Reorientation of T cell receptors during immunological synapse formation:a vortex-driven model / 中国免疫学杂志

Objective:To develop the mechanistic model for the reorientation of T cell receptors during immunological synapse formation.Methods:Based on the theory of energy transfer during double-molecular reactions in the context of classical fluid mechanics,a vortex-driven model was proposed where in the coupled receptor/ligand molecules within the immunological synapse recruit the T cell receptors.Results:The model results indicated that driven by the consecutive vortexes with specific combinations of strengths and acting frequencies of vortexes,TCR transport speed can reach the values matching up to the experimental measurements(0.04-0.1 ?m/s).Conclusion:The model demonstrated that during the coupling,the membrane-tethered receptor-ligand pairs may transform their binding energies into the rotational energies of the reactants,thereby leading to the vortexes of the surrounding water continuum insider and outside the T cell,and these resulting vortexes may function as the engines for the reorientation of T cell receptors.

Prediction of MHC class Ⅰ binding peptides using neural network ensembles / 中国病理生理杂志

AIM: To predict MHC class Ⅰ binding peptides by using neural network ensembles. METHODS: As a combination of neural networks, neural network ensemble (NNE) was here used to improve the predictive performance. Based on a database of 628 nonamers and their classified binding capacities, the generalized NNEs were used to classify peptides respectively with non, low, moderate and high binding capacities to MHC class I molecule encoded by gene HLA-A*0201. The predictive power of NNE was further evaluated by running generalized NNE on a set of actual T-cell epitopes. RESULTS: The generalized NNEs achieved an average predictive hit rate of 0.8 for the above classifications. In addition, NNE was also efficient in the prediction of the potential T-cell epitopes, and about 84% of the actual T-cell epitopes were among the potentially antigenic peptides with high and moderate affinities. CONCLUSION: The NNEs 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.