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Artificial Intelligence and Improved Drugs: Another Big Step Forward
Designing drugs that target proteins requires knowing the three-dimensional (3D) shape of the target protein and creating a drug whose shape binds to that target protein. As of June 2021, nearly a century of research had determined the shape of only about one third of human proteins. Then, in July 2021, two groups reported using artificial intelligence (AI) to predict the shape of nearly every human protein from their nucleic acid sequences (NEJM JW Gen Med Sep 1 2021 and Nature 2021; 596:590 and 583). In an editorial, Science called it the scientific breakthrough of the year. Yet skeptics hypothesized that new drugs based on this knowledge would be far in the future.
The same AI software that predicted the shape of proteins in the 2021 experiments now has been used to design small strings of 50 to 65 amino acids (“mini-antibodies”) with 3D shapes that were predicted to bind to 13 important human target proteins: growth factors that are important in stimulating growth of cancers, and components of several infectious agents, including influenza and SARS-CoV-2. The mini-antibodies were shown to bind to their targets and to work as expected — for example, a mini-antibody designed to bind to the SARS-CoV-2 spike protein protected mice from infection.
These AI-designed mini-antibodies constitute a novel new class of drugs that are simple and inexpensive to produce, in contrast to monoclonal antibodies. Whether such drugs will prove to be effective against human disease obviously will require clinical trials. Such trials are expected to begin in 2023 — just 2 years after a discovery that some skeptics dismissed as “hype.”
Cao L et al.
Title: Design of protein-binding proteins from the target structure alone.
Source: Nature 2022 May ; [e-pub]. (Abstract/FREE Full Text)
Title: Software-designed miniproteins could create new class of drugs.
Source: Science 2022 Apr ; [e-pub]. (Abstract/FREE Full Text)