directed evolution

Season 1, Episode 2: Evolving Enzymes to Create Unnatural Compounds with Tina Boville

First Author: Christina Boville

Episode Summary

Commodity molecules are vital ingredients for everything important to our modern world, including food, energy, and medicine. However, creating these molecules still largely relies on old processes that suffer from low yield, laborious methods, and unsustainable inputs and byproducts. Tina envisions a world where all molecules are created quickly, easily, and sustainably through enzymes, biology’s chemical catalyst. Here, Tina describes how she used an extremely powerful method called directed evolution to build a novel enzyme that can create the non-canonical amino acid 4-cyanotryptophan, a fluorescent molecule that is extremely difficult to make with traditional chemistry.

About the Author

  • Tina performed this work as a postdoc in the lab of Nobel Laureate Professor Frances Arnold at Caltech. The lab is world renowned for developing the methods around directed evolution and applying them to create proteins that do unnatural chemistries. 

  • Tina is now the co-founder and CEO at Aralez Bio whose focus is on developing efficient, sustainable alternatives to chemical manufacturing through enzyme engineering. 

Key Takeaways

  • Enzymes are proteins that induce specific chemical reactions to occur. They can create molecules much more efficiently and sustainably than using traditional chemistry

  • One class of molecules, called non-canonical amino acids, are extremely important precursors to drugs and have specific properties that make them desirable for biotech.

  • Making highly pure non-canonical amino acids is difficult with traditional chemistry, requiring many time-consuming reactions and toxic byproducts. But nature has yet to generate an enzyme that can create these.

  • A process called directed evolution mimics nature’s process by heavily mutating a starting enzyme and sequentially pushing it to make a molecule of interest.

  • When using directed evolution, “you get what you screen for”. Said another way: the outcome of the process is highly dependent on how the experiment was run and what was optimized for.

  • With directed evolution, the non-canonical amino acid 4-cyanotryptophan is generated overnight with no harmful byproducts; something that would take a team of chemists months to do.

Translation

  • The evolved enzyme that creates 4-cyanotryptophan became the cornerstone technology of Aralez Bio.

  • Tina spent the last parts of her postdoc defining customers and building a team to launch the company.

  • Through enzyme engineering, Aralez Bio plans to replace many unsustainable and time consuming chemistries that currently plague commodity molecules.

PaperImproved Synthesis of 4-Cyanotryptophan and Other Tryptophan Analogues in Aqueous Solvent Using Variants of TrpB from Thermotoga maritima. Journal of Organic Chemistry, 2018


Season 1, Episode 1: Low-N Protein Engineering with Surge Biswas

First Author: Surojit “Surge” Biswas

Episode Summary

Protein engineering has been dominated by two opposing paradigms; directed evolution, a massive screening technique, and rational design, a completely computational approach. Surge has fused these two paradigms by developing a machine learning technique that discovers an optimal protein design by training on a low number of engineered proteins. Here, Surge discusses how this hybrid method works, how it enabled the creation of better fluorophores and enzymes, and what this method will unlock next.

About the Author

  • Surge performed this work as a graduate student at Harvard in the lab of Professor George Church. George is one of the founding fathers of synthetic biology and the lab is known for developing high throughput methods to design, build, and test bioengineered parts.

  • As CEO and co-founder of Nabla Bio, Surge is now focused on pointing the algorithms and methods developed in his academic work toward building proteins that can improve human health or protect the environment.

Key Takeaways

  • Methods from natural language processing algorithms (like Siri or Alexa) are adapted to understand how nature builds proteins.

  • These machine learning algorithms distill fundamental structural, as well as  evolutionary and and biophysical properties about proteins.

  • Fusing these models with real world data enables us to make proteins with improved or novel functionality.

  • By checking how a few mutations (low-n) affect the function of a protein, Surge evolved proteins in a computer to make better fluorescent proteins and enzymes.

  • These models know a lot about proteins in general and can therefore be applied to a wide variety of tasks that improve human health and protect the environment.

Translation

  • This methodology dramatically reduces the time, cost, and labor of evolving proteins, making it a perfect tool to create commodity proteins.

  • Based on this technology, Surge co-founded Nabla Bio whose goal is to engineer supernatural proteins that enable biology to solve the world's biggest problems.

PaperLow-N protein engineering with data-efficient deep learning. bioRxiV, 2020