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Protein Folding AI Is Making a 'Once in a Generation' Advance in Biology - Singularity Hub

Protein Folding AI Is Making a 'Once in a Generation' Advance in Biology - Singularity Hub

Protein Folding AI Is Making a 'Once in a Generation' Advance in Biology - Singularity Hub
Jul 20, 2021 1 min, 56 secs

In two back-to-back papers last week, scientists at DeepMind and the University of Washington described deep learning-based methods to solve protein folding—the last step of executing the programming in our DNA, and a “once in a generation advance.”.

The fast and furious development of Covid-19 vaccines relied on scientists parsing multiple protein targets on the virus, including the spike proteins that vaccines target.

If DNA is the background base code, then proteins are its execution—the actual game that you play.

It’s why deciphering protein structure is so important: like a key to a lock, a drug can only dock onto a protein at specific spots.

Proteins are made of building blocks called amino acids, which are in turn programmed by DNA.

Similar to the Rosetta stone, our cells can easily translate DNA code into protein building blocks inside a clam-shell-like structure, which spits out a string of one-dimensional amino acids.

These ribbons are then shuffled through a whole cellular infrastructure that allows the protein to fold into its final structure.

Christian Anfinsen famously asserted that the one-dimensional sequence itself can computationally predict a protein’s 3D structure.

These methods often “freeze” a protein and map its internal structure down to the atomic level using X-rays.

The first looks at the amino acid building blocks of a protein and compares them to all the other sequences in a protein database.

The tool next examines how one protein’s amino acids interact with another within the same protein, for example, by examining the distance between two distant building blocks.

Finally, the third track looks at the 3D coordinates of each atom that makes up a protein building block—kind of like mapping the studs on a Lego block—to compile the final 3D structure.

For a simple protein, the algorithm was able to solve the structure using a gaming computer in about 10 minutes.

RoseTTAFold was also able to tackle the “protein assemble” problem, in that it could predict the structure of proteins, made up of multiple units, by simply looking at the amino acid sequence alone.

For example, they were able to predict how the structure of an immune molecule locks onto its target.

With the two studies, we’re entering a new world of predicting—and subsequently engineering or changing—the building blocks of life.

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