Google DeepMind has announced the launch of AlphaProteo, an AI system that helps researchers in biology and health design new high-strength proteins that bind precisely and strongly to target molecules.
AlphaProteo was trained on the Protein Data Bank (PDB) to enable breakthroughs in science and education by providing access and tools for exploring, visualizing, and analyzing experimentally determined 3D structures from the PDB archive.
AlphaProteo generates candidate proteins that bind to a target based on the structure of the target molecule and a set of preferred binding sites on that molecule.
The tech giant said the binder has the potential to open up new areas of research in drug development and diagnostic biosensors.
“AlphaProteo can generate novel protein binders for a range of target proteins, including VEGF-A, which is associated with cancer and diabetic complications. This is the first time that an AI tool has successfully designed a protein binder for VEGF-A,” Google DeepMind’s protein design and wet lab teams said in a blog post.
“AlphaProteo achieved higher experimental success rates and 3-300-fold better binding affinities for the seven target proteins we tested than the best existing methods.”
To test AlphaProteo, the AI’s developers designed binders for a range of target proteins, including “two viral proteins involved in infection, BHRF1 and the SARS-CoV-2 spike protein receptor binding domain, SC2RBD, as well as five proteins involved in cancer, inflammation and autoimmune diseases: IL-7Rɑ, PD-L1, TrkA, IL-17A and VEGF-A.”
The binding success rate for one of the viral targets, BHRF1, averaged 88%, 10 times higher than previous methods.
The Google DeepMind Web Lab team collaborated with external research groups, including researchers from the Francis Crick Institute, and the data confirmed that AlphaProteo binders prevented SARS-CoV-2 from infecting human cells.
AlphaProteo has demonstrated the ability to reduce the time required for initial experimentation with protein binders for a variety of applications.
But despite the breakthroughs, the researchers noted that AI systems have limitations.
For example, AlphaProteo did not produce a suitable binder for TNFα, a protein associated with autoimmune diseases such as rheumatoid arthritis.
“Computational analysis showed that designing binders against TNFɑ is extremely challenging, so we chose TNFɑ to pose a strong challenge to AlphaProteo. We will continue to improve and extend the capabilities of AlphaProteo with the goal of ultimately addressing such challenging targets,” the authors write.
The AlphaProteo research team plans to collaborate with the scientific community to observe the impact of AlphaProteo on other biological problems and to further understand its limitations.
Additionally, the team has been exploring its drug design applications at Isomorphic Labs.
Larger trends
In June, Google Research and Google DeepMind published a paper announcing the creation of a new LLM for drug discovery and therapeutic development, called Tx-LLM, that is a fine-tuning of Med-PaLM 2.
The tech giant’s Med-PaLM 2 is a generative AI technology that uses Google’s LLM to answer medical questions.
A study conducted by Google Research in collaboration with Google DeepMind in May showed that the tech giant had expanded the capabilities of its Med-Gemini-2D, Med-Gemini-3D, and Med-Gemini Polygenic AI models.
Google said it has fine-tuned Med-Gemini’s capabilities using data from histopathology, dermatology, 2D and 3D radiology, genomics and ophthalmology.
In 2023, Google released MedLM, two foundational models built on Med-PaLM 2 designed to answer medical questions, generate insights from unstructured data, and summarize medical information.
While piloting LLM in healthcare organizations, the company said it learned that the most effective AI models are designed to address specific use cases.
As a result, a large-scale model of MedLM will be created to handle complex tasks, and a mid-scale model that can be fine-tuned and expanded across a range of tasks.
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