Communications of the ACM
New Algorithms May Revolutionize Drug Discoveries—and Our Understanding of Life
A new set of machine-learning algorithms that can generate three-dimensional structures of tiny protein molecules may revolutionize the development of drug therapies for a range of diseases.
Credit: Structura Biotechnology Inc.
A new set of machine learning algorithms developed by University of Toronto (U of T) researchers that can generate three-dimensional (3D) structures of tiny protein molecules may revolutionize the development of drug therapies for a range of diseases, from Alzheimer's to cancer.
"Designing successful drugs is like solving a puzzle," says U of T PhD student Ali Punjani, who helped develop the algorithms. "Without knowing the three-dimensional shape of a protein, it would be like trying to solve that puzzle with a blindfold on."
The ability to determine the 3D atomic structure of protein molecules is critical in understanding how they work and how they will respond to drug therapies, notes Punjani.
Drugs work by binding to a specific protein molecule and changing its 3D shape, altering the way it works once inside the body. The ideal drug is designed in a shape that will only bind to a specific protein or proteins involved in a disease while eliminating side effects that occur when drugs bind to other proteins in the body.
This new set of algorithms reconstructs 3D structures of protein molecules using microscopic images. Since proteins are tiny—even smaller than a wavelength of light - they can't be seen directly without using sophisticated techniques like electron cryomicroscopy (cryo-EM). This new method is revolutionizing the way scientists can discover 3D protein structures, allowing the study of many proteins that simply could not be studied in the past.
From Phys.org
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