Uncursing winner's curse: on-line monitoring of directed evolution convergence

Directed evolution (DE) is a versatile protein-engineering strategy, successfully applied to a range of proteins, including enzymes, antibodies, and viral vectors. However, DE can be time-consuming and costly, as it typically requires many rounds of selection to identify desired mutants. Next-generation sequencing allows monitoring of millions of variants during DE and can be leveraged to reduce the number of selection rounds. Unfortunately the noisy nature of the sequencing data impedes the estimation of the performance of individual variants. Here, we propose ACIDES that combines statistical inference and in-silico simulations to improve performance estimation in DE by providing accurate statistical scores. We tested ACIDES first on a novel random-peptide-insertion experiment and then on several public datasets from DE of viral vectors and phage-display. ACIDES allows experimentalists to reliably estimate variant performance on the fly and can aid protein engineering pipelines in a range of applications, including gene therapy.