Email of the day on deep learning
Comment of the Day

July 26 2021

Commentary by Eoin Treacy

Email of the day on deep learning

This week the British company Deep Minds, owned by Google, announced an important breakthrough on the knowledge of proteins. The company's CEO said that they are working on various projects including nuclear fusion. If they are successful in this venture, it will transform the demand for uranium and lithium.

Eoin Treacy's view

Thank you for this email and this article from the MIT Technology Review may also be of interest. Here is a section:

In the new version of AlphaFold, predictions come with a confidence score that the tool uses to flag how close it thinks each predicted shape is to the real thing. Using this measure, DeepMind found that AlphaFold predicted shapes for 36% of human proteins with an accuracy that is correct down to the level of individual atoms. This is good enough for drug development, says Hassabis.   

Previously, after decades of work, only 17% of the proteins in the human body have had their structures identified in the lab. If AlphaFold’s predictions are as accurate as DeepMind says, the tool has more than doubled this number in just a few weeks.

Even predictions that are not fully accurate at the atomic level are still useful. For more than half of the proteins in the human body, AlphaFold has predicted a shape that should be good enough for researchers to figure out the protein’s function. The rest of AlphaFold’s current predictions are either incorrect, or are for the third of proteins in the human body that don’t have a structure at all until they bind with others. “They’re floppy,” says Hassabis.

“The fact that it can be applied at this level of quality is an impressive thing,” says Mohammed AlQuraish, a systems biologist at Columbia University who has developed his own software for predicting protein structure. He also points out that having structures for most of the proteins in an organism will make it possible to study how these proteins work as a system, not just in isolation. “That’s what I think is most exciting,” he says.

How much of medicine discovery is about trial and error and how much is about tailored solutions? That’s the delineation between the new era of genetics and everything that came before. With knowledge of how proteins interact and how they are made, it is increasingly possible to design targeted solutions to everything in the materials sciences.

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