Protein-based Assemblies and Molecular Machines

David Baker from the University of Washington presents breakthrough advancements in de novo protein design. Deep learning pattern recognition hallucinates the desired protein structure and also generates the correct peptide sequence for accurate folding, and predicted proteins are highly transferable to actual proteins produced in a lab. Results are easily transferable to the production scale on a rapid timeline of weeks.

Applications are vast in breadth and depth. Present interests are designing protein assemblies for molecular machines. Components have been successfully assembled and although results are not yet satisfactorily validated, they appear to perform controlled work. Another application is targeting cells with a more accurate computational recognition method. A third application is clinical trials that are underway of a novel vaccine that is highly effective against coronavirus.

Longer-term applications:

• Universal Vaccines
• Larger Alphabet of Novel Amino Acids
• Advanced Drug Delivery
• Smart Therapeutics
• Next-Generation Materials

David Baker is a Nobel laureate, professor of biochemistry, HHMI investigator, and director of the Institute for Protein Design at the University of Washington. His lab develops software for protein design and uses it to create molecules that address challenges in medicine, technology, and sustainability. Recent work includes the development of machine learning methods for generating functional proteins. David is also an adjunct professor of genome sciences, bioengineering, chemical engineering, computer science, and physics at the University of Washington. He has published more than 650 scientific papers, been awarded over 100 patents, and co-founded 21 biotechnology companies.