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.
Highlights
Atomistic Molecular Dynamics provides powerful and flexible tools for the prediction and analysis of molecular and macromolecular systems
In venturing to gain a scientific understanding of these elusive, recondite worlds far beyond the limits of our direct experience, we seek to do synthetic reductionism: dissecting phenomena associated with the nature and behaviour of the biological molecules comprising the systems we study, and building mathematical models
The cell-mediated immune response forms the other half of adaptive immunity; here activated T cells react against foreign antigen presented on the surface of other cells
Summary
MD simulation [146] can be seen as a “virtual experiment” where atoms and molecules interact under the control of physical laws. In terms of modelling the association of MHC molecule with antigenic peptides, it is increasingly clear that the whole peptide contributes to affinity, and to T cell-mediated immunogenicity; effective models of binding must represent this adequately This MD does implicitly, and our group has extensive and continuing experience of developing reliable tools for this kind of study. There is and will be a need to progress constantly to larger and larger simulations and to use the technique to drive rather than follow experiment While both MD and related methods hold out the greatest hope for true de-novo predictions of MHC binding, their present success rate is very much lower than that of data driven models.
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