Deep Mind and Protein Folding

 Was introduced to the nature of this problem early on.  Difficult and fundamental to so many bio science applications.   If Deep Learning has solved this, it is a very big thing.   Following.

Deep Learning Aces Protein Folding   By Don Monroe, Commissioned by CACM Staff, April 6, 2021

Paper folding as a metaphor for the folding of amino acids.

DeepMind's AlphaFold 2 is able to predict the three-dimensional shape into which specific chains of amino acids will fold.

London-based DeepMind is renowned for using deep learning to beat humanity's best Go player. In December, however, the organization revealed its prowess against Mother Nature by predicting the three-dimensional shape into which specific chains of amino acids will fold.

Recognizing the biological and medical significance of the folding question, researchers have a longstanding project to compare different algorithms. This biennial "Critical Assessment of protein Structure Prediction," or CASP, has documented steady progress over the last 26 years. DeepMind's entry, AlphaFold 2, blew the field away.

"They pretty much got it," said Andrei Lupas of the Max Planck Institute for Developmental Biology in Tübingen, Germany, who volunteers as an "assessor" for CASP. "It was the kind of breakthrough that changes the game completely."

Proteins are long molecules that string together scores or hundreds of the 20-odd amino acids. Their precise order is specified by the genetic information in DNA, which is now readily and cheaply determined. To understand protein function, however, researchers need to know how this chain folds into a compact molecular machine, choosing one configuration from among a vast number of possibilities (historically estimated as 10300).

Protein structure was historically derived from x-ray scattering, which requires painstaking preparation of large, purified crystals, or from nuclear magnetic resonance. More recently the field has benefited from electron microscope characterization of smaller flash-frozen samples.

These experiments have revealed important organizational principles, such as attractions between various types of amino acid, as well as recurring structural motifs like helices and sheets. Predicting which overall configuration has the lowest energy from the sequence alone, however, has remained a daunting computational task.  ....  "