Biological complexity: AI predicts protein folds (Introduction)

by David Turell , Tuesday, December 01, 2020, 22:51 (1241 days ago) @ David Turell

Life exists based on precisely folded proteins in which is their shape produces a precise functional mode:

https://www.newscientist.com/article/2261156-deepminds-ai-biologist-can-decipher-secret...

"An AI system developed by UK-based company DeepMind has achieved the long-sought-after goal of accurately predicting the shape of proteins from their sequence alone, a key part of understanding how the machinery of life works. In a competition, AlphaFold was able to match two-thirds of the results achieved by humans doing expensive and time-consuming lab experiments.

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"Proteins are vital for life. Cells are full of machines – from turbines that generate energy to transporters that walk along tracks pulling cargo – that are built from proteins, and the shapes of these machines are crucial. For instance, the coronavirus can enter and infect cells because the spike protein on its surface fits into a receptor on human cells, like a key into a lock.

"These shapes depend on the sequence of 20 different amino acids that are chained together to make proteins. It is easy to work out the sequence of any protein because this is determined by the DNA that codes for it. But despite half a century of efforts, biologists hadn’t previously been able to work out the shape of a protein from its sequence alone.

"Instead, they have had to rely on experimental methods such as X-ray crystallography, which involves analysing the diffraction pattern formed when an X-ray beam is fired through a protein crystal.

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"For each target protein, groups including DeepMind’s look for variants found in related species and feed their sequence and structure into the AI system, along with the sequence of the target protein. The idea is that the system learns to work out the shape of the target protein by looking at patterns linking sequence and structure.

"Predicted shapes are scored out of 100 based on how close each amino acid is to the position determined by experiment. A score above 90 is considered to be on a par with results obtained by experiments.

"In the 2016 competition, the best team got a median score of around 40 in the hardest category. In 2018, the first version of AlphaFold got a median score of nearly 60 in this category. This year, a redesigned AlphaFold got a median score of 87 in the hardest category. Across all categories, it scored above 90 for two-thirds of the proteins.

"DeepMind found an AI learning technique also works in human brains
While this result is amazing, there were some clear failures, says Moult. For instance, AlphaFold didn’t do well with a protein whose structure is influenced by interactions with other proteins that surround it."

Comment: The shape and folding is based on amino acid sequences, attritive and repulsive electrical forces and also the influence of neighboring molecules. The latter creates a problem as indicated.