I can’t wait to see how it works when you apply it to orphan proteins that don’t have any evolutionary relatives in the training dataset. At least some of this efficacy probably comes from effectively encoding an ability to see very deep evolutionary homology hidden in sequences, and variations around ancient motifs.
I also wonder if the system can be reversed, such that you give it a 3-dimensional backbone arrangement and it dreams up a sequence to fold into it.
I’m especially curious about the second one. Is there any known situation where we have a 3-dimensional arrangement but not the sequence? I can think of two possibilities: either we have an existing protein for which we know the structure but not the sequence (which from my modest understanding looks improbable, because sequence seems easier) or we have an application (medical for example) that needs a specific kind of structure, and we want to know how to create such a protein.
I would mostly be thinking of engineering novel proteins. That field is pretty rudimentary (though the work of Michael Hecht at Princeton fascinates me).
This could also lead to an interesting race dynamic between biotech/pharma companies in the near future. If a novel disease target protein is identified and DeepMind decides to license their technology, all companies would in theory have access to the same structural information at the same time. Then it becomes a matter of who can execute screening, medicinal chemistry optimization, and clinical evaluation the fastest. Having a strategy in place to be the first to obtain patents for chemical matter modulating that target protein would also be a large advantage in this kind of situation.
I can’t wait to see how it works when you apply it to orphan proteins that don’t have any evolutionary relatives in the training dataset. At least some of this efficacy probably comes from effectively encoding an ability to see very deep evolutionary homology hidden in sequences, and variations around ancient motifs.
I also wonder if the system can be reversed, such that you give it a 3-dimensional backbone arrangement and it dreams up a sequence to fold into it.
EDIT: See this writeup https://www.blopig.com/blog/2020/12/casp14-what-google-deepminds-alphafold-2-really-achieved-and-what-it-means-for-protein-folding-biology-and-bioinformatics/
Cool ideas!
I’m especially curious about the second one. Is there any known situation where we have a 3-dimensional arrangement but not the sequence? I can think of two possibilities: either we have an existing protein for which we know the structure but not the sequence (which from my modest understanding looks improbable, because sequence seems easier) or we have an application (medical for example) that needs a specific kind of structure, and we want to know how to create such a protein.
Are these what you had in mind?
I would mostly be thinking of engineering novel proteins. That field is pretty rudimentary (though the work of Michael Hecht at Princeton fascinates me).
This could also lead to an interesting race dynamic between biotech/pharma companies in the near future. If a novel disease target protein is identified and DeepMind decides to license their technology, all companies would in theory have access to the same structural information at the same time. Then it becomes a matter of who can execute screening, medicinal chemistry optimization, and clinical evaluation the fastest. Having a strategy in place to be the first to obtain patents for chemical matter modulating that target protein would also be a large advantage in this kind of situation.
See this writeup
https://www.blopig.com/blog/2020/12/casp14-what-google-deepminds-alphafold-2-really-achieved-and-what-it-means-for-protein-folding-biology-and-bioinformatics/