11–20 of 62 posts
> After decades of effort, 17% of the total residues in human protein sequences are covered by an experimentally-determined structure. Here we dramatically expand structural coverage by applying the state-of-the-art machine learning method, AlphaFold2, at scale to almost the entire human proteome (98.5% of human proteins).
Maybe I was wrong for expecting the impossible here, but I was excited to see this specific structure and it appears that there is still work to do. Nevertheless, kudos to Deepmind on their amazing achievement and contributions to the field!
I'm impressed and grateful that DeepMind released this resource, this will save a lot of compute from labs trying to replicate an entire exome for themselves. While some structures look great, there are still some misses here. Important structures like BRCA1 (a well-studied breast cancer associated protein) are just structures for the BRCT and RING domains surrounded by a low-confidence string of amino acids, likely…
Another famous one would be R-domain of CFTR, which was not resolved in experimental structure determination, and AlphaFold models correctly show disorder there. Nothing to be done in those cases except perform molecular simulation or other experiments to assess dynamic ensembles, https://alphafold.ebi.ac.uk/entry/P13569
Citation factory, that's what it is.
I'm impressed and grateful that DeepMind released this resource, this will save a lot of compute from labs trying to replicate an entire exome for themselves. While some structures look great, there are still some misses here. Important structures like BRCA1 (a well-studied breast cancer associated protein) are just structures for the BRCT and RING domains surrounded by a low-confidence string of amino acids, likely…
I'm impressed and grateful that DeepMind released this resource, this will save a lot of compute from labs trying to replicate an entire exome for themselves. While some structures look great, there are still some misses here. Important structures like BRCA1 (a well-studied breast cancer associated protein) are just structures for the BRCT and RING domains surrounded by a low-confidence string of amino acids, likely…
A curious non-biologist here: how valuable are these low confidence predictions for biologists? In other words, is it hard to predict but easy to check situation as with, say, prime numbers in mathematics?
I'm impressed and grateful that DeepMind released this resource, this will save a lot of compute from labs trying to replicate an entire exome for themselves. While some structures look great, there are still some misses here. Important structures like BRCA1 (a well-studied breast cancer associated protein) are just structures for the BRCT and RING domains surrounded by a low-confidence string of amino acids, likely…
A curious non-biologist here: how valuable are these low confidence predictions for biologists? In other words, is it hard to predict but easy to check situation as with, say, prime numbers in mathematics?
yikes, this doesn't even do some basic stuff like trim off pre-protein segments for secreted proteins... Without this, you could get some very incorrect structures.