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Computational Modelling of Immune System Processes
Computational Modelling of Immune System Processes

Immunodominance lies at the heart of the immune system's ability to distinguish self from non-self. Understanding and possibly controlling the mechanisms that govern immunodominance will have profound consequences for the fight against several classes of diseases, including viral infections and cancer. In the first phase of this project, we focus on computational modelling of MHC class I peptide editing.

Molecules encoded within the genetic region known as the major histocompatability complex (called MHC class I molecules) direct cytotoxic T lymphocytes (CTL) towards virus infected or cancerous cells, thereby destroying them and preventing disease progression. MHC class I molecules work by binding to peptide fragments arising from intracellular protein turnover and presenting them on the cell membrane where they can be recognised by CTL. Most cells will therefore present an array of tens of thousands of different peptides at their cell surface, some of which will be unique to virus infected or cancerous cells. The efficacy of a CTL response to these peptides depends to a large extent on the ability of MHC class I molecules to select only a limited number of the many billions of different peptides that could be generated by the random hydrolysis of all intracellular proteins. Peptide loading of MHC class I occurs in the endoplasmic reticulum (ER) and is assisted by multiple cofactors which are thought to enable the selection process. Of these, only the molecule tapasin appears to influence the relative abundance of high, medium and low affinity peptides loaded onto MHC class I. A full understanding of the molecular mechanism of peptide editing within the ER is important for our understanding of immunodominance: the predominance of some CTL specificities over others during an immune response, which could ultimately determine its efficacy. Our understanding of peptide editing in the ER is currently limited by a lack of an analytical framework for the description of the experiments. Hence, we have created a functional model of the interaction between MHC class I, peptide and tapasin as a first approximation to test possible molecular mechanisms underlying the peptide editing functions of tapasin. Our modelling shows how peptide editing and enhanced presentation of high affinity MHC peptide complexes is achieved by a combination of the chaperone tapasin and kinetic control of the binding mechanism. From this model we propose a general mechanism of kinetic filtering, which explains how MHC class I is able to discriminate between peptides of different affinities. We use this to propose a set of hypotheses that account for the nature of allelic differences in peptide editing. We arrive at a concise model that enables a quantitative description of peptide editing, which is compatible with current biological observations.