GNSS & Machine Learning Engineer

Tag: Baker Lab

RFdiffusion from Baker Lab solves the Protein Generation Problem

While ProteinMPNN takes a protein backbone (N-Ca-C-O atoms) and finds an amino acid sequence that would fold to that backbone structure, RFdiffusion [Twitter] instead makes the protein backbone by just providing some geometrical and functional constraints like “create a molecule that binds X”.

The authors used a guided diffusion model for generating new proteins in the same way as Dall-E produces high-quality images that have never existed before by a diffusion technique.

See also this presentation by David Baker.

If I interpret this announcement correctly it means that drug design is now basically solved (or starts to get interesting depending on the viewpoint).

This technique can be expected to increase the number of potential drugs to fight diseases heavily. However, animal tests and human studies can also be expected as the bottlenecks of the new possibilities. Techniques like organ chips from companies like emulate may be a way out of this dilemma (before one-day entire cell, tissue, or whole body computational simulations become possible).

ProteinMPNN from Baker Lab can reverse AlphaFold

The software tool ProteinMPNN (Message Passing Neural Network) from Baker Lab can predict from a given 3D protein structure possible amino acid sequences that would fold into the given structure, in this way effectively reversing what AlphaFold from DeepMind or ESMFold from Meta can do. So the approach allows to design proteins. With a DNA/RNA printer as the BioXp from TelesisBio or the Syntax system from DNAScript it is possible to directly output the desired protein or a virus that generates the protein in a cell when injected into the body.

The source code is available on GitHub and has also already been integrated into a Hugging Face space. See also here.

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