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This article is part of the supplement: Computational Vaccinology: State-of-the-art Assessments

Open Access Highly Accessed Review

T-cell epitope prediction and immune complex simulation using molecular dynamics: state of the art and persisting challenges

Darren R Flower1*, Kanchan Phadwal2, Isabel K Macdonald3, Peter V Coveney4, Matthew N Davies5 and Shunzhou Wan4

Author Affiliations

1 Life and Health Sciences, Aston University, Aston Triangle, Birmingham B4 7ET, UK

2 Oxford Biomedical Research Centre, The John Radcliffe Hospital, Room 4503, Corridor 4b, Level 4, Oxford, OX 3 9DU, UK

3 OncImmune Limited, Clinical Sciences Building, Nottingham City Hospital, Hucknall Rd. Nottingham, NG5 1PB, UK

4 Centre for Computational Science, Chemistry Department, University College of London, 20 Gordon Street, WC1H 0AJ, London, UK

5 SGDP, Institute of Psychiatry, King's College London, De Crespigny Park, London, SE5 8AF, UK

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Immunome Research 2010, 6(Suppl 2):S4  doi:10.1186/1745-7580-6-S2-S4

Published: 3 November 2010

Abstract

Atomistic Molecular Dynamics provides powerful and flexible tools for the prediction and analysis of molecular and macromolecular systems. Specifically, it provides a means by which we can measure theoretically that which cannot be measured experimentally: the dynamic time-evolution of complex systems comprising atoms and molecules. It is particularly suitable for the simulation and analysis of the otherwise inaccessible details of MHC-peptide interaction and, on a larger scale, the simulation of the immune synapse. Progress has been relatively tentative yet the emergence of truly high-performance computing and the development of coarse-grained simulation now offers us the hope of accurately predicting thermodynamic parameters and of simulating not merely a handful of proteins but larger, longer simulations comprising thousands of protein molecules and the cellular scale structures they form. We exemplify this within the context of immunoinformatics.