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SUMMARY:Prediction of Protein Structure and Interaction by Physics and Inf
 ormatics
DTSTART;VALUE=DATE-TIME:20191107T043000Z
DTEND;VALUE=DATE-TIME:20191107T045000Z
DTSTAMP;VALUE=DATE-TIME:20260724T113241Z
UID:indico-contribution-28@sshep.snu.ac.kr
DESCRIPTION:Speakers: Chaok Seok (Seoul National University)\nProtein stru
 cture prediction problem has challenged theoretical and computational phys
 ical scientists since the first protein structure was published in 1958. T
 here have been steady progresses in protein structure prediction since the
 n\, but major contributions to the progress came from informatics-based ap
 proaches rather than from physics-based approaches. Recently\, DeepMind’
 s AlphaFold made a further contribution by introducing deep learning to ex
 tract structural information from the large sequence database. Meaningful 
 contribution of physics-based approaches began to be made only in 2012 in 
 the field of structure refinement. However\, structural improvements that 
 can be achieved by refinement with current physics-based approaches are ve
 ry limited due to both energy and sampling problems. To overcome this limi
 tation\, we are taking an approach that combines physics and informatics\,
  including deep learning. We take similar approaches to predict interactio
 ns of proteins with other proteins or small ligands including short peptid
 es and oligosaccharides. Our goal is to develop protein structure modeling
  techniques that can provide useful predictions even in the absence of ava
 ilable information\, although currently available experimental data would 
 play important roles in developing such techniques. Such modeling methods 
 would be very useful for applications to a wide range of biomedical resear
 ch and drug discovery.\n\nhttps://sshep.snu.ac.kr/event/107/contributions/
 28/
LOCATION:Seoul National University\, College of Natural Sciences\, Buildin
 g 25-1 국제회의실 (International Conference Hall)
URL:https://sshep.snu.ac.kr/event/107/contributions/28/
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