Study On Structural And Kinetic Mechanisms And Quantitative Patterns Of T Cell Specific Recognition

T cell antigen recognition is the initial behavior of cellular immune system against dangerous signals. There are two specific sources in T cell antigen recognition: one is MHC molecule and the other one is the presented antigenic peptide by MHC, while the antigenic peptide is the most important deterministic factor. T cells are documented to be extremely sensitive to antigens and simultaneously with high specificity, while the underlying mechanism has not been worked out. To date, there are two ways to the understanding of this mechanism, namely, the structure (space)-dependent mechanism and the time-dependent mechanism. The structure (space)-dependent mechanism argues that the signaling of T cell specific recognition is achieved by the conformational changes of the antigen recognition module (mainly the complex combining pMHC, TCR/CD3, and coreceptor CD4 or CD8 molecules); while the time-dependent mechanism insists that T cell antigen recognition is dominated by the dynamics which depends on the interaction kinetics of various receptor/ligand pairs (esp., the antigenic pMHC/TCR pair) between T cell and APC.One important objective of studying T cell specific recognition is to provide the theoretical basis for the design of the T cell epitope-based vaccines. As T cell epitope is the specific condition for the activation of the T cell, then, how to find the epitope presented by specific MHC molecule from the pathogens is critical to the design of the T cell epitope-based vaccines. Because T cell can only cognize the peptides presented by the MHC molecule, the predictors for T cell epitopes are usually based on the prediction of the peptides binding to a specific MHC molecule.This paper studied the structural mechanisms underlying the TCR recognition of pMHC as well as the coupling of CD2 with CD58, and tried to provide an approach to the understanding ofthe mechanism that underlies the T cell specific recognition based on the structure. In addition, we proposed a model based on the vortex-driven mechanism to interpret the reorientation of T cell receptor during the formation of immunological synapse, and thereby tried to understand the T cell specific recognition based on the kinetics. Finally, on the prediction of T cell epitopes, we employed the neural network ensemble to predict the peptides binding to MHC class I molecule. The results are as follows;1. When TCR binding to pMHC, the slight structural adjustment only occurs on the CDRs and as this change is just local, it will not affect the close-to-membrane structure of the TCR, showing that TCR signaling is not determined by the structure change of the TCR; In addition, receptor/ligand coupling is mediated by the various weak interactions, which distribute anisotropically in the extensive interface of two reactants. This will lead to the preferential docking orientation during the interaction of the receptor/ligand.2. We demonstrated that the couplings of synaptic receptor/ligand pairs per se have driven the directed motion of T cell receptors. During the coupling, the membrane-tethered receptor/ligand pairs are assumed to transform the binding free energies into the rotational energies of the reactants, thereby leading to the vortexes of the surrounding water continuum inside and outside the T cell. These resulting vortexes can recruit TCRs (as well as other synaptic molecules) into the synapse and ma> also orient the active cytoskeletal transport of receptors toward the junction. The model results indicated that efficient TCR reorientation requires sufficiently high values of both strengths and acting frequencies of the consecutive driving vortexes, which are finally determined by the coordination of kinetics and quantitative levels of various receptor/ligand interactions within the synapse. With the thymic selections on the T cells, TCR reorientation mechanisms in the selected T cells are all at a critical point where only TCR/pMHC interactions with specific range of kinetic parameters and quantitative levels can trigger the formation of the mature synapse - a precursor for T cell activation. This may direct the understanding of the coexistence of high sensitivity with high specificity in the T cell antigen recognition just based on the kinetics and quantitative levels of TCR/pMHC interactions themselves rather than on the subsequent signaling cascades.3. On a database of 628 nonamers and their classified binding capacities to MHC class I molecule encoded by gene HLA-A*0201, the generalized neural network ensemble (NNE) with12 component neural networks achieved an average predictive hit rate of 0.8 for the classifications of the peptides respectively with non, low, moderate and high binding capacities. In addition, NNE was also efficient in the prediction of the potential T-cell epitopes. The predictive power of NNE was further evaluated by running generalized NNE on a set of actual T-cell epitopes (including 50 actual epitopes) and showed that about 84% of the actual T-cell epitopes are among the potentially antigenic peptides with high and moderate affinities. Therefore, the neural network ensemble can be applied in the prediction of MHC class I binding peptides and moreover, after proper modifications, they can be conveniently extended to cover peptides with any length and thus suitable for the prediction of peptides binding to other MHC class I or even class II molecules.The above results suggested thai in order to crack the paradox of the coexistence of high specificity with high sensitivity during T cell antigen recognition, it is required that the structure (space(-dependent mechanism should be integrated with the time-dependent mechanism and that the denotations of structure and time should be extended. In other words, the so-called "structure" should not refer to the structure of single molecule or molecular complex, but to the super-molecular cluster at the cellular level, i.e. the immunological synapse; while the concept "time" should be defined by the evolving dynamics that involves the structural content, namely, the coordination of the kinetics and quantitative levels of various receptor/ligand interactions within the synapse.On the predictors for the T cell epitopes, neural network ensemble is a valuable choice worthy of advanced studies. NNE considers the contribution of each amino acid residue at the presented peptide to the affinity for the MHC molecule, and remarkably improves the generalization capacity of the learning system through training the component neural networks and integrating their predictive abilities. In addition, neural network ensembles are characterized by their high-throughput data processing capacities.