“The dream of predicting a protein shape just from its gene sequence is now a reality,” said Paul Adams, Associate Laboratory Director for Biosciences at Berkeley Lab. For Adams and other structural biologists who study proteins, predicting their shape offers a key to understanding their function and accelerating treatments for diseases like cancer and COVID-19.

The current approaches to accurately mapping that shape, however, usually rely on complex experiments at synchrotrons. But even these sophisticated processes have their limitations—the data and quality aren’t always sufficient to understand a protein at an atomic level. By applying powerful machine learning methods to the large library of protein structures it is now possible to predict a protein’s shape from its gene sequence.

Researchers in Berkeley Lab’s Molecular Biophysics & Integrated Bioimaging Division joined an international effort led by the University of Washington to produce a computer software tool called RoseTTAFold. The algorithm simultaneously takes into account patterns, distances, and coordinates of amino acids. As these data inputs flow in, the tool assesses relationships within and between structures, eventually helping to build a very detailed picture of a protein’s shape.

Summary: Machine learning algorithm produced fewer decision-making errors than professionals when it came to clinical diagnosis of patients.

Source: University of Montreal.

It’s an old adage: there’s no harm in getting a second opinion. But what if that second opinion could be generated by a computer, using artificial intelligence? Would it come up with better treatment recommendations than your professional proposes?

A new type of artificial intelligence (AI) algorithm, developed by the Mayo Clinic and the Google Research Brain Team, can potentially pave the way toward more directed brain stimulation for the treatment of Parkinson’s disease and other movement-related disorders.

According to researchers, this algorithm can more accurately determine the interaction between different regions of the brain — data that will be key for improving the way brain stimulation devices are used in the real world for treating Parkinson’s.

“Our findings show that this new type of algorithm may help us understand which brain regions directly interact with one another, which in turn may help guide placement of electrodes for stimulating devices to treat network brain diseases,” Kai Miller, MD, PhD, a neurosurgeon at Mayo Clinic and the first author of the study, said in a press release.