Optimizing directed evolution of molecules in the lab

In recent years, scientists have discovered ways to speed up the process on a small scale. This allows them to create new proteins and other molecules in their lab rapidly.

Using a directed evolution technique, they yielded new antibodies to treat cancer and other diseases, enzymes used in biofuel production, and imaging agents for magnetic resonance imaging (MRI).

Scientists at MIT have developed a robotic platform that can perform 100 times as many directed-evolution experiments in parallel. This offers a chance to create a solution while monitoring their progress in real-time.

Along with developing new molecules instantly, the new technique could also simulate natural evolution and answer fundamental questions about how it works.

Kevin Esvelt, an assistant professor in MIT’s Media Lab and the senior author of the new study, said, “Traditionally, directed evolution has been much more of an art than a science, let alone an engineering discipline. And that remains true until you can systematically explore different permutations and observe the results.”

Using a phage-assisted continuous evolution (PACE) method, the direct evolution can be performed 1 billion times faster than traditional directed evolution experiments. However, the technique often fails to come up with a solution. Hence, scientists need to guess which new conditions will do better.

In a new study, scientists demonstrated a new technique called phage and robotics-assisted near-continuous evolution (PRANCE) that can evolve 100 times as many populations in parallel, using different conditions.

In the new PRANCE system, bacteriophage populations are grown in wells of a 96-well plate instead of a single bioreactor. This allows for many more evolutionary trajectories to occur simultaneously.

A robot monitors each viral population as it goes through the evolution process. When the virus successfully generates the desired protein, it produces a fluorescent protein that the robot can detect.

MIT graduate student Erika DeBenedictis said, “The robot can babysit this population of viruses by measuring this readout, which allows it to see whether the viruses are performing well, or whether they’re struggling and something needs to be done to help them.”

If the viruses are struggling to survive, the target protein is not evolving in the desired way. The robot can help save them from extinction by replacing the bacteria they’re infecting with a different strain, making it easier for the viruses to replicate. This prevents the population from dying out, which is a cause of failure for many directed evolution experiments.

Postdoc Emma Chory said, “We can tune these evolutions in real-time, in direct response to how well these evolutions are occurring. We can tell when an experiment is succeeding, and we can change the environment, which gives us many more shots on goal, which is great from both a bioengineering perspective and a basic science perspective.”

Scientists used their new platform to engineer a molecule that allows viruses to encode their genes in a new way. The genetic code of all living organisms stipulates that three DNA base pairs specify one amino acid.

However, scientists could evolve several viral transfer RNA (tRNA) molecules that read four DNA base pairs instead of three.

In another experiment, scientists evolved a molecule that allows viruses to incorporate a synthetic amino acid into the proteins they make. All viruses and living cells use 20 naturally occurring amino acids to build their proteins. Still, scientists, in this study, generated an enzyme that can incorporate an additional amino acid called Boc-lysine.

Using PRANCE, scientists are trying to make novel small-molecule drugs.

Scientists noted, “Other possible applications for this kind of large-scale directed evolution include trying to evolve enzymes that degrade plastic more efficiently, or molecules that can edit the epigenome, similarly to how CRISPR can edit the genome.”

With this system, scientists can also better understand the step-by-step process that leads to a particular evolutionary outcome. Because they can study so many populations in parallel, they can tweak factors such as the mutation rate, original population size, and environmental conditions and then analyze how those variations affect the outcome.

Chory said, “Our system allows us to perform these evolutions with substantially more understanding of what’s happening in the system. We can learn about the history of the evolution, not just the endpoint.”

Journal Reference:

1. Erika A. DeBenedictis et al. Systematic molecular evolution enables robust biomolecule discovery. DOI: 10.1038/s41592-021-01348-4