Learning the molecular grammar of protein condensate formation

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• Kadi Liis Saar
• Monday 31 May 2021, 17:00-17:30
• virtual ZOOM meeting ID: 263 591 6003, Passcode: 000042, https://us02web.zoom.us/j/2635916003?pwd=ZlBEQnRENGwxNmJGMENGMWxjak5nUT09.

If you have a question about this talk, please contact Bingqing Cheng .

Machine learning approaches have had a strong impact on many areas of natural sciences, including the study of proteins. My talk focuses on the use of machine learning approaches in the context of studying a specific property of proteins, their tendency to form biomolecular condensates. Biomolecular condensates are central to a very wide variety of cellular processes ranging from gene expression to protein translation. Recent years have given new insight into the factors that affect protein phase behaviour 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 models for describing the tendency of proteins to form condensates. The results illustrate that pre-trained language models have the capability to capture the specifics of important cellular processes at a high accuracy and comparably to physics-based models.

This talk is part of the Machine learning in Physics, Chemistry and Materials discussion group (MLDG) series.

This talk is included in these lists:

• Hanchen DaDaDash
• Lennard-Jones Centre external
• Machine learning in Physics, Chemistry and Materials discussion group (MLDG)
• virtual ZOOM meeting ID: 263 591 6003, Passcode: 000042, https://us02web.zoom.us/j/2635916003?pwd=ZlBEQnRENGwxNmJGMENGMWxjak5nUT09

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