Protein engineering is a powerful tool in synthetic biology that can create proteins with tremendous potential for therapeutic and industrial use. The field has recently taken giant leaps forward from its origins, and some of its pioneers predict that the next five to ten years hold exponentially more promise.
True protein engineering is building proteins completely from scratch, engineered to do what you want them to do.
Early protein engineering was what David Baker, Ph.D., director of the Institute for Protein Design at the University of Washington, called “Neanderthal protein design.” “You’d look around in nature for something where you could make a couple of amino acid substitutions. It’s like when early humans needed a tool, they’d sharpen a stick or bone for their purposes. But that’s not really protein engineering. Rather than strapping a human onto a bird, you figure out the principles of flight and design something like an airplane. That’s true protein engineering: building proteins completely from scratch, engineered to do what you want them to do.”
Two decades ago, Dr. Baker and his colleagues created a novel protein folding program called Rosetta, which predicted protein structures from their amino acid sequences—a project that quickly grew into a crowdsourced system called Rosetta@home, which allows people to donate idle computer time to the vast computational resources needed to map all the probable protein folds. With this vast network of scientists and home enthusiasts involved, Rosetta was able to move beyond predicting the structures of proteins that exist in nature to designing completely new “unnatural” proteins—and then to custom-designed proteins for particular purposes.
Influenza virus
For example, they worked with virologist Ian Wilson, Ph.D., a professor of structural biology at the Scripps Research Institute, to design a protein that would exploit a vulnerability on the surface of the influenza virus. Using Rosetta, they identified candidate chains of amino acids that might fold in the right way, and tested the top candidates on the real flu virus. The most successful of the new proteins, which they named HB1.6928.2.3, proved 100% protective against death from the flu in a mouse model in a study published in Nature in October 2017.
“We can now design self-assembling particles that resemble viruses that are completely synthetic, get them to encapsulate their own nucleic acids, and evolve them for whatever purpose we want,” Dr. Baker says. “Besides designing tiny little mini-proteins that bind tightly to targets like the influenza hemagluttinin, we’ve been able to design membrane proteins and all new types of channels and sensors from scratch.”
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Dr. Baker’s next set of papers—which he says will be out by the end of 2018 or early 2019—will be focused on using these techniques to make new medicines. But that’s just the beginning. “We can make new fluorescent proteins for probes and diagnostics and more sophisticated medicines that can recognize targets with higher specificity—mimics of naturally occurring hormones that have improved pharmacological properties. I try not to predict the future, but I hope that within the next five years, we will have entirely new classes of medicines and materials based on de novo protein designs.”
On the new materials front, Dr. Baker envisions novel solar cells, self-assembling systems that have improved properties for energy applications, engineered spider silk that scales up the natural version’s tensile strength and flexibility, and protein mineral composites. “Seashells are very hard. What if you could make a seashell in any shape you wanted?” he queries. “We’re going to be busy.”
Computational methods
Philip Romero, Ph.D., an assistant professor of biochemistry at the University of Wisconsin, uses computational methods to investigate the relationships between protein sequence, structure, and function—identifying sequences tied to useful properties that he can then engineer into new proteins with the functions he wants. “We generate sequence function data sets, see how the sequence maps to some property we want to optimize, and use ideas from statistics and machine learning to understand how interactions between residues contribute to those properties.”
To kick-start the process, his lab is combining artificial intelligence with robotic automation. “In the past, a grad student or post-doc would make the mutations in a protein, evaluate the effects, and come up with a hypothesis for how the protein is working. Then they would make more mutations, repeat the process five or ten times, and slowly get something improved. We think we can fully automate the entire process.”
Once the computational methods his lab has developed identify key protein design sequences, they’re handed over to a liquid-handling robot that mimics the process of a protein engineer in an automated fashion. “The robot can be working around the clock, optimizing,” Dr. Romero says. “We can let this thing go for a week or so and it will have gone through tens of rounds of optimization without any need for input from a human.”
Currently, his group is using the system to engineer enzymes for biomass desconstruction as part of the Great Lakes Bioenergy Reseach Center funded by the DOE. “The goal is to optimize enzymes that can degrade biomass into sugars to in turn be transformed into fuel. We expect to have some solid preliminary results within the next year.”
Many problems remain to be solved in protein engineering. “We have design methods today that did not exist five years ago, but they’re far from perfect,” says Dr. Baker. “We need more control of the accuracy of our designs. One outstanding challenge is developing catalysts for reactions for which there currently are no catalysts. That’s a very hard problem. Another critical issue is understanding how these designed proteins interact with biological systems. We have put a bunch of them into mice and they do not appear to be immunogenic, but they haven’t gone into people yet.”