{"id":308,"date":"2026-02-03T16:45:07","date_gmt":"2026-02-03T15:45:07","guid":{"rendered":"https:\/\/project.inria.fr\/ncsb\/?p=308"},"modified":"2026-03-18T12:25:18","modified_gmt":"2026-03-18T11:25:18","slug":"the-structure-function-conundrum-for-proteins-a-perspective","status":"publish","type":"post","link":"https:\/\/project.inria.fr\/ncsb\/the-structure-function-conundrum-for-proteins-a-perspective\/","title":{"rendered":"The structure-function conundrum for proteins: a perspective"},"content":{"rendered":"<p><strong>Frederic Cazals<\/strong><\/p>\n\n\n\n<p><a href=\"mailto:Frederic.Cazals@inria.fr\" target=\"_blank\" rel=\"noreferrer noopener\">Frederic.Cazals@inria.fr<\/a><br>Centre Inria d&#8217;Universit\u00e9 C\u00f4te d&#8217;Azur, Algorithms-Biology-Structure<\/p>\n\n\n\n<p><strong>Abstract<\/strong><\/p>\n\n\n\n<p>The function of proteins relies on a subtle mix between structural and dynamical properties (thermodynamics, kinetics). Deep learning based methods, pioneered by AlphaFold, for which the 2024 Nobel prize in chemistry was co-awarded, have revolutionized protein structure prediction and enabled the prediction of whole proteomes.<\/p>\n\n\n\n<p>This talk will put current work in perspective in two respects.&nbsp; First, I will present recent work on the critical assessment of AlphaFold predictions, stressing the gap remaining between structure and dynamics.&nbsp; Second, I will describe structure generation methods combining advanced kinematics and&nbsp; sampling techniques, as well as novel models for joint distributions of torsion angles, to make a stride towards more efficient thermodynamics and kinetics.<\/p>\n\n\n\n<p>A quick software demo using plugins from the Structural Bioinformatics Library will conclude the talk.<\/p>","protected":false},"excerpt":{"rendered":"<p>Frederic Cazals Frederic.Cazals@inria.frCentre Inria d&#8217;Universit\u00e9 C\u00f4te d&#8217;Azur, Algorithms-Biology-Structure Abstract The function of proteins relies on a subtle mix between structural and dynamical properties (thermodynamics, kinetics). Deep learning based methods, pioneered by AlphaFold, for which the 2024 Nobel prize in chemistry was co-awarded, have revolutionized protein structure prediction and enabled the\u2026<\/p>\n<p> <a class=\"continue-reading-link\" href=\"https:\/\/project.inria.fr\/ncsb\/the-structure-function-conundrum-for-proteins-a-perspective\/\"><span>Continue reading<\/span><i class=\"crycon-right-dir\"><\/i><\/a> <\/p>\n","protected":false},"author":2411,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[7],"tags":[],"class_list":["post-308","post","type-post","status-publish","format-standard","hentry","category-abstracts"],"_links":{"self":[{"href":"https:\/\/project.inria.fr\/ncsb\/wp-json\/wp\/v2\/posts\/308","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/project.inria.fr\/ncsb\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/project.inria.fr\/ncsb\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/project.inria.fr\/ncsb\/wp-json\/wp\/v2\/users\/2411"}],"replies":[{"embeddable":true,"href":"https:\/\/project.inria.fr\/ncsb\/wp-json\/wp\/v2\/comments?post=308"}],"version-history":[{"count":2,"href":"https:\/\/project.inria.fr\/ncsb\/wp-json\/wp\/v2\/posts\/308\/revisions"}],"predecessor-version":[{"id":360,"href":"https:\/\/project.inria.fr\/ncsb\/wp-json\/wp\/v2\/posts\/308\/revisions\/360"}],"wp:attachment":[{"href":"https:\/\/project.inria.fr\/ncsb\/wp-json\/wp\/v2\/media?parent=308"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/project.inria.fr\/ncsb\/wp-json\/wp\/v2\/categories?post=308"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/project.inria.fr\/ncsb\/wp-json\/wp\/v2\/tags?post=308"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}