Deep-learning technology allows Toronto-based biotech firm Cyclica to help researchers, companies and governments do drug discovery in record time--including in the COVID-19 crisis. On January 27, 2020, there were still fewer than 1,000 confirmed reported cases of SARS-COV2 in the world, confined mostly to China, though the first positive tests had also appeared in the U.S. state of Washington and would soon follow in Northern Italy. On that same day, the leadership team of Cyclica--a data-driven, drug discovery biotech company based in Toronto--met to discuss how it might get involved. Within a week, discussions had commenced with China's Institute of Materia Medica about sharing Cyclica's AI-enabled platform, Polypharm DB, platform to begin isolating potential therapies through machine learning. On March 5, both sides formalized their partnership, and days later, anticipating the coming quest for inoculations and remedies, Cyclica made the platform available pro bono to any researchers working on treatments for what would soon be widely known as COVID-19.

Scientists last month unveiled the most exhaustive database yet of the proteins that form the building blocks of life, in a breakthrough where observers said would "fundamentally change biological research". Every cell in every living organism is triggered to perform its function by proteins that deliver constant instructions to maintain health and ward off infection. Unlike the genome -- the complete sequence of human genes that encode cellular life -- the human proteome is constantly changing in response to genetic instructions and environmental stimuli. Understanding how proteins operate -- the shape in which they end up, or "fold" into -- within cells has fascinated scientists for decades. But determining each protein's precise function through direct experimentation is painstaking.

DeepMind released a free, open-source, big-deal database last week containing AI predictions for the shapes of every protein in the human body. Not only is it the most complete picture of the human proteome (full set of proteins) to date, according to the London-based AI lab--it's also "doubling humanity's accumulated knowledge of high-accuracy human protein structures." Deepening our understanding of protein structures can lead to major leaps forward in understanding diseases, as well as in drug and vaccine development. That could help allay anything from neglected diseases to the next pandemic. Recap: In December 2020, AlphaFold, DeepMind's neural network, made a breakthrough in protein folding--a biological mystery that's puzzled scientists for 50 years.

Proteins are essential building blocks of living organisms. Every human cell is replete with them. While the understanding of the shapes of proteins is important for making medical advances, only a fraction of these had been deciphered until recently. The ability to use artificial intelligence (AI) to predict the structures of almost every protein made by the human body could help to accelerate the discovery of new drugs to treat disease. A program called AlphaFold can predict the structures of 350,000 proteins belonging to humans and other organisms.

Scientists on Thursday unveiled the most exhaustive database yet of the proteins that form the building blocks of life, in a breakthrough observers said would "fundamentally change biological research". Every cell in every living organism is triggered to perform its function by proteins that deliver constant instructions to maintain health and ward off infection. Unlike the genome -- the complete sequence of human genes that encode cellular life -- the human proteome is constantly changing in response to genetic instructions and environmental stimuli. Understanding how proteins operate -- the shape in which they end up, or "fold" into -- within cells has fascinated scientists for decades. But determining each protein's precise function through direct experimentation is painstaking.

The human mediator complex has long been one of the most challenging multi-protein systems for structural biologists to understand.Credit: Yuan He The human genome holds the instructions for more than 20,000 proteins. But only about one-third of those have had their 3D structures determined experimentally. And in many cases, those structures are only partially known. Now, a transformative artificial intelligence (AI) tool called AlphaFold, which has been developed by Google's sister company DeepMind in London, has predicted the structure of nearly the entire human proteome (the full complement of proteins expressed by an organism). In addition, the tool has predicted almost complete proteomes for various other organisms, ranging from mice and maize (corn) to the malaria parasite (see'Folding options').

Protein structures to represent the data obtained via AlphaFold. DeepMind and EMBL release the most complete database of predicted 3D structures of human proteins. Partners use AlphaFold, the AI system recognized last year as a solution to the protein structure prediction problem, to release more than 350,000 protein structure predictions including the entire human proteome to the scientific community. DeepMind today announced its partnership with the European Molecular Biology Laboratory (EMBL), Europe's flagship laboratory for the life sciences, to make the most complete and accurate database yet of predicted protein structure models for the human proteome. This will cover all 20,000 proteins expressed by the human genome, and the data will be freely and openly available to the scientific community.

T-cell immunomodulatory protein homolog, from the AlphaFold Protein Structure Database, reproduced under a CC-BY-4.0 license. DeepMind and the European Molecular Biology Laboratory (EMBL) have partnered to produce a database of predicted protein structure models. The first release covers all 20,000 proteins expressed in the human proteome, and the proteomes of 20 other biologically significant organisms, totalling over 350k structures. In the coming months they plan to expand the database to cover a large proportion of all catalogued proteins (the over 100 million in UniRef90). The data is freely and openly available to the scientific community. You can access the AlphaFold Protein Structure Database here.

It took decades of painstaking research to map the structure of just 17 per cent of the proteins used within the human body, but less than a year for UK-based AI company DeepMind to raise that figure to 98.5 per cent. The company is making all this data freely available, which could lead to rapid advances in the development of new drugs. Determining the complex, crumpled shape of proteins based on the sequence of amino acids that make them has been a huge scientific hurdle. Some amino acids are attracted to others, some are repelled by water, and the chains form intricate shapes that are hard to calculate accurately. Understanding these structures enables new, highly targeted drugs to be designed that bind to specific parts of proteins. Genetic research had long provided the ability to determine the sequence of a protein, but an efficient way of finding the shape – crucial to understanding its properties – has proven elusive.