Data-driven engineering of protein therapeutics

Abstract

Protein therapeutics requires a series of properties beyond biochemical activity, including serum stability, low immunogenicity, and manufacturability. Mutations that improve one property often decrease one or more of the other essential requirements for therapeutic efficacy, making the protein engineering challenge difficult. The past decade has seen an explosion of new techniques centered around cheaply reading and writing DNA. This review highlights the recent use of such high throughput technologies for engineering protein therapeutics. Examples include the use of human antibody repertoire sequence data to pair antibody heavy and light chains, comprehensive mutational analysis for engineering antibody specificity, and the use of ancestral and inter-species sequence data to engineer simultaneous improvements in enzyme catalytic efficiency and stability. We conclude with a perspective on further ways to integrate mature protein engineering pipelines with the exponential increases in the volume of sequencing data expected in the forthcoming decade.