  Apparent parallels between natural language and biological sequence have led
to a recent surge in the application of deep language models (LMs) to the
analysis of antibody and other biological sequences. However, a lack of a
rigorous linguistic formalization of biological sequence languages, which would
define basic components, such as lexicon (i.e., the discrete units of the
language) and grammar (i.e., the rules that link sequence well-formedness,
structure, and meaning) has led to largely domain-unspecific applications of
LMs, which do not take into account the underlying structure of the biological
sequences studied. A linguistic formalization, on the other hand, establishes
linguistically-informed and thus domain-adapted components for LM applications.
It would facilitate a better understanding of how differences and similarities
between natural language and biological sequences influence the quality of LMs,
which is crucial for the design of interpretable models with extractable
sequence-functions relationship rules, such as the ones underlying the antibody
specificity prediction problem. Deciphering the rules of antibody specificity
is crucial to accelerating rational and in silico biotherapeutic drug design.
Here, we formalize the properties of the antibody language and thereby
establish not only a foundation for the application of linguistic tools in
adaptive immune receptor analysis but also for the systematic immunolinguistic
studies of immune receptor specificity in general.
