Filtering Epitope Alignments to Improve Protein Surface Prediction

  • Brendan Mumey
  • Nathaniel Ohler
  • Thomas Angel
  • Algirdas Jesaitis
  • Edward Dratz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4331)


In previous work, we developed a new algorithm to computationally predict the epitope, the antibody binding surface of a protein, based on aligning individual mimetic probe sequences derived from an experimental process called antibody imprinting for the protein of interest. A program called EPIMAP implements this algorithm and produces a list of the top-scoring alignment(s) of the probe to protein. Typically 50-100 probes sequences will be known experimentally and must be individually aligned using EPIMAP. The goal of the work reported in this paper is to select the most mutually compatible alignments (one for each probe used) in order to improve the accuracy of epitope prediction. We formalize this problem, show that it is NP-complete and describe an effective branch-and-bound search algorithm that works well in practice for inputs of interest. We show in our experimental results section that filtering alignments improves the accuracy the epitope prediction.


Epitope Prediction Primary Amino Acid Sequence Current Good Solution Antibody Epitope Alignment Direction 
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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Brendan Mumey
    • 1
  • Nathaniel Ohler
    • 1
  • Thomas Angel
    • 2
  • Algirdas Jesaitis
    • 2
  • Edward Dratz
    • 3
  1. 1.Department of Computer ScienceMontana State UniversityBozemanUSA
  2. 2.Department of MicrobiologyMontana State UniversityBozemanUSA
  3. 3.Department of BiochemistryMontana State UniversityBozemanUSA

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