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• W3017667722 abstract "ABSTRACT 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; how-ever 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 . Comments . Please post comments or questions on this paper as new issues at https://git.io/Jvxkn ." @default.
• W3017667722 created "2020-05-01" @default.
• W3017667722 creator A5084752404 @default.
• W3017667722 creator A5085169998 @default.
• W3017667722 date "2020-04-25" @default.
• W3017667722 modified "2023-10-02" @default.
• W3017667722 title "Using B cell receptor lineage structures to predict affinity" @default.
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• W3017667722 doi "https://doi.org/10.1101/2020.04.24.060194" @default.
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