Author:
Petersen Brian M.,Kirby Monica B.,Chrispens Karson M.,Irvin Olivia M.,Strawn Isabell K.,Haas Cyrus M.,Walker Alexis M.,Baumer Zachary T.,Ulmer Sophia A.,Ayala Edgardo,Rhodes Emily R.,Guthmiller Jenna J.,Steiner Paul J.,Whitehead Timothy A.
Abstract
Antibodies are engineerable quantities in medicine. Learning antibody molecular recognition would enable thein silicodesign of high affinity binders against nearly any proteinaceous surface. Yet, publicly available experiment antibody sequence-binding datasets may not contain the mutagenic, antigenic, or antibody sequence diversity necessary for deep learning approaches to capture molecular recognition. In part, this is because limited experimental platforms exist for assessing quantitative and simultaneous sequence-function relationships for multiple antibodies. Here we present MAGMA-seq, an integrated technology that combinesmultipleantigens andmultipleantibodies and determines quantitative biophysical parameters using deepsequencing. We demonstrate MAGMA-seq on two pooled libraries comprising mutants of ten different human antibodies spanning light chain gene usage, CDR H3 length, and antigenic targets. We demonstrate the comprehensive mapping of potential antibody development pathways, sequence-binding relationships for multiple antibodies simultaneously, and identification of paratope sequence determinants for binding recognition for broadly neutralizing antibodies (bnAbs). MAGMA-seq enables rapid and scalable antibody engineering of multiple lead candidates because it can measure binding for mutants of many given parental antibodies in a single experiment.
Publisher
Cold Spring Harbor Laboratory