BepiPred-2.0: Sequential B-Cell Epitope Predictor

The BepiPred-2.0 server predicts B-cell epitopes from a protein sequence, using a Random Forest algorithm trained on epitopes and non-epitope amino acids determined from crystal structures. A sequential prediction smoothing is performed afterwards.


The BepiPred-2.0 server requires protein sequence(s) in fasta format, and can not handle nucleic acid sequences.

Paste protein sequence(s) in fasta format into field marked by arrow A or upload a fasta file marked by arrow B. Click the submit button, marked by arrow C, when protein sequences are entered.

After the server successfully finishes the job, a summary page shows up. If an error happens during modelling a log will appear specifying the error.

Use the navigation bar (arrow A) to flip through the various output pages. The default page is the summary, which shows a scrollable table (arrow D) illustrating the sequences with a sequence markup illustrating the B-cell epitope predictions. Hover over the sequence name to reveal the description of the sequence obtained from the fasta header.

Use the Epitope Threshold slider (arrow C) to interactively change the classified epitope residues, (E for epitopes and . for non-epitopes). For guidance of which Epitope Threshold is suitable for you, click the on the slider. Use the dropdown download button (arrow B) to download the output as JSON or CSV, or select "All Downloads" to get short description of the download files.

A more advanced output visualitation is available, if clicked the "Advanced Output is Off" button.

Advanced Output Format

When advanced outformat is activated, predictions from NetsurfP is additionally available as sequence markup for each sequence and a description of the markup types are revealed (arrow A).

Two new gradients are added, illustrating to easily distinguish between secondary structure types. Coils, relative surface accessibility and BepiPred-2.0 epitope predictions all carry same gradient, as epitopes often are found in coils and needs to be exposed. This can be used to easily find correlations of high scoring areas between these three types.

For previous version go to: BepiPred-1.0

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