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AI makes huge progress predicting how proteins fold – one of biology's greatest challenges – promising rapid drug development

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A "deep learning" software program from Google-owned lab DeepMind showed great progress in solving one of biology's greatest challenges – understanding protein folding. Protein folding is the process by which a protein takes its shape from a string of building blocks to its final three-dimensional structure, which determines its function. By better predicting how proteins take their structure, or "fold," scientists can more quickly develop drugs that, for example, block the action of crucial viral proteins. Solving what biologists call "the protein-folding problem" is a big deal. Proteins are the workhorses of cells and are present in all living organisms.


'It will change everything': DeepMind's AI makes gigantic leap in solving protein structures

Nature

A protein's function is determined by its 3D shape.Credit: DeepMind An artificial intelligence (AI) network developed by Google AI offshoot DeepMind has made a gargantuan leap in solving one of biology's grandest challenges -- determining a protein's 3D shape from its amino-acid sequence. DeepMind's program, called AlphaFold, outperformed around 100 other teams in a biennial protein-structure prediction challenge called CASP, short for Critical Assessment of Structure Prediction. The results were announced on 30 November, at the start of the conference -- held virtually this year -- that takes stock of the exercise. "This is a big deal," says John Moult, a computational biologist at the University of Maryland in College Park, who co-founded CASP in 1994 to improve computational methods for accurately predicting protein structures. "In some sense the problem is solved."


'It will change everything': DeepMind's AI makes gigantic leap in solving protein structures

#artificialintelligence

A protein's function is determined by its 3D shape.Credit: DeepMind An artificial intelligence (AI) network developed by Google AI offshoot DeepMind has made a gargantuan leap in solving one of biology's grandest challenges -- determining a protein's 3D shape from its amino-acid sequence. DeepMind's program, called AlphaFold, outperformed around 100 other teams in a biennial protein-structure prediction challenge called CASP, short for Critical Assessment of Structure Prediction. The results were announced on 30 November, at the start of the conference -- held virtually this year -- that takes stock of the exercise. "This is a big deal," says John Moult, a computational biologist at the University of Maryland in College Park, who co-founded CASP in 1994 to improve computational methods for accurately predicting protein structures. "In some sense the problem is solved."


AlphaFold-based databases and fully-fledged, easy-to-use, online AlphaFold interfaces poised to…

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In a nutshell, proteins are linear chains of multiple amino acids, each of which consists in a constant unit of 4 non-hydrogen atoms plus a sidechain of variable size, ranging from none to around 20 atoms. The amino acids are connected through the constant unit, called backbone, to form a polypeptide that does not remain random but rather acquires one or more arrangements in space. That is, they fold into 3D structures. What exact structure a protein will adopt in 3D depends essentially on the identity of the amino acid sidechains, i.e. its amino acid sequence. Very briefly and simplifying definitions that are quite more complex, amino acid sequences are encoded by genes; the collection of genes of an organism is its genome; and the collection of proteins encoded in a genome is the proteome. To be more precise, and this will be important later, the polypeptide actually can fold into multiple substructures, each of which is called a domain.


AI to predict protein structure millions time faster - RNG HEALTH

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There is an escalating race to get to the bottom of predicting the 3D structures of proteins from their amino-acid sequences. It would not be wrong if it is said that it is one of the biggest challenges that the biological world face. Here again, thanks to the new artificial intelligence (AI) who comes to the rescue. At the completion of last year, Google's AI firm DeepMind introduced an algorithm called AlphaFold, which merged two techniques that were evolving in the field and defeated established contestants in a competition on a protein-structure prediction by an unexpected margin. And this year, in April, a US researcher discovered an algorithm that practices an entirely different approach.