Benchling has launched its new AlphaFold beta feature. As the scientific community seeks to use AlphaFold and other advancements in AI, this new beta feature overcomes challenges of implementation, computing power and resourcing to make for ease of experimentation and integration with AlphaFold on the Benchling platform.
AlphaFold is an AI program developed by DeepMind that can predict the 3D structure of a protein from an amino acid sequence with unprecedented accuracy. Not only is it a scientific breakthrough with huge potential, but it is also emblematic of the new era of modern biotech — data-driven, open-sourced, collaborative and ultimately, faster than ever. Despite this, the vast majority of labs are unable to access AlphaFold today. The software is open source to use, but setting up the machine learning architecture to run the AlphaFold algorithm is extremely complicated, and takes significant engineering bandwidth to use in a stable, sustainable way.
Born out of a Benchling hackathon, the AlphaFold beta feature allows customers to select any amino acid sequence stored in Benchling, request a 3D structure for it, and visualise the results in its platform. Customers can view and interact with the 3D structures in a Molstar (Mol) viewer alongside the primary sequence. The structure files (.pdb format) also can be downloaded for more sophisticated modeling using third party applications. Scientists may readily share these protein structure files with other teammates, further extending the reach and utility of the data output.
Now for the first time, with the AlphaFold beta feature, scientists can not only predict 3D structures of novel proteins directly within Benchling, but also centralise experimental context, collaborate with teammates, and connect with downstream scientific workflows on a single, secure platform.
“Our team gets excited about two things: science and bringing software to science,” said Ashu Singhal, president and co-founder of Benchling. “By making AlphaFold available to the biotech industry at the click of a button, scientists will be able to seamlessly experiment with this exciting advancement and find new ways to leverage AlphaFold output in their research. While the use cases for AlphaFold are still being explored and proven, our goal with the beta feature is to support its community.”
Early beta users
PetMedix, a Cambridge-based veterinary therapeutics biotech, is developing therapeutic antibodies for companion animals, and having the ability to produce AlphaFold structures of our antibodies and antigens allows the business to better understand the biology behind them.
“PetMedix is developing therapeutic antibodies for companion animals, and having the ability to produce AlphaFold structures of our antibodies and antigens allows us to better understand the biology behind them,” said Dr. Albert Vilella, Head of Bioinformatics at PetMedix. “There has been a lot of technological developments in AI that are now being applied to answering biological questions in important fields such as immunology. We see this in the literature, for example in the study of Covid-19 immune response and antibody design, and we are excited to be able to apply these technologies to our antibodies, so we can help save and improve the lives of animals all over the world.”