{"id":39414,"date":"2022-08-02T12:58:35","date_gmt":"2022-08-02T11:58:35","guid":{"rendered":"https:\/\/ub-blog.meduniwien.ac.at\/blog\/?p=39414"},"modified":"2022-08-02T12:58:35","modified_gmt":"2022-08-02T11:58:35","slug":"alphafold-protein-structure-database-developed-by-deepmind-and-embl-ebi","status":"publish","type":"post","link":"https:\/\/ub.meduniwien.ac.at\/blog\/?p=39414","title":{"rendered":"AlphaFold Protein Structure Database (Developed by DeepMind and EMBL-EBI)"},"content":{"rendered":"

AlphaFold Protein Structure Database<\/span><\/h1>\n

URL:\u00a0https:\/\/alphafold.ebi.ac.uk\/<\/a><\/p>\n

Die Vorhersage der 3-dimensionalen Proteinstruktur durch einen Algorithmus einer Artificial Intelligence<\/em> wurde Ende 2021 vom Journal SCIENCE zum Breakthrough of the Year<\/em> gew\u00e4hlt.<\/p>\n

DeepMind<\/strong><\/a>, eine Tochtergesellschaft von GOOGLE, gab 2020 bekannt, mittels k\u00fcnstlicher Intelligenz die r\u00e4umliche Struktur aufgrund einer bekannten Aminos\u00e4uresequenz von Proteinen hervorsagen zu k\u00f6nnen. 2021 publizierten sie mit Hilfe des entsprechenden Programmes, AlphaFold<\/strong><\/a>, 350.000 Proteine. Und nun, im Jahr 2021, stellt die Firma in der gemeinsam mit dem EMBL<\/a> <\/strong>(European Molecular Biology Laboratory) \u00fcber 200 Millionen Proteine in der Datenbank AlphaFold Protein Structure Database<\/strong><\/a> der wissenschaftlichen Forschergemeinde zur Verf\u00fcgung.<\/p>\n

Dieser wissenschaftliche Durchbruch kann in seiner Bedeutung gar nicht hoch genug eingesch\u00e4tzt werden.<\/p>\n

\n

LITERATUR<\/strong><\/span><\/p>\n

\n
\n
Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, et al. Highly accurate protein structure prediction with AlphaFold.<\/strong> Nature. 2021 Aug;596(7873):583\u20139.<\/div>\n<\/div>\n
<\/div>\n<\/div>\n
\n
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Varadi M, Anyango S, Deshpande M, Nair S, Natassia C, Yordanova G, et al. AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models<\/strong>. Nucleic Acids Res. 2022 Jan 7;50(D1):D439\u201344.<\/div>\n
<\/div>\n<\/div>\n<\/div>\n
\n
Thorp HH. Proteins, proteins everywhere<\/strong>. Science. 2021 Dec 17;374(6574):1415\u20131415.<\/div>\n<\/div>\n
\n
\n<\/div>\n
SPEKTRUM.DE – Jeder kann jetzt Proteine falten<\/a><\/div>\n
<\/div>\n
HEISE ONLINE – AlphaFold: Deepmind-KI ermittelt 3D-Struktur fast aller bekannter Proteine<\/a>.<\/div>\n
<\/div>\n
NYT – A.I. Predicts the Shape of nearly Every Protein Known to Science.<\/a><\/div>\n
\n
VIDEOS<\/span><\/h6>\n

DEEPMIND – DeepMind’s AlphaFold 2 Explained! AI Breakthrough in Protein folding! What we know (& what we don’t<\/a>)<\/p>\n

EMBL – How to interpret AlphaFold structures<\/a><\/p>\n

\n","protected":false},"excerpt":{"rendered":"

AlphaFold Protein Structure Database URL:\u00a0https:\/\/alphafold.ebi.ac.uk\/ Die Vorhersage der 3-dimensionalen Proteinstruktur durch einen Algorithmus einer Artificial Intelligence wurde Ende 2021 vom Journal SCIENCE zum Breakthrough of the Year gew\u00e4hlt. DeepMind, eine Tochtergesellschaft von GOOGLE, gab 2020 bekannt, mittels k\u00fcnstlicher Intelligenz die r\u00e4umliche Struktur aufgrund einer bekannten Aminos\u00e4uresequenz von Proteinen hervorsagen zu k\u00f6nnen. 2021 publizierten sie mit … AlphaFold Protein Structure Database (Developed by DeepMind and EMBL-EBI)<\/span> weiterlesen →<\/span><\/a><\/p>\n","protected":false},"author":10,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"ngg_post_thumbnail":0,"footnotes":""},"categories":[10,33,2,16],"tags":[],"class_list":["post-39414","post","type-post","status-publish","format-standard","hentry","category-datenbanken","category-gastbeitrage","category-news","category-news1"],"views":3343,"_links":{"self":[{"href":"https:\/\/ub.meduniwien.ac.at\/blog\/index.php?rest_route=\/wp\/v2\/posts\/39414","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/ub.meduniwien.ac.at\/blog\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/ub.meduniwien.ac.at\/blog\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/ub.meduniwien.ac.at\/blog\/index.php?rest_route=\/wp\/v2\/users\/10"}],"replies":[{"embeddable":true,"href":"https:\/\/ub.meduniwien.ac.at\/blog\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=39414"}],"version-history":[{"count":9,"href":"https:\/\/ub.meduniwien.ac.at\/blog\/index.php?rest_route=\/wp\/v2\/posts\/39414\/revisions"}],"predecessor-version":[{"id":39425,"href":"https:\/\/ub.meduniwien.ac.at\/blog\/index.php?rest_route=\/wp\/v2\/posts\/39414\/revisions\/39425"}],"wp:attachment":[{"href":"https:\/\/ub.meduniwien.ac.at\/blog\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=39414"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/ub.meduniwien.ac.at\/blog\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=39414"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/ub.meduniwien.ac.at\/blog\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=39414"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}