You might be developing a vaccine or an immunodiagnostic test, or wishing to produce an antibody to probe a protein's structure and function, or you might be an antibody producing company wishing to expand its product range.

At the outset of any of these pursuits, possibly the most frequently expressed concern is which peptide sequences to choose for generating antibodies.

Alternatively, you may be experiencing difficulty producing antibodies to a purified protein and even be considering artificially tagging the target protein with an unrelated epitope.

Or, you may be frustrated by the lack of commercially available antibodies to regions other than protein N- and C-termini, and, thus, be considering producing your own antibodies to target specific regions of a protein of interest.

Also, you may be unsure of the thoroughness of an epitope prediction service that is bundled with a peptide synthesis or epitope mapping service.

In any of these situations you might be faced with choosing between self-performed epitope prediction and analysis, which is difficult, or enlisting assistance from experts such as Epitope Informatics.


Epitope Informatics specialises in in silico epitope discovery and related protein annotation, with a particular focus being epitope prediction and target protein epitope contextual analysis for the identification and targeting of antibody epitopes. To be the best at what we do, our core technology includes use of consensus algorithmic profiling to identify and rank B cell epitopes at the surface of a target protein.

Our solution, which you will not find anywhere else, is to provide an in silico epitope discovery service that addresses:

  • Epitope prediction accuracy, and the evidence for most prediction platforms that it is better to arrive at a consensus through use of a variety of critically evaluated algorithms and approaches.
  • Cross-referencing to critically evaluated information available for a target protein.
  • The evidence that certain epitope prediction algorithms are better than others.
  • Ranking predicted epitopes in order of likely antigenicity and weighting given to regions predicted to be surface located but not predicted to be antigenic, or vice versa.
  • Significance of peaks of predicted antigenicity seen in certain algorithm profiles.
  • Optimal algorithm "window" settings, and adjusting these to vary the "smoothing effect" and obtain extra information from the algorithm output.
  • Degree of importance attached to findings of secondary structure prediction, in particular, β-turn structures.
  • Predicted epitope association with protein sequence motifs providing further evidence supporting the surface-location of such epitopes, and raising the possibility of epitope association with protein function.
  • Primary protein database searching, and parameters recommended for use with shorter query strings such as predicted epitopes.
  • Antibody cross-reactivities identified by searching primary protein databases for sequences showing identity to predicted epitopes.
  • Gaps (insertions and deletions) in returned sequence alignments from protein database searches.
  • Epitope low complexity sequence and its effect on antibody specificity and alignments obtained from protein database searching.
  • Any association of sequence pattern motifs with B cell epitopes, which can cause important alignments in protein databases to be missed.
  • Choice of predicted epitope sequence and peptide conjugation strategy that avoids any sequence pattern motifs associated with predicted epitopes.