Enzyme evolution has enabled numerous advances in biotechnology. However, directed evolution programs can still require many iterative rounds of screening to identify optimal mutant sequences. This is due to the sparsity of the fitness landscape, which in turn, is due to hidden mutations that only offer improvements synergistically in combination with other mutations. These hidden mutations are only identified by evaluating mutant combinations, necessitating large combinatorial libraries or iterative rounds of screening. Here, we report a multi-agent directed evolution approach that incorporates diverse substrate analogues in the screening process. With multiple substrates acting like multiple agents navigating the fitness landscape, we are able to identify hidden mutant residues that impact substrate specificity without a need for testing numerous combinations. We initially validate this approach in engineering a malonyl-CoA synthetase for improved activity with a wide variety of non-natural substrates. We found that hidden mutations are often distant from the active site, making them hard to predict using popular structure-based methods. Interestingly, many of the hidden mutations identified in this case are expected to destabilize interactions between elements of tertiary structure, potentially affecting protein flexibility. This approach may be widely applicable to accelerate enzyme engineering. Lastly, multi-agent system inspired approaches may be more broadly useful in tackling other complex combinatorial search problems in biology.
Exploiting enzyme evolution for computational protein design
