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Workshop: Machine Learning in Structural Biology Workshop

Antibody optimization enabled by artificial intelligence predictions of binding affinity and naturalness

Sharrol Bachas · Goran Rakocevic · David Spencer · Anand Sastry · Robel Haile · John Sutton · George Kasun · Andrew Stachyra · Jahir Gutierrez · Edriss Yassine · Borka Medjo · Vincent Blay · Christa Kohnert · Jennifer Stanton · Alexander Brown · Nebojsa Tijanic · Cailen McCloskey · Rebecca Viazzo · Rebecca Consbruck · Hayley Carter · Simon Gottreich-Levine · Shaheed Abdulhaqq · Jacob Shaul · Abigail Ventura · Randal Olson · Engin Yapici · Joshua Meier · Sean McClain · Matthew Weinstock · Gregory Hannum · Ariel Schwartz · Miles Gander · Roberto Spreafico


Traditional antibody optimization approaches involve screening a small subset of the available sequence space, often resulting in drug candidates with suboptimal binding affinity, developability or immunogenicity. Based on two distinct antibodies, we demonstrate that deep contextual language models trained on high-throughput affinity data can quantitatively predict binding of unseen antibody sequence variants. These variants span a KD range of three orders of magnitude over a large mutational space. Our models reveal strong epistatic effects, which highlight the need for intelligent screening approaches. In addition, we introduce the modeling of “naturalness”, a metric that scores antibody variants for similarity to natural immunoglobulins. We show that naturalness is associated with measures of drug developability and immunogenicity, and that it can be optimized alongside binding affinity using a genetic algorithm. This approach promises to accelerate and improve antibody engineering, and may increase the success rate in developing novel antibody and related drug candidates.

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