Over the past two years, machine learning has revolutionized protein structure prediction. This has been led by experimentally characterized de novo protein designs that have been generated using physically based approaches. Now, a similar revolution in protein design is described.
In recently published work, a team from the lab of Breakthrough Prize winner David Baker, PhD, professor of biochemistry at the University of Washington School of Medicine, showed that machine learning can be used to create protein molecules much more accurately and quickly than previously possible. They described a deep learning–based protein sequence design method, ProteinMPNN, and its outstanding performance in both in silico and experimental tests. The scientists hope this advance will lead to many new vaccines, treatments, tools for carbon capture, and sustainable biomaterials.
