues were predicted as part of epitopes.
    A sensitivity of 0.47 means that 47% of the epitope residues were predicted as part of epitopes.

  3. Press the "Submit sequence" button.

  4. A WWW page will return the results when the prediction is ready. Response time depends on system load.

Output format


The output consists of 7 columns:
  • Chain Id
  • Residue number
  • Amino acid
  • Contact number
  • Propensity score
  • DiscoTope score
  • <=B. Identified B cell epitope


    DiscoTope predictions for '1a2y'.
    	Looking only at Chain(s):  A
    	propensity score radius = 22.000 Angstroms, Upper Halfsphere radiues = 14.000, windowsize = 1, alpha = 0.115
    	Threshold = -3.700
    1. Download Prediction File
    2. Download PDB file
    3. Download pymol display script
         Note that the file '1a2y.pdb' (from above) must reside
         in the same directory as '1a2y_pymol.pml'
    4. View results in Jmol (please be patient...requires Jmol applet download) Residues colored by binary code - Yellow = predicted epitope residues
    4. View results in Jmol (please be patient...requires Jmol applet download) Residues colored by DiscoTope score - Red = high score, Blue = low score
    A	1	ASP	12	-3.653	-4.613
    A	2	ILE	24	-6.595	-8.597
    A	3	VAL	3	-6.961	-6.505
    A	4	LEU	36	-10.827	-13.722
    A	5	THR	7	-10.343	-9.959
    A	6	GLN	25	-11.905	-13.411
    A	7	SER	5	-10.829	-10.158
    A	8	PRO	9	-10.021	-9.904
    A	9	ALA	0	-9.261	-8.196
    A	10	SER	4	-8.622	-8.090
    A	11	LEU	26	-8.722	-10.709
    A	12	SER	1	-5.088	-4.618
    A	13	ALA	26	-6.063	-8.356
    A	14	SER	1	-3.958	-3.617	<=B
    A	15	VAL	3	-4.265	-4.119
    A	16	GLY	2	-4.568	-4.273
    A	17	GLU	13	-6.781	-7.496
    Identified 8 B-Cell epitope residues out of 107 total residues

  • References

    Reliable B cell epitope predictions: Impacts of method development and improved benchmarking
    Jens Vindahl Kringelum, Claus Lundegaard, Ole Lund, and Morten Nielsen
    Plos Computational Biology, 2012

    Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark


    The interaction between antibodies and antigens is one of the most important immune system mechanisms for clearing infectious organisms from the host. Antibodies bind to antigens at sites referred to as B-cell epitopes. Identification of the exact location of B-cell epitopes is essential in several biomedical applications such as; rational vaccine design, development of disease diagnostics and immunotherapeutics. However, experimental mapping of epitopes is resource intensive making in silico methods an appealing complementary approach.

    To date, the reported performance of methods for in silico mapping of B-cell epitopes has been moderate. Several issues regarding the evaluation data sets may however have led to the performance values being underestimated: Rarely, all potential epitopes have been mapped on an antigen, and antibodies are generally raised against the antigen in a given biological context not against the antigen monomer. Improper dealing with these aspects leads to many artificial false positive predictions and hence to incorrect low performance values.

    To demonstrate the impact of proper benchmark definitions, we here present an updated version of the DiscoTope method incorporating a novel spatial neighborhood definition and half-sphere exposure as surface measure. Compared to other state-of-the-art prediction methods, Discotope-2.0 displayed improved performance both in cross-validation and in independent evaluations. Using DiscoTope-2.0, we assessed the impact on performance when using proper benchmark definitions. For 13 proteins in the training data set where sufficient biological information was available to make a proper benchmark redefinition, the average AUC performance was improved from 0.791 to 0.824. Similarly, the average AUC performance on an independent evaluation data set improved from 0.712 to 0.727

    Our results thus demonstrate that given proper benchmark definitions, B-cell epitope prediction methods achieve highly significant predictive performances suggesting these tools to be a powerful asset in rational epitope discovery.

    The updated version of DiscoTope is available at www.cbs.dtu.dk/services/DiscoTope-2.0.

    Link to Paper


    Correspondence:        Technical Support: