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DESCRIPTION:This webinar is organised by the ELIXIR 3D-BioInfo Community\n
 \n				The event will be hosted by\n\n				\n					\n						\n							\n						
 		Dr. Gonzalo Parra\n\n								Barcelona Supercomputing Center (BSC) \n		
 					\n							\n							\n								Dr Neeladri Sen\n\n								University Co
 llege\, London (UCL\n							\n							\n						\n					\n				\n\n				Programm
 e: \n			\n			 \n		\n	\n\n\nDe novo antibody design with RFdiffusion\n\nD
 r. Joe Watson\n	(EMBO Postdoctoral Scholar at the Institute for Protein De
 sign\, University of Washington.)\n\nDe novo protein design seeks to learn
  the underlying principles of protein folding from natural proteins and to
  subsequently apply them to generate novel proteins with programmable func
 tions. In recent years\, there have been significant and concomitant advan
 ces in both our abilities to learn from protein structural data (AlphaFold
 2\, RoseTTAFold) and in generative deep-learning methods in other fields (
 image and text generation). In this talk\, I will discuss our recent work 
 building upon these advances\, in which we trained a generative neural net
 work for de novo protein design\; RoseTTAFold Diffusion (RFdiffusion). I w
 ill focus on the applications of RFdiffusion for designing protein-protein
  interactions\, describing the characterization of hundreds of functional 
 designed binders\, followed by recent advances where we have extended RFdi
 ffusion to design de novo nanobodies and antibodies.\n\n \n\nFrom AlphaFo
 ld to PyMOL: Enabling seamless access to Structural Bioinformatics Tools\n
 \nSerena Rosignoli\n	(Department of Biochemical Sciences “A. Rossi Fanel
 li”\, Sapienza University of Rome\, Rome\, Italy)\n\nAlphaFold\, the gro
 undbreaking AI system developed by Google DeepMind\, has set the stage for
  a paradigm shift in structural biology. Its remarkable accuracy in predic
 ting protein structures has ushered in a revolution in the field. However\
 , the journey from AlphaFold’s initial release (AF2) to its current stat
 e has been marked by significant developments that have reshaped the lands
 cape of structural bioinformatics.\n	One pivotal milestone in AlphaFold’
 s journey was the creation of a comprehensive database of predictions. Thi
 s resource has not only facilitated its widespread adoption but has also s
 purred a wave of research and innovation across various biotechnological d
 omains. As scientists and developers from diverse backgrounds delved into 
 the intricacies of AlphaFold\, a rapid increase in its utilization emerged
 \, leading to unexpected insights and applications.\n	The growth in AlphaF
 old’s utilization has also catalyzed new software development efforts\, 
 with many focused on integrating AlphaFold’s predictions and addressing 
 the algorithm’s remaining challenges. This development has introduced ex
 citing possibilities\, but it has also raised crucial questions. Whilst pr
 esenting our PyMOL-integrated solution to assist structural bioinformatics
 \, we’re equally interested in broader questions.\n\n\n	How can we ensur
 e that AlphaFold seamlessly integrates into established protocols within s
 tructural bioinformatics?\n	How can the synergy between AI-driven predicti
 ons and user-friendly platforms democratize access to cutting-edge structu
 ral biology tools?\n	What role can the combination of homology-based metho
 ds and ab-initio algorithms play in advancing our understanding of complex
  protein structures?\n\n\nYou can find previous webinars from the 3D-BioIn
 fo Community on the Community webinars page.\n\n 
SUMMARY:3D-BioInfo: Protein Engineering And Design 2024
URL;VALUE=URI:https://www.elixir-europe.org/events/3d-bioinfo-protein-engin
 eering-and-design-2024
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