{"id":69,"date":"2022-08-01T19:20:44","date_gmt":"2022-08-01T19:20:44","guid":{"rendered":"https:\/\/www.dgipl.com\/blog\/?p=69"},"modified":"2022-09-01T13:02:33","modified_gmt":"2022-09-01T13:02:33","slug":"new-era-in-digital-biology","status":"publish","type":"post","link":"https:\/\/www.dgipl.com\/blog\/new-era-in-digital-biology\/","title":{"rendered":"\u2018New era in digital biology\u2019: AI reveals structures of nearly all known proteins"},"content":{"rendered":"\n<h4 class=\"wp-block-heading\">AI reveals structures of nearly all known proteins.<\/h4>\n\n\n\n<p>Twelve months ago, the artificial intelligence (AI) company DeepMind stunned many scientists with\u00a0the release of predicted structures for some 350,000 proteins, part of the work recognized as\u00a0Science\u2019s 2021 Breakthrough of the Year. On July 28, 2022, DeepMind and its partners went much, much further. The company\u00a0<a href=\"https:\/\/www.deepmind.com\/blog\/alphafold-reveals-the-structure-of-the-protein-universe\">unveiled the likely structures of nearly all known proteins<\/a>, more than 200 million from bacteria to humans, a striking achievement for AI and a potential treasure trove for drug development and evolutionary studies.<\/p>\n\n\n\n<p>\u201cWe\u2019re releasing now the structures for the whole protein universe,\u201d said Demis Hassabis, founder and CEO of DeepMind, at a press conference in London.<\/p>\n\n\n\n<p>The structural bounty comes from AlphaFold, one of the new AI programs that have cracked the protein-folding problem, the long-standing challenge of accurately deriving the 3D shapes of proteins from their amino acid sequences. Yesterday has released AlphaFold\u2019s new prediction structure into an existing database through a partnership with the European Molecular Biology Laboratory\u2019s European Bioinformatics Institute (EMBL-EBI). database \u201chas provided structural biologists with this powerful new tool where you can look up the 3D structure of a protein almost as easily as you can do a keyword Google search,\u201d Hassabis said.<\/p>\n\n\n\n<p>Eric Topol, director of the Scripps Research Translational Institute, echoed the amazement of many outside scientists. \u201cAlphaFold is the singular and momentous advance in life science that demonstrates the power of AI,\u201d <em>Moreover<\/em> he tweeted. \u201cWe can expect more biological mysteries to solve each day with this new addition of structures illuminating nearly the entire protein universe.<\/p>\n\n\n\n<p>The DeepMind structure release is \u201cremarkable,\u201d said Ewan Birney, deputy director general of EMBL, at the press conference. \u201cIt will make many researchers worldwide think about what experiments they can now do.\u201d<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">From where AlphaFold come?<\/h4>\n\n\n\n<p>AlphaFold comes from organisms ranging from bacteria to plants to vertebrates, including mice, zebrafish, and humans. Kathryn Tunyasuvunakool, a DeepMind research scientist, said it took AlphaFold roughly 10 to 20 seconds to make each protein prediction. The company had to work closely with EMBL-EBI, she noted, to figure out how to present the immense number of structures in the database.<\/p>\n\n\n\n<p><em>Moreover<\/em>, DeepMind says more than 500,000 researchers have already used the database since its launch last year. Hassabis predicted a \u201cnew era in digital biology\u201d in which drug developers could go from AI-predicted structures of proteins important to any medical condition to using AI to design small molecules that influence those proteins\u2014and therefore treat an illness.<\/p>\n\n\n\n<p>Others are using the structure predictions to develop vaccine candidates, probe basic biology questions such as how the so-called&nbsp;nuclear pore complex&nbsp;gatekeeps which molecules enter a cell\u2019s nucleus, or examine the&nbsp;evolution of proteins&nbsp;when life first evolved.<\/p>\n\n\n\n<p>Hassabis, however, cautioned the release of the structures is merely a starting point. \u201cThere\u2019s still obviously a lot of biology, and a lot of chemistry, that has to be done.\u201d<\/p>\n\n\n\n<p><\/p>\n\n\n\n<p>More information: <a href=\"https:\/\/www.deepmind.com\/blog\/alphafold-reveals-the-structure-of-the-protein-universe\">https:\/\/www.deepmind.com\/blog\/alphafold-reveals-the-structure-of-the-protein-universe<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/www.science.org\/content\/article\/new-era-digital-biology-ai-reveals-structures-nearly-all-known-proteins\">https:\/\/www.science.org\/content\/article\/new-era-digital-biology-ai-reveals-structures-nearly-all-known-proteins<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/www.nature.com\/articles\/d41586-022-02083-2\">https:\/\/www.nature.com\/articles\/d41586-022-02083-2<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>AI reveals structures of nearly all known proteins. Twelve months ago, the artificial intelligence (AI) company DeepMind stunned many scientists with\u00a0the release of predicted structures for some 350,000 proteins, part of the work recognized as\u00a0Science\u2019s 2021 Breakthrough of the Year. On July 28, 2022, DeepMind and its partners went much, much further. The company\u00a0unveiled the [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":70,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"nf_dc_page":"","_FSMCFIC_featured_image_caption":"","_FSMCFIC_featured_image_nocaption":"","_FSMCFIC_featured_image_hide":"","_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[1],"tags":[],"class_list":["post-69","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>New era in digital biology - Interstellar Communication<\/title>\n<meta name=\"description\" content=\"A new era in digital biology Twelve months ago, the artificial intelligence (AI) company DeepMind stunned many scientists with\u00a0the\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.dgipl.com\/blog\/new-era-in-digital-biology\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"New era in digital biology - 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