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DTSTAMP:20230831T095745Z
LOCATION:Sanada I
DTSTART;TZID=Europe/Stockholm:20230626T163000
DTEND;TZID=Europe/Stockholm:20230626T170000
UID:submissions.pasc-conference.org_PASC23_sess120_msa276@linklings.com
SUMMARY:Protein Language Models for Mapping Structure-Function Relationshi
 ps
DESCRIPTION:Minisymposium\n\nBurkhard Rost (Technical University of Munich
 )\n\nThe Rostlab aims to use evolutionary information (EI) combined with m
 achine learning (ML) and artificial intelligence (AI) to predict aspects o
 f protein function and structure from sequence. The use of EI and ML allow
 ed a breakthrough in secondary structure prediction 30 years ago, and it h
 as been the root for all state-of-the-art predictions of protein structure
  and function, including AlphaFold2. With the recent breakthrough developm
 ent of protein Language Models (pLMs), it has become possible to extract i
 nformation from protein sequences and transfer it to supervised learning m
 ethods for protein predictions, reaching or surpassing state-of-the-art me
 thods without use of evolutionary information. I will present our groups d
 evelopments of pLMs and new methods to predict protein structure and funct
 ion, up to assessing the effects of sequence variation upon structure and 
 towards disease. The understanding of AI and control of database bias are 
 crucial in computational biology and can serve as a sandbox to prepare mor
 e sensitive applications of AI in society.\n\nDomain: Life Sciences\n\nSes
 sion Chair: Arvind Ramanathan (Argonne National Laboratory, University of 
 Chicago)
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