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DTSTAMP:20260628T152639Z
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DTSTART:20231121T170000Z
DTEND:20231121T170000Z
DESCRIPTION: \n\n\n	\n		\n			\n				This webinar is by the ELIXIR 3D-BioIn
 fo Community chaired by Prof. Shoshana Wodak \n\n				(Group Leader at V
 IB Structural Biology Research Center) \n			\n			\n		\n	\n\n\nIs this yet
  another multiverse talk?! Exploring murky regions of the protein multiver
 se with ancestral fragments and deep generative models\n\nDr. Eli Draizen\
 n	(Postdoctoral Scholar\, UC San Francisco)\n\nWe are finally in the era o
 f the protein multiverse! The ‘protein universe’ is commonly used to 
 describe the collection of all possible proteins. ‘Possible’ refers to
  all modern\, full-sized proteins found in public databases and could even
  include novel proteins designed with deep generative models that explore 
 the ‘dark’ regions of the learned protein space. \n\nHowever\, modern
  proteins did not arise abruptly\, as singular events\, but rather over th
 e course of at least 3.5 billion years of evolution. The molecular evoluti
 onary processes that yielded their intricate 3D structures involve duplica
 tion\, recombination and mutation of genetic elements\, corresponding to s
 hort peptide fragments. This process can be viewed as corresponding to evo
 lutionarily time-resolved protein universes\, or protein multiverse.\n\nI
 dentifying ancestral fragments is crucial to deciphering the interrelation
 ships amongst proteins\, as well as how evolution acts upon protein sequen
 ces\, structures and functions. Traditionally\, common fragments have been
  found using alignment approaches\, which becomes challenging when protein
 s have undergone extensive permutations—allowing for architecture simila
 rity despite topological variability\, a phenomenon we term the Urfold. We
  have created a framework to identify compact\, potentially-discontinuous 
 peptide fragments by combining deep generative models of protein superfami
 lies with explainable AI to identify relevant atoms.\n\nOur approach recap
 itulates known relationships (established via manual analyses) amongst the
  evolutionarily ancient small β-barrels and amongst P-loop–containing p
 roteins. We are now applying our approach to every CATH superfamily\, incl
 uding CATH-AlphaFold2 predicted domain structures. Because of the generali
 ty of our model’s approach\, we anticipate that it can enable the discov
 ery of new ancestral peptides. Alternative views of the protein universe\,
  aka protein multiverse—such as the Urfold—offer new ways to explore 
 exceedingly remote protein relationships\, beyond traditional hierarchical
  classification systems\, and could allow for finer grained functional ann
 otations.\n\nSWISH-X\, an expanded approach to detect cryptic pockets in p
 roteins and at protein-protein interfaces\n\nAlberto Borsatto\n	(PhD Stude
 nt - Gervasio Lab\, University of Geneva)\n\nProtein-protein interactions 
 mediate most molecular processes in the cell\, offering a significant oppo
 rtunity to expand the set of known druggable targets. Unfortunately\, targ
 eting these interactions can be challenging due to their typically flat an
 d featureless interaction surfaces\, which often change as the complex for
 ms. Such surface changes may reveal hidden (cryptic) druggable pockets.\n\
 nHere\, we analyse a set of well-characterised protein-protein interaction
 s harbouring cryptic pockets and investigate the predictive power of curre
 nt computational methods. Based on our observations\, we develop a new com
 putational strategy\, SWISH-X (SWISH Expanded)\, which combines the establ
 ished cryptic pocket identification capabilities of SWISH with the rapid t
 emperature range exploration of OPES MultiThermal. SWISH-X is able to reli
 ably identify cryptic pockets at protein-protein interfaces while retainin
 g its predictive power for revealing cryptic pockets in isolated proteins\
 , such as TEM-1 β-lactamase\n\nYou can find previous webinars from the 3D
 -BioInfo Community on the Community webinars page.\n\n 
SUMMARY:3DSig: Latest Developments in Structural Bioinformatics
URL;VALUE=URI:https://www.elixir-europe.org/events/3dsig-latest-development
 s-structural-bioinformatics
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