New AI tool may help find unknown proteins and design new ones
September 19 2022 12:11 AM

Artificial Intelligence (AI), machine learning and deep learning have become ubiquitous terms in business, commerce and technology. While AI is the concept of creating smart intelligent machines, machine learning is a subset of AI that helps to build AI-driven applications and deep learning a subset of machine learning that uses vast volumes of data and complex algorithms to train a model. In this context, it is fascinating that a new AI tool, ProteinMPNN, could help researchers discover previously unknown proteins and design entirely new ones. When harnessed, it could help unlock the development of more efficient vaccines, speed up research for the cure to cancer, or lead to completely new materials.
Two papers published in Science last Thursday by a group from the University of Washington are the latest examples of how deep learning is revolutionising protein design by giving scientists new research tools. Traditionally researchers engineer proteins by tweaking those that occur in nature, but ProteinMPNN will open an entire new universe of possible proteins for researchers to design from scratch. “In nature, proteins solve basically all the problems of life, ranging from harvesting energy from sunlight to making molecules. Everything in biology happens from proteins,” said David Baker, one of the scientists behind the paper and director of the Institute for Protein Design at the University of Washington.
Alphabet-owned AI lab DeepMind had in 2020 announced AlphaFold, an AI tool that used deep learning to solve one of the “grand challenges” of biology: accurately predicting the shapes of proteins. Proteins are fundamental to life, and understanding their shape is vital to working with them. Earlier this summer DeepMind announced that AlphaFold could now predict the shapes of all proteins known to science. Proteins consist of hundreds of thousands of amino acids that are linked up in long chains, which then fold into three-dimensional shapes. AlphaFold helps researchers predict the resulting structure, offering insight into how they will behave.
ProteinMPNN system uses a neural network trained on a very large number of examples of amino acid sequences, which fold into three-dimensional structures. To design proteins that are useful for real-world applications, such as a new enzyme that digests plastic, researchers first have to figure out what protein backbone would have that function. To do that, researchers in Baker’s lab use two machine-learning methods, detailed in an article in Science last July, that the team calls “constrained hallucination” and “in painting.” Using these methods, the researchers can create a completely new protein that hasn’t been seen in nature before, such as a giant ring-like structure. Baker’s team is experimenting with whether those ring-like structures could be used as components of tiny machines that operate at the nanoscale. In the future, these nanomachines could be used to unclog arteries, for example. “These contributions and others recently are transforming the field of biomolecular structure prediction and design,” said Jeffrey Gray, a professor of chemical and biomolecular engineering at Johns Hopkins University. The implications are dramatic in terms of understanding biology, health and disease, and in designing new molecules to reduce human suffering. Gray’s lab will combine deep-learning tools they developed with ones from the Baker lab to better understand the immune system and immune-related diseases, and use AI to design therapeutics.

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