How different are self and nonself?

TL;DR

This paper explores how the immune system reliably distinguishes self from nonself peptides despite their similar distributions, by leveraging inhomogeneities in sequence space that enable sensitive discrimination near self-peptides.

Contribution

It reveals that self and nonself peptides have nearly identical distributions, and discrimination relies on targeting the inhomogeneous regions between samples, a novel insight into immune recognition.

Findings

Self and nonself peptides have nearly identical distributions.

Discrimination is based on targeting inhomogeneous regions in sequence space.

The model explains immune responses to mutation-derived cancer neoantigens.

Abstract

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.