Artificial Intelligence in Medicine
Volume 35, Issue 1 , Pages 147-156, September 2005

Prediction of MHC class II binders using the ant colony search strategy

  • Oleksiy Karpenko

      Affiliations

    • Department of Bioengineering (MC063), University of Illinois at Chicago, 851 South Morgan Street, Chicago, IL 60607, USA
  • ,
  • Jianming Shi

      Affiliations

    • Department of Computer Science and Systems Engineering, Muroran Institute of Technology, 27-1 Mizumoto-Cho, Muroran, Hokkaido 0508585, Japan
    • Corresponding Author InformationCorresponding author. Tel.: +81 143 46 5423; fax: +81 143 46 5423.
  • ,
  • Yang Dai

      Affiliations

    • Department of Bioengineering (MC063), University of Illinois at Chicago, 851 South Morgan Street, Chicago, IL 60607, USA
    • Corresponding Author InformationCorresponding author. Tel.: +1 312 413 1487; fax: +1 312 996 5921.

Received 17 November 2004; received in revised form 22 January 2005; accepted 22 February 2005.

Summary 

Objective:

Predictions of the binding ability of antigen peptides to major histocompatibility complex (MHC) class II molecules are important in vaccine development. The variable length of each binding peptide complicates this prediction.

Methodology:

Motivated by the search properties of the ant colony system (ACS), a method for the identification of an alignment for a given set of short protein peptides has been developed. This alignment is further used for the derivation of a position specific scoring matrix. The distinguishing feature of this method is the use of the collective optimized search strategy of ants for the selection of the alignment.

Results:

The performance of the new model has been evaluated with several benchmark datasets. It achieves better or comparable results as compared to the performance of existing methods.

Conclusion:

The experiments demonstrate that the predictive performance of the scoring matrix embodies several promising characteristics.

Keywords: MHC class II binding peptide, Ant colony system, Multiple alignment, Scoring matrix

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PII: S0933-3657(05)00053-9

doi:10.1016/j.artmed.2005.02.002

Artificial Intelligence in Medicine
Volume 35, Issue 1 , Pages 147-156, September 2005