NGBS2022 Talk 11: Protein design using deep learning – David Baker

Protein design using deep learning
Towards a mechanistic understanding of genome regulation
Speaker: David Baker, University of Washington, USA
Chair: Stephen McLaughlin, MRC Laboratory of Molecular Biology, UK
Co-Chair: David Rueda, Imperial College, London and MRC-LMS UK

David Baker describes de novo protein design via two methods using physically-based Rosetta and using deep learning neural networks. Using the first method, he shows how proteins can be designed for potential therapeutic use and to understand the photophysics of chlorophyll or create a transmembrane beta-barrel protein that can function as ion channel. This approach has also generated a set designed a set of protein building blocks that can be used for formations of larger structures not found in nature. In the second half of this talk, David describes how using a deep-learning approach can be used to find a protein sequence that will fold into a designed structure. This has been used to generate large symmetric rings, novel selective catalysts and stable epitopes for vaccine development.

Read more about David Bakers’s work here:
https://www.bakerlab.org/

You can find more about the Next Generation Biophysics Symposium 2022 here:
https://www3.mrc-lmb.cam.ac.uk/sites/...

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