论文标题
自我和非自然有何不同？
How different are self and nonself?
论文作者
论文摘要
生物学和人工网络通常在相似的输入之间做出可靠的区分，并学习了这些区别的规则。在某些方面，免疫系统中的自我/非自我歧视是相似的，既可靠，又（部分）通过胸腺选择学习。与其他例子相反，我们表明自我和非自然肽的分布几乎相同，但强烈不均匀。仅是因为自肽是从该分布中抽出的特定有限样本，而T细胞可以针对这些样品之间的空间。在传统的学习问题中，这将构成过度拟合并导致灾难。在这里，强烈的不均匀性意味着免疫系统通过靶向类似于自我的肽而获得的，对序列的最大敏感性只有一两个替代。例如，从序列空间中潜在分布的结构进行的这种预测与观察到的对突变衍生的癌症新抗原的反应相符。
Biological and artificial networks routinely make reliable distinctions between similar inputs, and the rules for making these distinctions are learned. In some ways, self/nonself discrimination in the immune system is similar, being both reliable and (partly) learned through thymic selection. In contrast to other examples, we show that the distributions of self and nonself peptides are nearly identical but strongly inhomogeneous. Reliable discrimination is possible only because self-peptides are a particular finite sample drawn out of this distribution, and T cells can target the spaces in between these samples. In conventional learning problems, this would constitute overfitting and lead to disaster. Here, the strong inhomogeneities imply instead that the immune system gains by targeting peptides which are similar to self, with maximum sensitivity for sequences just one or two substitutions away. This prediction from the structure of the underlying distribution in sequence space agrees, for example, with the observed responses to mutation derived cancer neoantigens.
