AI tools such as ChatGPT are dramatically changing the way text, images, and code are generated. Similarly, machine learning algorithms and generative AI are disrupting conventional methods in life sciences and accelerating timelines in drug discovery and materials development.
DeepMind’s AlphaFold is arguably the most renowned machine learning model in this domain. It predicts a protein’s 3D structure from its amino acid sequence and has been utilized by over a million researchers in the 18 months since its public release. Numerous other AI tools have emerged since then, including the recently open-sourced RFDiffusion, which allows researchers to generate computational protein designs using only their laptops.
However, translating these computational designs into tangible, functional proteins remains a challenge. Adaptyv Bio aims to address this issue with its next-generation protein foundry. By integrating advanced robotics, microfluidics, and synthetic biology techniques, Adaptyv Bio is constructing a full-stack platform to enable protein engineers to validate their AI-generated protein designs.
Julian Englert, CEO and co-founder of Adaptyv Bio, said, “Proteins are central to the biorevolution, whether as new medicines, improved enzymes for research and industrial applications, or as materials with unique properties. As a protein designer, you now have access to incredible new AI tools like AlphaFold or RFDiffusion. However, validating your protein designs in the lab to see if they work remains a huge challenge.”
AI models thrive on data for training and improving their predictions. By simplifying the process of generating data about the effectiveness of designed proteins, Adaptyv Bio enables protein engineers and AI models to receive more feedback about their designs, guiding them toward better-performing proteins.
Englert added, “Think of the AI in a self-driving car. To keep the car on the road and reach its destination, the AI model needs a tight feedback loop by obtaining plenty of high-quality data from the car’s camera sensors. The same principle applies to an AI model designing new proteins, with the feedback mechanism involving the actual creation of proteins in our lab and testing their performance.”
Adaptyv Bio was established by a group of engineers from EPFL, the Swiss Federal Institute for Technology in Lausanne, motivated by the time-consuming processes of conducting biological experiments in labs. In 2022, they secured $2.5 million in pre-seed funding from Wingman Venture, after participating in Y Combinator, the world’s most selective startup accelerator. The team has since expanded to 12 engineers with diverse backgrounds in synthetic biology, microengineering, software development, and machine learning. The company is located at the newly constructed Biopole life science campus in Lausanne, Switzerland, where they are developing their technology in cutting-edge lab facilities with picturesque views of Lake Geneva and the Swiss-French Alps.
Adaptyv Bio’s foundry centers around protein engineering workcells—custom, automated setups that miniaturize processes typically requiring multiple laboratory machines, performing them in parallel on tiny microfluidic chips. Users can write experimental protocols (or have AI write them) and the workcells execute the experiments autonomously, while closely controlling and monitoring the experiments’ parameters. All measurement data is automatically processed and uploaded to allow users to refine their machine learning models with each experiment.
Englert said, “Our workcells are fully automated, use 1,000 times fewer reagents than any commercially available alternative, and we can run thousands of different proteins per day on each individual setup. To streamline the experimental workflows, we have developed a lot of custom synthetic biology and automation techniques. Over the next 12 months, we plan to scale up our lab further and increase the number of protein design applications we can support. We also just opened up early access for users to submit their protein design projects to us, and we’re trying to onboard new projects as soon as possible.”
To further accelerate the field of protein engineering, Adaptyv Bio has open-sourced two of their internal tools that have already started gaining traction among researchers and engineers in the field. ProteinFlow is a Python library that allows protein designers to easily create high-quality datasets for better AI models. Automancer is an extensible software platform to run automated experiments, enabling researchers to build their own experimental protocols and integrate different laboratory instruments.
“Our mission is to make protein engineering easier and enable more researchers to design new proteins. Consider the proteins that comprise the incredibly powerful molecular machinery inside every single cell in our body. Imagine the kind of technological progress humanity could make if we could start designing novel proteins for personalized medicines, industrial applications like new enzymes, or better, more sustainable materials,” added Julian Englert.
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