Indeed there are many pitfalls between a protein sequence and something useful to humanity, but there is reason to believe the technique is capable of generating such proteins:
1) In the paper they express several of their designs and show stability via circular dichroism experiments. They also show size exclusion chromatography results indicating some of the proteins are of the expected size and are not aggregating.
2) Since RFDiffusion and ProteinMPNN, which generates the actually amino acid sequence, are trained using Protein Data Bank (PDB) data, it's reasonable to presume the predicted proteins will be well behaved. To solve a protein structure via say X-ray crystallography, EM, or NMR and deposit it into the PDB requires bucket loads of stable protein. I used several grams of recombinant protein for a X-ray structure I solved. Since the ML models are trained on well behaved proteins, I can believe the generated proteins will also be well behaved.
1) In the paper they express several of their designs and show stability via circular dichroism experiments. They also show size exclusion chromatography results indicating some of the proteins are of the expected size and are not aggregating.
2) Since RFDiffusion and ProteinMPNN, which generates the actually amino acid sequence, are trained using Protein Data Bank (PDB) data, it's reasonable to presume the predicted proteins will be well behaved. To solve a protein structure via say X-ray crystallography, EM, or NMR and deposit it into the PDB requires bucket loads of stable protein. I used several grams of recombinant protein for a X-ray structure I solved. Since the ML models are trained on well behaved proteins, I can believe the generated proteins will also be well behaved.