Accelerate targeted development of molecules in the laboratory using a new robotic platform


Bacteriophage Phi X 174 Electron Microphotography. Credit: Wikipedia / CC BY-SA 4.0

Natural evolution is a slow process that depends on the gradual accumulation of genetic mutations. In recent years, researchers have found ways to speed up the process on a small scale, allowing them to quickly create new proteins and other molecules in their laboratory.

This widely used technique, known as directed evolution, has provided new antibodies for the treatment of cancer and other diseases, enzymes used for the production of biofuels, and magnetic resonance imaging (MRI) imaging agents.

Researchers at MIT have now developed a robotic platform that can perform 100 times as many experiments with targeted development in parallel, giving many more populations the chance to come up with a solution while monitoring their progress in real time. In addition to helping scientists develop new molecules faster, the technique can also be used to simulate natural evolution and answer basic questions about how it works.

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

MIT graduate student Erika DeBenedictis and postdoc Emma Chory are the lead authors of the paper, which will be published today in The methods of nature.

Quick development

Controlled evolution works by speeding up the accumulation and selection of new mutations. For example, if researchers wanted to create an antibody that binds to a cancer protein, they would start with a test tube containing hundreds of millions of yeast cells or other microbes that have been engineered to express mammalian antibodies on their surfaces. These cells would be exposed to the cancer protein that the researchers wanted the antibody to bind to, and the researchers would select the ones that bind best.

Researchers would then introduce random mutations into the antibody sequence and screen these new proteins again. The process can be repeated many times until the best candidate shows up.

About 10 years ago, as a graduate student at Harvard University, Esvelt developed a way to accelerate directed evolution. This approach utilizes bacteriophages (viruses that infect bacteria) to help proteins develop faster toward a desired function. The gene that the researchers hope to optimize is linked to a gene necessary for bacteriophage survival, and the viruses compete against each other to optimize the protein. The selection process is run continuously, shortening each mutation round to the lifespan of the bacteriophage (which is about 20 minutes), and can be repeated many times without the need for human intervention.

Using this method, known as subject-assisted continuous evolution (PACE), controlled evolution can be performed 1 billion times faster than traditional directed evolution experiments. However, evolution often fails to find a solution, requiring scientists to guess which new set of conditions will perform better.

The technique described in the new The methods of nature paper, which researchers have named professional and robotic technology-assisted near-continuous evolution (PRANCE), can evolve 100 times as many populations in parallel using different conditions.

In the new PRANCE system, bacteriophage populations (which can only infect a specific bacterial strain) are grown in wells on a 96-well plate instead of a single bioreactor. This allows many more evolutionary orbits to occur simultaneously. Each virus population is monitored by a robot as it undergoes the evolutionary process. When the virus succeeds in generating the desired protein, it produces a fluorescent protein that the robot can detect.

“The robot can babysit this population of viruses by measuring this readout, which allows it to see if the viruses are doing well or if they are really fighting, and something needs to be done to help them,” says DeBenedictis.

If viruses struggle to survive, which means the target protein does not evolve as desired, the robot can help save them from extinction by replacing the bacteria they infect with another strain that makes it easier for viruses to replicate . This prevents the population from dying out, which is a cause of failure for many directed evolutionary attempts.

“We can tune these developments in real time, in direct response to how well these developments are happening,” says Chory. “We can see when an experiment succeeds and we can change the environment, which gives us many more shots at targets, which is amazing from both a bioengineering perspective and a basic scientific perspective.”

New molecules

In this study, the researchers used their new platform to construct a molecule that allows viruses to encode their genes in a new way. The genetic code for all living organisms determines that three DNA base pairs specify one amino acid. However, the MIT team was able to develop several viral transfer RNA (tRNA) molecules that read four DNA base pairs instead of three.

In another experiment, they developed a molecule that allows viruses to incorporate a synthetic amino acid into the proteins they make. All viruses and living cells use the same 20 naturally occurring amino acids to build their proteins, but the MIT team was able to generate an enzyme that can incorporate an extra amino acid called Boc-lysine.

Researchers are now using PRANCE to try to make new drugs with small molecules. Other possible uses for this form of large-scale controlled evolution include attempts to develop enzymes that degrade plastics more efficiently, or molecules that can edit the epigenome, in the same way that CRISPR can edit the genome, the researchers say.

With this system, scientists can also gain a better understanding of the step-by-step process leading to a particular evolutionary outcome. Because they can study so many populations in parallel, they can adjust factors such as the mutation rate, the size of the native population, and environmental conditions and then analyze how these variations affect the outcome. This type of large-scale, controlled experiment could allow them to potentially answer basic questions about how evolution occurs naturally.

“Our system allows us to actually carry out these developments with significantly more understanding of what’s going on in the system,” says Chory. “We can learn about the history of evolution, not just the end point.”

Team engineers directed development of translation system for efficient incorporation of unnatural amino acids

More information:
Erika A. DeBenedictis et al., Systematic molecular evolution enables robust biomolecule discovery, The methods of nature (2021). DOI: 10.1038 / s41592-021-01348-4

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