Protein design, the inverse of the protein folding problem

[John Clark]

AlphaFold solved the protein folding problem some time ago and now, judging from two articles in today's issue of the journal Science, it looks like the inverse problem has also been solved, the protein design problem. If you tell a program called "ProteinMPNN" that you want a 3-D protein that has an activation site that will perform a very specific function, and has the proper scaffolding to keep that activation site stable, and is also shaped in just the right way so that it can fit into a very tight corner where it is needed like a key into a lock, then ProteinMPNN will tell you what linear sequence of amino acids will fold up into that 3-D shape. I think this is a very big deal, the implications for medicine are obvious but it also signifies a huge advance in Nanotechnology because the authors claim the 3-D shape the sequence of amino acids folds up into is within 0.06 Nanometers of the requested shape, and Nanotechnology is about placing atoms exactly where you want them to go, and enzymes are proteins and they act like little machines.  

Robust deep learning–based protein sequence design using ProteinMPNN

Hallucinating symmetric protein assemblies

John K Clark

[Brent Meeker]

So then we know which nucleic acid sequence will cause cells to produce the desired protein, and we can use CRISPR to modify DNA to produce the RNA that will produce the desired protein...if we knew what protein was desired.

Can superpowers be far behind?

Brent

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[Lawrence Crowell]

It is curious what the relationship is. The Alpha-Fold is an algorithm that computes the shape give a peptide sequence. This is a bit wider, in that it finds a form corresponding to a function.

LC

[Telmo Menezes]

The fires model (ProteinMPNN) appears to be more or less the reverse of AlphaFold, as John says. Given the 3D structure of a protein, it attempts to find a sequence of amino acids that would fold into that shape. Algorithms already existed in this domain, but they managed to improve accuracy using deep learning (from 32.9% to 52.4%). I have no idea how "good enough" the latter number is, and if there is more room for improvement with deep learning alone.

Then the second paper is about a generative model, that provides a protein given some high-level features (number of protomers and protomer length). My understanding is that this allows one to design a "key" that fits some specific "lock". Generative models are capable of learning the mapping from one feature space to another (for example, from images of apples to textual descriptions of apples  e.g. "big red apple") and then operate in reverse. Here they must have trained the model with known 3D structures and the associated features they are interested in, and can now "hallucinate" new molecules from sets of features that the model has never seen before, but that allow for some novel application.

Telmo