Buus S, Lauemøller S L, Worning P, Kesmir C, Frimurer T, Corbet S, Fomsgaard A, Hilden J, Holm A, Brunak S
Division of Experimental Immunology, Institute of Medical Microbiology and Immunology, University of Copenhagen, Denmark.
Tissue Antigens. 2003 Nov;62(5):378-84. doi: 10.1034/j.1399-0039.2003.00112.x.
We have generated Artificial Neural Networks (ANN) capable of performing sensitive, quantitative predictions of peptide binding to the MHC class I molecule, HLA-A*0204. We have shown that such quantitative ANN are superior to conventional classification ANN, that have been trained to predict binding vs non-binding peptides. Furthermore, quantitative ANN allowed a straightforward application of a 'Query by Committee' (QBC) principle whereby particularly information-rich peptides could be identified and subsequently tested experimentally. Iterative training based on QBC-selected peptides considerably increased the sensitivity without compromising the efficiency of the prediction. This suggests a general, rational and unbiased approach to the development of high quality predictions of epitopes restricted to this and other HLA molecules. Due to their quantitative nature, such predictions will cover a wide range of MHC-binding affinities of immunological interest, and they can be readily integrated with predictions of other events involved in generating immunogenic epitopes. These predictions have the capacity to perform rapid proteome-wide searches for epitopes. Finally, it is an example of an iterative feedback loop whereby advanced, computational bioinformatics optimize experimental strategy, and vice versa.
我们已经构建了能够对肽与主要组织相容性复合体I类分子HLA - A*0204的结合进行灵敏、定量预测的人工神经网络（ANN）。我们已经表明，这种定量ANN优于传统的分类ANN，传统分类ANN是经过训练来预测结合肽与非结合肽的。此外，定量ANN允许直接应用“委员会查询”（QBC）原则，据此可以识别特别富含信息的肽，随后进行实验测试。基于QBC选择的肽进行迭代训练，在不影响预测效率的情况下显著提高了灵敏度。这表明了一种通用、合理且无偏见的方法，用于开发针对此HLA分子及其他HLA分子的高质量表位预测。由于其定量性质，此类预测将涵盖广泛的具有免疫学意义的MHC结合亲和力，并且它们可以很容易地与生成免疫原性表位所涉及的其他事件的预测相结合。这些预测有能力在全蛋白质组范围内快速搜索表位。最后，这是一个迭代反馈回路的例子，即先进的计算生物信息学优化实验策略，反之亦然。
