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

ABSTRACT

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.