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Pharmaceutical and biotech companies looking for new drugs are doing tests along the way to see how proteins interact. Analysis of the protein-protein interaction can reveal how cells communicate, how genes are regulated, and how the immune system identifies and attacks disease, said David Younger, co-founder and CEO of A-Alpha Bio. It is a necessary process but it takes time. For businesses large and small, this usually means testing one protein interaction at a time.
A-Alpha Bio’s technology allows millions of protein interactions to be tested simultaneously. The Seattle-based startup already has a handful of biotech industry partners. She now has $ 20 million to expand the capabilities of the technology and grow the business. The Series A funding round announced on Wednesday was led by Madrona Venture Group; Perceptive Xontogeny Venture Fund and Lux Capital have also invested.
A-Alpha Bio is part of the growing contingent in proteomics, the large-scale study of proteins. This area has become a hot zone for investment. Nautilus Biotechnology was recently made public in a merger deal that earned it $ 350 million. The Seattle-based company technology produces protein landscape maps that provide insight into disease pathways and progression. Seer, based in Redwood City, Calif., Raised $ 55 million last year to support the development of its protein analysis platform. As these companies detect proteins in a sample, Younger said that A-Alpha Bio is working in a different area of proteomics, finding out which proteins bind to each other.
The startup’s approach begins with yeast, the same yeast that is used to make bread and beer. Yeast cells are frequently used in scientific experiments because they have some similarities to human cells and they are highly manipulable. A-Alpha Bio’s technology, called AlphaSeq, is a lab method the company developed to use genetically engineered yeast cells to test millions of protein interactions at the same time.
The methods traditionally used to test for protein interactions come with tradeoffs between quantity and quality, Younger said. To get high-quality results, scientists test two proteins in one experiment. This approach is not scalable. If quantity is the goal, technology platforms from companies such as Adimab and Distributed Bio analyze a large number of proteins. But every measure is shoddy, Younger said.
AlphaSeq was developed to deliver quality and quantity in the same test. The company has focused its research in two areas: the discovery of antibodies and the identification of targets for molecular glues, which are a key part of an emerging therapeutic approach called targeted protein degradation. A-Alpha Bio partners with pharmaceutical companies to help them discover these targets. Publicly disclosed partners include Twist Biopharma, which is a division of South San Francisco-based Twist Bioscience, and Seattle-based Lumen Bioscience.
Younger did not set out to make better antibodies or molecular glues. A-Alpha Bio’s roots stretch back almost a decade to his days as a graduate student at the Institute for Protein Design and the Center for Synthetic Biology at the University of Washington. At the time, he was trying to solve a problem that he and many of his peers were facing. Using computers, it is common for a student to design up to a thousand proteins in a week, or even in a single day.
“Because of the maturity of computer protein design, it has come a long way,” Younger said. “But you still have to test them. This is the bottleneck. It is impossible to test all of these proteins.
Young’s graduate work consisted of developing the platform that would become AlphaSeq. As the research progressed, he was faced with the question of how to maximize his impact. Publishing an article would make the research available for others to pursue. He also thought of laying off research at a company. He chooses a third option: to start a business.
A-Alpha Bio was formed in 2017. The following year, the startup received its first research grant for innovation in small businesses, a phase 1 award for the development of a profiling platform. of drugs using yeast cells. The company’s focus on antibodies and molecular glues stems from the client discovery component of grant preparation. Younger said he and his team had been pressured to go out and talk to as many experts in the pharmaceutical industry as possible to identify the needs of the market. In the case of antibodies, the problem Younger has heard over and over again is that a company may have thousands of drug candidates, but their tools allow them to select only one at a time. Therefore, maybe 10 or less antibodies undergo full testing, as it is simply not possible to test all of them.
Molecular glues are a new area of research for industry as the targeted degradation of proteins is still a new area of research. The technology involves tagging a disease-causing protein for removal by the cell’s integrated machinery to get rid of old or damaged proteins. The challenge is that not all proteins have the affinity to stick to the molecular tag that marks a protein for elimination. This is where molecular glue comes in. A-Alpha Bio received a Phase II grant last year to develop its molecular glue discovery technology.
At the moment, A-Alpha Bio’s technology supports drug research from larger partners. The startup receives an upfront payment under agreements that also commit milestone payments and, when therapy commercializes, royalties on sales. Going forward, Younger sees A-Alpha Bio using its technology to create an internal drug pipeline. In the shorter term, A-Alpha Bio’s research could contribute to the development of drugs against Covid-19. The startup is looking for antibodies that can bind to SARS-CoV-2, research with Lumen supported by a grant from the Bill & Melinda Gates Foundation. A year ago, A-Alpha Bio received a Phase 1 SBIR grant to develop an antibody generation platform for coronavirus variants.
The ability to test millions of protein interactions means that AlphaSeq produces a lot of data. Younger said the next steps for A-Alpha Bio include building capacity to analyze this data. The company aims to use machine learning techniques to predict how proteins interact. Over the next 18-24 months, Younger expects the startup’s workforce of 13 to grow to 50, with many of those hires joining the machine learning and data science team, led by Ryan. Emerson, an adaptive biotechnology veteran.
“Over time, we will accumulate the greatest repository of protein interactions,” Younger said. “As we build this database, we can use tools like machine learning to start making engineered protein interactions a computational problem rather than an experimental problem. “
Photo by Flickr user Roger W via Creative Commons license
