Filtering epitope alignments to improve protein surface prediction

  • Authors:

  • Brendan Mumey;Nathaniel Ohler;Thomas Angel;Algirdas Jesaitis;Edward Dratz

  • Affiliations:

  • Department of Computer Science, Montana State University, Bozeman, MT;Department of Computer Science, Montana State University, Bozeman, MT;Department of Microbiology, Montana State University, Bozeman, MT;Department of Microbiology, Montana State University, Bozeman, MT;Department of Biochemistry, Montana State University, Bozeman, MT

  • Venue:
  • ISPA'06 Proceedings of the 2006 international conference on Frontiers of High Performance Computing and Networking
  • Year:
  • 2006

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Abstract

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.