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Cool stuff, but more likely useful as a primary filter or search tool rather than for detailed understanding of protein structure and function. See this quote:

Cool stuff, but more likely useful as a primary filter or search tool rather than for detailed understanding of protein structure and function. See this quote: > "Sergey Ovchinnikov, an evolutionary biologist at Harvard University in Cambridge, Massachusetts, wonders about the hundreds of millions of predictions that ESMFold made with low-confidence. Some might lack a defined structure, at least in isolation, whereas others might be non-coding DNA mistaken as a protein-coding material." Understanding of how these proteins function requires high-resolution information about bond angles and atom-atom distances, particularly for non-structural proteins (i.e. interesting catalytic enzymes). Hence, wet-lab work and protein structure characterization via X-ray and NMR methods aren't going anywhere.

COGlory 4y ago
Medium confidence is often close enough that you can formulate a hypothesis, but high confidence still isn't close enough that you'd stake millions of dollars of experiments on it anyways. Most of the people in structural biology in pharma that I have chatted with said they're still solving the structures of their targets even with high-confidence AlphaFold models. For me, as someone solving structures on a monthly basis, AlphaFold is great because after I get back my electron density map, I dump my sequence into AlphaFold, get a model (of any confidence) and most of it fits well enough into my density map that I don't have to start trying to model from nothing, and it saves me honestly days of work.
- siver_john 4y ago
  That's a cool way I had never thought about it being used. Just for my own curiosity do you know if that is a common use case in the protein solving field? (I'd imagine it would also be useful in NMR experiments for getting initial point labels).
  - flobosg 4y ago
    It is quite common to use predicted models as an aid for phasing and molecular replacement; even Foldit models have been adopted for that purpose: https://www.nature.com/articles/nsmb.2119 https://www.nature.com/articles/nsmb.2119
- dekhn 4y ago
  What's really funny about using AlphaFold predictions to bootstrap a model into a density map is eventually, your structure, based on an AF prediction, will eventually be folded into the dataset used to train the next version of AlphaFold. Talk about test set/training set leakage!

COGlory 4y ago

Medium confidence is often close enough that you can formulate a hypothesis, but high confidence still isn't close enough that you'd stake millions of dollars of experiments on it anyways. Most of the people in structural biology in pharma that I have chatted with said they're still solving the structures of their targets even with high-confidence AlphaFold models. For me, as someone solving structures on a monthly basis, AlphaFold is great because after I get back my electron density map, I dump my sequence into AlphaFold, get a model (of any confidence) and most of it fits well enough into my density map that I don't have to start trying to model from nothing, and it saves me honestly days of work.

siver_john 4y ago
That's a cool way I had never thought about it being used. Just for my own curiosity do you know if that is a common use case in the protein solving field? (I'd imagine it would also be useful in NMR experiments for getting initial point labels).
- flobosg 4y ago
  It is quite common to use predicted models as an aid for phasing and molecular replacement; even Foldit models have been adopted for that purpose: https://www.nature.com/articles/nsmb.2119 https://www.nature.com/articles/nsmb.2119
dekhn 4y ago
What's really funny about using AlphaFold predictions to bootstrap a model into a density map is eventually, your structure, based on an AF prediction, will eventually be folded into the dataset used to train the next version of AlphaFold. Talk about test set/training set leakage!