C Adami

HH Lin

Indranil Mitra

JC Tong

P Wang

PJ O'Brien

Yan Cui

BMC Bioinformatics

English

PubMed

Springer - Publisher Connector

Understanding molecular recognition and epitope prediction from Information Theoretic approach

POSTER PRESENTATION Open Access
Understanding molecular recognition and
epitope prediction from Information
Theoretic approach
Indranil Mitra1*, Yan Cui2
From UT-ORNL-KBRIN Bioinformatics Summit 2010
Cadiz, KY, USA. 19-21 March 2010
Background
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 appli-
cations have been very successful at the sequence level,
many sequence comparison and binding site identifica-
tion methods now boasts a sound information theoretic
foundation.
Materials and methods
In this work we have developed a mathematical formal-
ism for applying information theory in identifying an
explicit computational strategy and developing algo-
rithms for the study of peptide/MHC interactions
through epitope predictions. A sampling method has
been initiated to circumvent the binding problem. Com-
parisons have been made with existing Machine Learn-
ing Methods and a validation of the efficiency of the
model may be tested [3,4].The results will have signifi-
cant impact for understanding the immune system and
for rational drug design [5].
Acknowledgments
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.
Author details
1Department of Mathematical Sciences, Clemson University, E-006 Martin
Hall, Clemson, SC 29631, USA. 2Department of Molecular Sciences & Center
of Integrative and Translational Genomics, University of Tennessee Health
Science Center, 858 Madison Ave. Memphis, TN 38163, USA.
Published: 23 July 2010
References
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.
2. Adami C: Physics of Life Reviews. 2004, 1.
3. O’Brien PJ, Herschlag D: Catalytic promiscuity and the evolution of new
enzymatic activities. Chem Biol 1999, 6:R91-R105.
4. Tong JC, Tan TW, Ranganathan S: Methods and Protocols for Prediction of
Immunogenic Epitopes. Brief Bioinform 2007, 8(2):96-108.
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.
doi:10.1186/1471-2105-11-S4-P22
Cite this article as: Mitra and Cui: Understanding molecular recognition
and epitope prediction from Information Theoretic approach. BMC
Bioinformatics 2010 11(Suppl 4):P22.
Submit your next manuscript to BioMed Central
and take full advantage of: 
• Convenient online submission
• Thorough peer review
• No space constraints or color figure charges
• Immediate publication on acceptance
• Inclusion in PubMed, CAS, Scopus and Google Scholar
• Research which is freely available for redistribution
Submit your manuscript at 
www.biomedcentral.com/submit
* Correspondence: aimitra@clemson.edu
1Department of Mathematical Sciences, Clemson University, E-006 Martin
Hall, Clemson, SC 29631, USA
Mitra and Cui BMC Bioinformatics 2010, 11(Suppl 4):P22
http://www.biomedcentral.com/1471-2105/11/S4/P22
© 2010 Mitra and Cui; licensee BioMed Central Ltd.


Crossref

Brusic V: Evaluation of MHC-II peptide binding prediction servers: applications for vaccine research.

Herschlag D: Catalytic promiscuity and the evolution of new enzymatic activities. Chem Biol

Methods and Protocols for Prediction of Immunogenic Epitopes. Brief Bioinform

Physics of Life Reviews.

file:///data/remote/core/dit/data/Springer-OA/pdf/40e/aHR0cDovL2xpbmsuc3ByaW5nZXIuY29tLzEwLjExODYvMTQ3MS0yMTA1LTExLVM0LVAyMi5wZGY=.pdf

Understanding molecular recognition and epitope prediction from Information Theoretic approach

Abstract

Similar works

Full text

Available Versions

Springer - Publisher Connector

Springer - Publisher Connector

Crossref