DeepMind AI Predicts Protein Structure
By: Gunnar De Winter
Tags: Machine Learning, Artificial Intelligence, Science, Biology, Technology

DeepMind AI Predicts Protein Structure

DeepMind’s Alphafold 2 system predicts protein structure from sequence data with an accuracy that rivals experimental methods.

The hemoglobin (HBE1) protein (Wikimedia commons, Emw)

Challenge accepted

If you are even remotely interested in science, you will have probably already heard about DeepMind’s latest leap. Their AI system Alphafold 2 has cracked predicting proteins’ 3D structure.

There are plenty of great articles about it. (For example, at Nature, New Scientist, and of course on the DeepMind blog itself.)

Since I have written about machine learning/AI in an earlier series of posts , I decided to write a brief post about this development as well. (The previous posts I am referring to: general science and art, but also more specifically in historical research, genetic enhancement, mental health, aging research (including the development of ‘aging clocks’), video game ecology, Hollywood, astrobiology, epidemiology, stock markets, and the job market)

For more details, do check the Nature/New Scientist/DeepMind articles linked above.

To the story.

Starting in 1994, every two years a ‘contest’ has been organized for modeling methods to predict the 3D structure of proteins. The latest edition of this Critical Assessment of Structure Prediction (CASP) concluded on November 30th. In the concluding (virtual) conference the DeepMind team announced the results (and resounding victory) of their Alphafold 2 system. Alphafold 2’s predictions were on par with those of the most recent experimental methods.

Protein structure predictions are assessed via the global distance test (GDT), which basically evaluates how closely the predicted positions of amino acids matches the actual — experimentally determined — ones. A GDT score of 90 is considered the gold standard for experimental methods. Alphafold 2 scored between 87 and 92.4.

A protein interlude

But first, let’s take a step back to see how important this could be.

(Wikimedia commons, Dhorspool)

The central dogma in molecular biology goes as follows:

DNA → RNA → protein.

DNA is transcribed into RNA, which is then translated into a sequence of amino acids.

That string of amino acids folds into a complex 3D structure.

Presto, a protein.

And, my protein-shake guzzling friends, protein does a lot more than build muscle. Proteins can be thought of as the workhorses of molecular biology. Regulating metabolism, DNA replication, signaling, structural support, transporting molecules, responding to stimuli… All in a day’s work.

Another important thing to know is that the function of a protein is strongly tied to its structure. As noted in this 2012 review on protein biology:

While Louis Sullivan’s assertion that ‘form follows function’ is true of most man-made structures; in protein science the reverse is true — function follows form.

So far, determining protein structure is done experimentally, through nuclear magnetic resonance, X-ray crystallography, or cryo-electron microscopy. The issue here is high cost (think millions of dollars for the equipment), long time (years in some cases), and plenty of trial and error.

But wait, you cleverly remark, we know the code. We know how DNA — via an RNA intermediary — codes for the different amino acids. So if we know the DNA chunk that codes for a protein, can’t we simply infer the protein’s structure?

Protein structure examples — with some virus capsids (Wikimedia commons, Alex Griewel)

The key word — and challenge — in the question is ‘simply’.

It turns out that protein folding is mindbogglingly complex.

In fact, around 50 years ago the quest began to predict protein structure from an amino acid sequence.

The problem is that the number of ways through which a string of amino acids can fold into a protein’s final shape is humongous.

In other words, brute force computation is just not enough to run through all possible folding actions.

But now, here comes Alphafold 2.

From DeepMind’s post:

A folded protein can be thought of as a “spatial graph”, where residues are the nodes and edges connect the residues in close proximity… For the latest version of AlphaFold, used at CASP14, we created an attention-based neural network system, trained end-to-end, that attempts to interpret the structure of this graph, … By iterating this process, the system develops strong predictions of the underlying physical structure of the protein and is able to determine highly-accurate structures in a matter of days…We trained this system on publicly available data consisting of ~170,000 protein structures…

A lot of available data + iterated machine learning neural network = breakthrough. (It’s of course much more complicated than that, but this is the gist.)

Next up?

Is this hype, or is it truly a breakthrough? How about both?

The true value — and this is just me talking — is that this is a breakthrough that enables and greatly facilitates other breakthroughs.

If Alphafold 2’s results are confirmed, this gives scientists a powerful way to predict protein structure faster and cheaper than ever before. This is very useful in understanding specific diseases, drug development, pandemic response,…

A little bit further down the line, this also allows the purposeful design of custom proteins. And moving into even more speculative territory: organic nanobots made by your own body.

But we’re not there yet.

What I would like to see next, building on this advance (because make no mistake, this is a big advance in computational molecular biology):

Predicting potential conformation changes — many proteins change shape as they perform their job. Knowing which changes are possible would allow us to examine the protein’s entire toolkit.

Predicting protein interactions — proteins often clump together into so-called complexes to perform their duty. Some of them also catalyze reactions as enzyme. Can we use structure data to predict which ones can work together/speed up reactions?

Application in designer proteins — currently Alphafold 2 works on known proteins. Can we reverse engineer the folding process to design our own ‘folds’ and endow the resulting proteins with a custom function?

The last word is for the DeepMind team: