11 ms·

The method relies on multiple-sequence-alignment (MSA) of homologous proteins. This cannot fold arbitrary proteins, only biologically relevant ones that have hi

The method relies on multiple-sequence-alignment (MSA) of homologous proteins. This cannot fold arbitrary proteins, only biologically relevant ones that have high quality MSAs available. It's also worth pointing out that the gold-standard for validating MSAs relies on PDBs of folded proteins. This is exciting work that will assist NMR and XRay crystallographers, but it's not a panacea of protein folding. https://github.com/deepmind/deepmind-research/issues/18 https://github.com/deepmind/deepmind-research/issues/18

flobosg 6y ago
In their CASP abstract[1] they mention alternatives to typical co-evolution features which improve performance in shallow MSA depths. [1]: https://predictioncenter.org/casp14/doc/CASP14_Abstracts.pdf#page=23 https://predictioncenter.org/casp14/doc/CASP14_Abstracts.pdf...
- gmorainbows 6y ago
  It doesn't matter so much how they perform the feature extraction, so much as what their inputs to the feature extraction are. This model requires a collection of wild-type proteins in an accurate MSA. Producing an accurate MSA is hard even if you have many homologs. They require protein homologs which means they can "only" do this for wild-type proteins. This work is useless with mutant and synthetic proteins. This is a big advancement that will assist crystallographers and NMR structural biologists with difficult wild-type proteins, but it doesn't "solve protein folding" by any stretch of the imagination.
  - flobosg 6y ago
    > Producing an accurate MSA is hard even if you have many homologs. To assess co-evolutionary couplings the amount of homologs in the MSA is not as important as the number of effective sequences (i.e. sequence depth and diversity) in it. > They require protein homologs which means they can "only" do this for wild-type proteins. Even remote homologs work, as shown by the widespread use of HHM-based methods in the prediction pipelines. > This work is useless with mutant and synthetic proteins. Unless you generate a flurry of data with them using deep mutational scanning for example. As long as correlated mutations are present in the MSA the technique should work as expected no matter where the protein sequences originated.
    - gmorainbows 6y ago
      I'm honestly not familiar with "deep mutational scanning." Can you share a link? I'm first author on papers related to the structural biology of coevolution and I competed in CASP about a decade ago, but I haven't kept up much since then.
      - flobosg 6y ago
        Sure! Here's a paper about the method: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4410700/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4410700/ And another one about its application in structure prediction: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7295002/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7295002/

flobosg 6y ago

In their CASP abstract[1] they mention alternatives to typical co-evolution features which improve performance in shallow MSA depths. [1]: https://predictioncenter.org/casp14/doc/CASP14_Abstracts.pdf#page=23 https://predictioncenter.org/casp14/doc/CASP14_Abstracts.pdf...

gmorainbows 6y ago
It doesn't matter so much how they perform the feature extraction, so much as what their inputs to the feature extraction are. This model requires a collection of wild-type proteins in an accurate MSA. Producing an accurate MSA is hard even if you have many homologs. They require protein homologs which means they can "only" do this for wild-type proteins. This work is useless with mutant and synthetic proteins. This is a big advancement that will assist crystallographers and NMR structural biologists with difficult wild-type proteins, but it doesn't "solve protein folding" by any stretch of the imagination.
- flobosg 6y ago
  > Producing an accurate MSA is hard even if you have many homologs. To assess co-evolutionary couplings the amount of homologs in the MSA is not as important as the number of effective sequences (i.e. sequence depth and diversity) in it. > They require protein homologs which means they can "only" do this for wild-type proteins. Even remote homologs work, as shown by the widespread use of HHM-based methods in the prediction pipelines. > This work is useless with mutant and synthetic proteins. Unless you generate a flurry of data with them using deep mutational scanning for example. As long as correlated mutations are present in the MSA the technique should work as expected no matter where the protein sequences originated.
  - gmorainbows 6y ago
    I'm honestly not familiar with "deep mutational scanning." Can you share a link? I'm first author on papers related to the structural biology of coevolution and I competed in CASP about a decade ago, but I haven't kept up much since then.
    - flobosg 6y ago
      Sure! Here's a paper about the method: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4410700/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4410700/ And another one about its application in structure prediction: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7295002/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7295002/