Directed protein evolution for generating antibodies with improved binding affinity or stability requires exploration of a vast space of possible mutations. Experimental high-throughput antibody engineering methods screen thousands to millions of variants using techniques like phage display or cell surface display. This imposes a heavy experimental burden. Computational methods provide a structure-guided rationale for selecting mutations, typically within the complementarity-determining regions, but still require experimental testing of many mutants.
Better prior information on evolutionary plausibility helps improve protein engineering efficiency.
A team of researchers led by Peter Kim at Stanford University has performed guided protein evolution using protein language models that were trained on millions of natural protein sequences. The models thereby learn amino acid patterns that are likely to be seen in nature. “Because the models are trained on millions of protein sequences produced by natural evolution, they are also helpful in suggesting mutations that are likely to have a