// Aug 2026
How Can AI-Directed Protein Engineering Improve Genomic Medicines?
AI-directed protein engineering can optimize proteins for genomic medicines when machine learning models are trained on high-quality experimental activity data and paired with rapid design-build-test cycles.
At ElevateBio, we combine automation, protein language models, and active learning to identify protein variants with improved activity for genomic medicine applications.
Gene insertion by large serine recombinases (LSRs) or retrotransposases can make safer, more potent CAR T cell therapies and can be the solution to broadly curing monogenic diseases caused by a variety of DNA changes within the patient population.
Better Starting Proteins Create a Stronger Engineering Foundation
Our computational biologists mined ElevateBio’s large, curated bioinformatic databases to identify a diverse set of novel large serine recombinases and their corresponding attachment sites. Most of these wildtype proteins mediated large gene insertion in human cells, with several outperforming the literature standard Bxb1 molecule. More protein diversity means we can select the optimal LSRs to engineer for each therapeutic challenge.
Engineering in Mammalian Cells Improves Human-Relevant Performance
Engineering in mammalian cells avoids the common trap of finding proteins that work well in bacteria, but not in humans. At ElevateBio, we use experimentally derived activity data from mammalian cells to intelligently discover more potent molecules than those selected by nature, which translate into superior therapies.
Protein Language Models Focus the Search on Higher-Value Design Space
The limited throughput of mammalian cell wet lab experiments makes probing the vast protein design space difficult. Our use of protein language models combined with experimental data enables exploration of the vast and otherwise unmanageable protein fitness activity landscape. By identifying high fitness protein domains near activity maxima, we can rapidly focus our search on protein modifications that improve function.
Automation Accelerates the Design-Build-Test Cycle
With ElevateBio’s automation platform, we can synthesize protein variants in-house, reducing cost and speeding up the design-build-test cycle. After two rounds of machine-learning guided protein optimization completed in just 4 calendar weeks, we found many single mutations that improved large gene insertion, including one that was 2.4-fold better than wildtype protein from nature. By generating pairwise combinations of the most beneficial mutations, we found a new double-mutant variant 3.4-fold more active than wildtype (view and download poster on this topic). These data additionally measured interactions between pairs of individual mutations to facilitate design of higher order mutants which will further improve activity.
The takeaway is clear: protein engineering is most powerful when combining protein language models, machine learning, automation, and mammalian cell-based activity profiling as one integrated system. At ElevateBio, that integrated system helps accelerate protein optimization for genomic medicine development and deliver more effective solutions for our partners.
Gavin Ellis, Senior Scientist, Translational Biology
Gavin Ellis is a Senior Scientist at ElevateBio, where he has worked cross-functionally to improve our best-in-class base editing, large gene insertion, and epigenetic editing modalities.
Before ElevateBio, Gavin spent nine years at the Center for Cellular Immunotherapies at the University of Pennsylvania generating novel CAR and TCR transgenic Treg therapeutics for autoimmune disease and allotransplantation.
