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DeepMind creates 'transformative' map of human proteins drawn by artificial intelligence

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AI research lab DeepMind has created the most comprehensive map of human proteins to date using artificial intelligence. The company, a subsidiary of Google-parent Alphabet, is releasing the data for free, with some scientists comparing the potential impact of the work to that of the Human Genome Project, an international effort to map every human gene. Proteins are long, complex molecules that perform numerous tasks in the body, from building tissue to fighting disease. Their purpose is dictated by their structure, which folds like origami into complex and irregular shapes. Understanding how a protein folds helps explain its function, which in turn helps scientists with a range of tasks -- from pursuing fundamental research on how the body works, to designing new medicines and treatments.


DeepMind will soon publish the structure of every protein known to science

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DeepMind, a sister company of Google, is giving the world access to a massive protein structure database -- a gift that has the potential to revolutionize scientific research. "This will be one of the most important datasets since the mapping of the Human Genome," Ewan Birney, deputy director general of the European Molecular Biology Laboratory, which partnered with DeepMind on the database, said in a press release. Protein structure: Proteins are molecules that are hugely important to the functioning of living organisms, including humans -- practically everything we're made of and everything our cells do is determined by our proteins. "It's the most significant contribution AI has made to advancing scientific knowledge to date." Every protein is made up of a long string of hundreds or even thousands of chemical compounds called amino acids, and the way that ribbon folds on itself determines the protein's function.


Artificial intelligence tool cracks code to imagine proteins in 3D

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An artificial intelligence network solved a scientific problem that has stumped researchers for half a century, successfully predicting the way proteins fold into three-dimensional shapes, a process that has typically taken expensive and painstaking lab work that could go on for decades. The way proteins, one of the building blocks of life, fold drives their functionality and behaviour. For instance, SARS-Cov-2 has a protein that folds as a spike. This shape, therefore, is relevant for biologists (including for its ability to find cures for illnesses). It isn't easy to predict the shape of a protein, though, based on the way amino acids come together to form a protein.


DeepMind AI cracks 50-year-old problem of protein folding

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Having risen to fame on its superhuman performance at playing games, the artificial intelligence group DeepMind has cracked a serious scientific problem that has stumped researchers for half a century. With its latest AI program, AlphaFold, the company and research laboratory showed it can predict how proteins fold into 3D shapes, a fiendishly complex process that is fundamental to understanding the biological machinery of life. Independent scientists said the breakthrough would help researchers tease apart the mechanisms that drive some diseases and pave the way for designer medicines, more nutritious crops and "green enzymes" that can break down plastic pollution. DeepMind said it had started work with a handful of scientific groups and would focus initially on malaria, sleeping sickness and leishmaniasis, a parasitic disease. "It marks an exciting moment for the field," said Demis Hassabis, DeepMind's founder and chief executive.


[Perspective] Bridging indigenous and scientific knowledge

Science

Indigenous land use practices have a fundamental role to play in controlling deforestation and reducing carbon dioxide emissions. Satellite imagery suggests that indigenous lands contribute substantially to maintaining carbon stocks and enhancing biodiversity relative to adjoining territory (1). Many of these sustainable land use practices are born, developed, and successfully implemented by the community without major influence from external stakeholders (2). A prerequisite for such community-owned solutions is indigenous knowledge, which is local and context-specific, transmitted orally or through imitation and demonstration, adaptive to changing environments, collectivized through a shared social memory, and situated within numerous interlinked facets of people's lives (3). Such local ecological knowledge is increasingly important given the growing global challenges of ecosystem degradation and climate change (4).