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Understanding molecular recognition and epitope prediction from Information Theoretic approach


Cellular immunity is dependent on T-cell recognition of peptide/major histocompatibility complex (MHC) and is a critical molecular recognition component [1]. A large class of bioinformatics tools facilitates the identification of T-cell epitopes to specific MHC alleles. However, not all peptide residues contribute equally or are relevant to binding due to polymorphism of genes encoding MHC, making development of statistical methods difficult. Information Theory has proved to be one of the most universal mathematical theories that governs virtually all processes [2]. The success of this approach in analyzing a huge range of engineering, technological and natural processes is impressive. In Molecular Biology the applications have been very successful at the sequence level, many sequence comparison and binding site identification methods now boasts a sound information theoretic foundation.

Materials and methods

In this work we have developed a mathematical formalism for applying information theory in identifying an explicit computational strategy and developing algorithms for the study of peptide/MHC interactions through epitope predictions. A sampling method has been initiated to circumvent the binding problem. Comparisons have been made with existing Machine Learning Methods and a validation of the efficiency of the model may be tested [3, 4].The results will have significant impact for understanding the immune system and for rational drug design [5].


  1. Lin HH, Zhang GL, Tongchusak S, Reinherzand EL, Brusic V: Evaluation of MHC-II peptide binding prediction servers: applications for vaccine research. BMC Bioinformatics 2008, 9(Suppl 12):S12-S22. 10.1186/1471-2105-9-S12-S22

    Article  Google Scholar 

  2. Adami C: Physics of Life Reviews. 2004, 1.

    Google Scholar 

  3. O'Brien PJ, Herschlag D: Catalytic promiscuity and the evolution of new enzymatic activities. Chem Biol 1999, 6: R91-R105. 10.1016/S1074-5521(99)80033-7

    Article  PubMed  Google Scholar 

  4. Tong JC, Tan TW, Ranganathan S: Methods and Protocols for Prediction of Immunogenic Epitopes. Brief Bioinform 2007, 8(2):96–108. 10.1093/bib/bbl038

    Article  CAS  PubMed  Google Scholar 

  5. Wang P, Sidney J, Dow C, Moth B, Sette A, Peters B: A Systematic Assessment of MHC Class II Peptide Binding Predictions and Evaluation of a Consensus Approach. PLoS Comput Biol 2008, 4(4):e1000048. 10.1371/journal.pcbi.1000048

    Article  PubMed Central  PubMed  Google Scholar 

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This work was partially supported by DOD grant W81XHW-05-01-0227 received by YC. Authors would also like to thank Dr. IrisAntes, Technical University, Munich for helpful discussions.

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Correspondence to Indranil Mitra.

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Open Access This article is published under license to BioMed Central Ltd. This is an Open Access article is distributed under the terms of the Creative Commons Attribution 2.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Mitra, I., Cui, Y. Understanding molecular recognition and epitope prediction from Information Theoretic approach. BMC Bioinformatics 11 (Suppl 4), P22 (2010).

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