  We are frequently faced with a large collection of antibodies, and want to
select those with highest affinity for their cognate antigen. When developing a
first-line therapeutic for a novel pathogen, for instance, we might look for
such antibodies in patients that have recovered. There exist effective
experimental methods of accomplishing this, such as cell sorting and baiting;
however they are time consuming and expensive. Next generation sequencing of B
cell receptor (BCR) repertoires offers an additional source of sequences that
could be tapped if we had a reliable method of selecting those coding for the
best antibodies. In this paper we introduce a method that uses evolutionary
information from the family of related sequences that share a naive ancestor to
predict the affinity of each resulting antibody for its antigen. When combined
with information on the identity of the antigen, this method should provide a
source of effective new antibodies. We also introduce a method for a related
task: given an antibody of interest and its inferred ancestral lineage, which
branches in the tree are likely to harbor key affinity-increasing mutations?
These methods are implemented as part of continuing development of the partis
BCR inference package, available at https://github.com/psathyrella/partis.
