Automated generation and evaluation of specific MHC binding predictive tools: ARB matrix applications

Huynh Hoa Bui, John Sidney, Bjoern Peters, Muthuraman Sathiamurthy, Asabe Sinichi, Kelly Anne Purton, Bianca R. Mothé, Francis V. Chisari, David I. Watkins, Alessandro Sette

Research output: Contribution to journalArticlepeer-review

197 Scopus citations


Prediction of which peptides can bind major histocompatibility complex (MHC) molecules is commonly used to assist in the identification of T cell epitopes. However, because of the large numbers of different MHC molecules of interest, each associated with different predictive tools, tool generation and evaluation can be a very resource intensive task. A methodology commonly used to predict MHC binding affinity is the matrix or linear coefficients method. Herein, we described Average Relative Binding (ARB) matrix methods that directly predict IC50 values allowing combination of searches involving different peptide sizes and alleles into a single global prediction. A computer program was developed to automate the generation and evaluation of ARB predictive tools. Using an in-house MHC binding database, we generated a total of 85 and 13 MHC class I and class II matrices, respectively. Results from the automated evaluation of tool efficiency are presented. We anticipate that this automation framework will be generally applicable to the generation and evaluation of large numbers of MHC predictive methods and tools, and will be of value to centralize and rationalize the process of evaluation of MHC predictions. MHC binding predictions based on ARB matrices were made available at web server.

Original languageEnglish (US)
Pages (from-to)304-314
Number of pages11
Issue number5
StatePublished - Jun 2005
Externally publishedYes


  • Automated tool evaluation
  • Binding prediction
  • Computer algorithms
  • MHC
  • Web server

ASJC Scopus subject areas

  • Immunology
  • Genetics


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