Pharmaceutical & Diagnostic Innovation

, Volume 4, Issue 9, pp 12–13 | Cite as

Optimizing Discovery of Therapeutic Proteins

AlgoNomics Offers In Silico Support
Emerging Technology

Executive summary

Identifying whether a therapeutic protein candidate could trigger adverse immune reactions is an important part of the development process. Conversely, selecting proteins with high immunogenicity is vital in vaccine discovery. Until recently, choosing candidate proteins with appropriate immunogenicity for further development relied on lengthy experiments and computer-based analysis of protein sequence. However, the Belgian company AlgoNomics has developed a structure-based algorithm that enables rapid and efficient determination of the immunogenicity of proteins. Epibase® can be used to select protein candidates with minimal immunogenicity for use as therapeutics, or those with high immunogenicity for formulation into vaccines.

The potential impact of this in silicotool in protein drug discovery was recognized when AlgoNomics became the recipient of the 2006 Frost & Sullivan Award for Technology Innovation. AlgoNomics has also developed a structural bioinformatics...


Human Leukocyte Antigen Therapeutic Protein Pharmaceutical Protein Major Histocompatibility Class High Immunogenicity 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Frost & Sullivan. Frost & Sullivan lauds AlgoNomics technology innovation. Media release: 24 Apr 2006Google Scholar

Copyright information

© Adis Data Information BV 2006

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