BcePred Prediction Server

The BcePred server predicts B cell epitopes based on physico-chemical properties of amino acids.

Bcepred has been tested on B cell epitope database (BCIPEP), and will therefore presumbly have better performance for prediction of B cell epitope of the antigen. Bcepred can predict continuous B cell epitopes.

B-cell epitope plays an important role in humoral response and also in synthetic vaccine design. Identified properties of B cell epitope includes hydrophilicity, flexibility/mobility, accessibility, polarity, exposed surface and turns. Quantification of these properties is determined by assigning a value to each of the 20 natural amino acids. The server is able to predict epitopes with 58.7% accuracy using combined methods at a threshold of 2.38.


Instructions Output format Data Sets Algorithm Team

Instructions


In order to use the BcePred server for prediction on amino acid sequences:
  1. (optional) Enter a name for the sequence.

  2. Enter the sequence in the sequence window (with no header line), or give a file name.

    The sequence must be written using the one letter amino acid code: `acdefghiklmnpqrstvwy' or `ACDEFGHIKLMNPQRSTVWY'.
    Other letters will be converted to `X' and treated as unknown amino acids.
    Other characters, such as whitespace and numbers, will simply be ignored.

  3. Choose the output format: Graphical or Tabular, the default is Graphical.

  4. Change the threshold: increase in the threshold results in better specificity, but worse sensitivity. At 2.37, it shows 58.3% accruacy with equal sensitivity and specificity using combined method.

  5. Select the Physio-chemical properties: Hydrophilicity or Flexibility or Accessibility or Turns or Exposed surface or Polarity or Antegenic Propensity or combined Methods (All).For multiple selection use Ctrl key

  6. Press the "Submit sequence" button.

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

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contact: G.P.S.Raghava                                                                  Department of Computational Biology                                                                 IMTECH