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SUMMARY:Learning the molecular grammar of protein condensate formation - K
 adi Liis Saar
DTSTART:20210531T160000Z
DTEND:20210531T163000Z
UID:TALK160822@talks.cam.ac.uk
CONTACT:Bingqing Cheng
DESCRIPTION:Machine learning approaches have had a strong impact on many a
 reas of natural sciences\, including the study of proteins. My talk focuse
 s on the use of machine learning approaches in the context of studying a s
 pecific property of proteins\, their tendency to form biomolecular condens
 ates. Biomolecular condensates are central to a very wide variety of cellu
 lar processes ranging from gene expression to protein translation. Recent 
 years have given new insight into the factors that affect protein phase be
 haviour but many of the specifics of the factors that gover process remain
  not understood. In my presentation\, I will show how we have been able to
  use both biophysics-based insight as well as hypothesis-free language mod
 els for describing the tendency of proteins to form condensates. The resul
 ts illustrate that pre-trained language models have the capability to capt
 ure the specifics of important cellular processes at a high accuracy and c
 omparably to physics-based models.
LOCATION:virtual ZOOM meeting ID: 263 591 6003\, Passcode: 000042\, https:
 //us02web.zoom.us/j/2635916003?pwd=ZlBEQnRENGwxNmJGMENGMWxjak5nUT09
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