{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T06:14:45Z","timestamp":1772172885203,"version":"3.50.1"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1008391","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2020,11,23]],"date-time":"2020-11-23T00:00:00Z","timestamp":1606089600000}}],"reference-count":77,"publisher":"Public Library of Science (PLoS)","issue":"11","license":[{"start":{"date-parts":[[2020,11,11]],"date-time":"2020-11-11T00:00:00Z","timestamp":1605052800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"NIH","award":["R01 GM113246"],"award-info":[{"award-number":["R01 GM113246"]}]},{"name":"NIH","award":["R01 AI146028"],"award-info":[{"award-number":["R01 AI146028"]}]},{"name":"NIH","award":["R01 AI138709"],"award-info":[{"award-number":["R01 AI138709"]}]},{"name":"NIH","award":["R01 AI120961"],"award-info":[{"award-number":["R01 AI120961"]}]},{"name":"NIH","award":["U19 AI128914"],"award-info":[{"award-number":["U19 AI128914"]}]},{"name":"HHMI\/Simons Foundation","award":["n\/a"],"award-info":[{"award-number":["n\/a"]}]},{"name":"UW\/Fred Hutch Center for AIDS Research","award":["P30 AI027757"],"award-info":[{"award-number":["P30 AI027757"]}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>\n                    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? We evaluate the performance of these methods on a wide variety of simulated samples, as well as two real data samples. These methods are implemented as part of continuing development of the\n                    <jats:monospace>partis<\/jats:monospace>\n                    BCR inference package, available at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/psathyrella\/partis\" xlink:type=\"simple\">https:\/\/github.com\/psathyrella\/partis<\/jats:ext-link>\n                    .\n                  <\/jats:p>\n                  <jats:p>Comments<\/jats:p>\n                  <jats:p>\n                    Please post comments or questions on this paper as new issues at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/git.io\/Jvxkn\" xlink:type=\"simple\">https:\/\/git.io\/Jvxkn<\/jats:ext-link>\n                    .\n                  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