The adaptive immune system consists of highly diverse B- and T-cell receptors, which can recognize a multitude of diverse pathogens.
Immune recognition relies on molecular interactions between immune receptors and pathogens, which in turn is determined by the complementarity of their 3D structures and amino acid compositions, i.e., their shapes. Immune shape space has been previously introduced as an abstraction of molecular recognition in the immune system.
However, the relationships between immune receptor sequence, protein structure, and specificity are very difficult to quantify in practice.
In this talk, I will discuss how the growing amount of immune repertoire sequence data together with protein structures can shed light on the organization of the adaptive immune system. I will introduce physically motivated machine learning approaches to learn representations of protein micro-environments in general, and of immune receptors, in particular. The learned models reflect the relevant biophysical properties that determine a protein’s stability, and function, and could be used to predict immune recognition and to design novel immunogens e.g. for vaccine design.