- With the help of several research partners, DeepMind has released a database containing the structures of almost every protein in the human body.
- These structures are computationally determined by AlphaFold, which is a breakthrough protein folding system.
- The database is free for anyone to use and could become a transformative tool in biology and medicine.
DeepMind and several research partners have released a database containing the 3D structures of nearly every protein in the human body, as computationally determined by the breakthrough protein folding system demonstrated last year, AlphaFold. The freely available database represents an enormous advance and convenience for scientists across hundreds of disciplines and domains, and may very well form the foundation of a new phase in biology and medicine.
The AlphaFold Protein Structure Database is a collaboration between DeepMind, the European Bioinformatics Institute and others, and consists of hundreds of thousands of protein sequences with their structures predicted by AlphaFold — and the plan is to add millions more to create a “protein almanac of the world.”
“We believe that this work represents the most significant contribution AI has made to advancing the state of scientific knowledge to date, and is a great example of the kind of benefits AI can bring to society,” said DeepMind founder and CEO Demis Hassabis.
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From genome to proteome
If you’re not familiar with proteomics in general — and it’s quite natural if that’s the case — the best way to think about this is perhaps in terms of another major effort: that of sequencing the human genome. As you may recall from the late ’90s and early ’00s, this was a huge endeavor undertaken by a large group of scientists and organizations across the globe and over many years. The genome, finished at last, has been instrumental to the diagnosis and understanding of countless conditions, and in the development of drugs and treatments for them.
It was, however, just the beginning of the work in that field — like finishing all the edge pieces of a giant puzzle. And one of the next big projects everyone turned their eyes toward in those years was understanding the human proteome — which is to say all the proteins used by the human body and encoded into the genome.
The problem with the proteome is that it’s much, much more complex. Proteins, like DNA, are sequences of known molecules; in DNA these are the handful of familiar bases (adenine, guanine, etc.), but in proteins they are the 20 amino acids (each of which is coded by multiple bases in genes). This in itself creates a great deal more complexity, but it’s only the start. The sequences aren’t simply “code” but actually twist and fold into tiny molecular origami machines that accomplish all kinds of tasks within our body. It’s like going from binary code to a complex language that manifests objects in the real world.
Practically speaking this means that the proteome is made up of not just 20,000 sequences of hundreds of acids each, but that each one of those sequences has a physical structure and function. And one of the hardest parts of understanding them is figuring out what shape is made from a given sequence. This is generally done experimentally using something like x-ray crystallography, a long, complex process that may take months or longer to figure out a single protein — if you happen to have the best labs and techniques at your disposal. The structure can also be predicted computationally, though the process has never been good enough to actually rely on — until AlphaFold came along.
Taking a discipline by surprise
Without going into the whole history of computational proteomics (as much as I’d like to), we essentially went from distributed brute-force tactics 15 years ago — remember Folding@home? — to more honed processes in the last decade. Then AI-based approaches came on the scene, making a splash in 2019 when DeepMind’s AlphaFold leapfrogged every other system in the world — then made another jump in 2020, achieving accuracy levels high enough and reliable enough that it prompted some experts to declare the problem of turning an arbitrary sequence into a 3D structure solved.
I’m only compressing this long history into one paragraph because it was extensively covered at the time, but it’s hard to overstate how sudden and complete this advance was. This was a problem that stumped the best minds in the world for decades, and it went from “we maybe have an approach that kind of works, but extremely slowly and at great cost” to “accurate, reliable, and can be done with off the shelf computers” in the space of a year.
The specifics of DeepMind’s advances and how it achieved them I will leave to specialists in the fields of computational biology and proteomics, who will no doubt be picking apart and iterating on this work over the coming months and years. It’s the practical results that concern us today, as the company employed its time since the publication of AlphaFold 2 (the version shown in 2020) not just tweaking the model, but running it… on every single protein sequence they could get their hands on.
The result is that 98.5% of the human proteome is now “folded,” as they say, meaning there is a predicted structure that the AI model is confident enough (and importantly, we are confident enough in its confidence) represents the real thing. Oh, and they also folded the proteome for 20 other organisms, like yeast and E. coli, amounting to about 350,000 protein structures total. It’s by far — by orders of magnitude — the largest and best collection of this absolutely crucial information.
All that will be made available as a freely browsable database that any researcher can simply plug a sequence or protein name into and immediately be provided the 3D structure. The details of the process and database can be found in a paper published today in the journal Nature.
“The database as you’ll see it tomorrow, it’s a search bar, it’s almost like Google search for protein structures,” said Hassabis in an interview with TechCrunch. “You can view it in the 3D visualizer, zoom around it, interrogate the genetic sequence… and the nice thing about doing it with EMBL-EBI is it’s linked to all their other databases. So you can immediately go and see related genes, And it’s linked to all these other databases, you can see related genes, related in other organisms, other proteins that have related functions, and so on.”
“As a scientist myself, who works on an almost unfathomable protein,” said EMBL-EBI’s Edith Heard (she didn’t specify which protein), “it’s really exciting to know that you can find out what the business end of a protein is now, in such a short time — it would have taken years. So being able to access the structure and say ‘aha, this is the business end,’ you can then focus on trying to work out what that business end does. And I think this is accelerating science by steps of years, a bit like being able to sequence genomes did decades ago.”
So new is the very idea of being able to do this that Hassabis said he fully expects the entire field to change — and change the database along with it.
“Structural biologists are not yet used to the idea that they can just look up anything in a matter of seconds, rather than take years to experimentally determine these things,” he said. “And I think that should lead to whole new types of approaches to questions that can be asked and experiments that can be done. Once we start getting wind of that, we may start building other tools that cater to this sort of serendipity: What if I want to look at 10,000 proteins related in a particular way? There isn’t really a normal way of doing that, because that isn’t really a normal question anyone would ask currently. So I imagine we’ll have to start producing new tools, and there’ll be demand for that once we start seeing how people interact with this.”
How is the World Economic Forum bringing data-driven healthcare to life?
The application of “precision medicine” to save and improve lives relies on good-quality, easily-accessible data on everything from our DNA to lifestyle and environmental factors. The opposite to a one-size-fits-all healthcare system, it has vast, untapped potential to transform the treatment and prediction of rare diseases—and disease in general.
But there is no global governance framework for such data and no common data portal. This is a problem that contributes to the premature deaths of hundreds of millions of rare-disease patients worldwide.
The World Economic Forum’s Breaking Barriers to Health Data Governance initiative is focused on creating, testing and growing a framework to support effective and responsible access – across borders – to sensitive health data for the treatment and diagnosis of rare diseases.
The data will be shared via a “federated data system”: a decentralized approach that allows different institutions to access each other’s data without that data ever leaving the organization it originated from. This is done via an application programming interface and strikes a balance between simply pooling data (posing security concerns) and limiting access completely.
The project is a collaboration between entities in the UK (Genomics England), Australia (Australian Genomics Health Alliance), Canada (Genomics4RD), and the US (Intermountain Healthcare).
That includes derivative and incrementally improved versions of the software itself, which has been released in open source along with a great deal of development history. Already we have seen an independently developed system, RoseTTAFold, from researchers at the University of Washington’s Baker Lab, which extrapolated from AlphaFold’s performance last year to create something similar yet more efficient — though DeepMind seems to have taken the lead again with its latest version. But the point was made that the secret sauce is out there for all to use.