Gerard, Sameer - that is highly impressive indeed
About that Editorial (nice read!), it frames these developments as a
"crisis" that heralds "the end of structural biology" - or at the very
least, you're responding to people having said that. I remain puzzled
that such stuff receive any editorial oxygen - to me it's like saying
that that higher flux and automation at synchrotrons threaten
crystallography. Or something. AlphaFold is just another tool - a
sensationally powerful one, of course, but it doesn't Do Science.
Unless there have been noises from Funders - in which case we do need a
calls to arms, to scream some sense into them.
Btw, for whoever missed it, AlphaFold models are as accurate as 3.5A
structures:
https://twitter.com/LindorffLarsen/status/1527410977213403147. So
they're amazing, but Real Crystallographers know the experimental
distance between 3.5 and the 2.2 needed for structure-supported ligand
design, to name just one.
Frank
On 28/07/2022 16:19, Gerard Kleywegt wrote:
Hi all,
I thought Sameer was burying the lead a tad in his message... :-) So,
for those of you who -like me- are not on social media:
==> As of today, the AlphaFold Protein Structure Database contains 214
million models predicted with AlphaFold, covering almost all of
UniProt. <==
So, if your favourite protein was not available in the database before
today, it's worth checking in again at
https://www.alphafold.ebi.ac.uk/ now.
See also:
- EMBL-EBI press release:
https://www.ebi.ac.uk/about/news/technology-and-innovation/alphafold-200-million/
- Nature news: https://www.nature.com/articles/d41586-022-02083-2
- IUCr J guest editorial about the potential impact of all this on
structural biologists (shameless plug):
https://journals.iucr.org/m/issues/2022/04/00/me6185/index.html
Best wishes,
--Gerard
On Thu, 28 Jul 2022, Sameer Velankar wrote:
Dear All,
You may have seen our announcement today about expanding the
AlphaFold Protein Structure Database to 214M predicted models. To
enable this expansion, we’ve updated the Predicted Aligned Error
(PAE) JSON format to make it compact (about 4x smaller):
The PAE JSON numbers are now rounded to the closest integer, giving
~75% compressed size reduction. The integer resolution is sufficient
for analytical purposes.
The indices are not stored anymore since we store the full 2D PAE
matrix rather than a sparse one, giving ~4% compressed size reduction.
The “distances” field has been renamed to “predicted_aligned_error”
and is now stored as a 2D array of shape (num_res, num_res) rather
than a 1D array. We renamed the field on purpose so that existing
code breaks rather than potentially silently returning wrong values.
For a protein of length num_res, the PAE JSON file has now the
following format:
[{
"predicted_aligned_error": [[0, 1, 4, 7, 9, ...], ...], # Shape:
(num_res, num_res).
"max_predicted_aligned_error": 31.75 # Scalar.
}]
The fields in the JSON file are:
predicted_aligned_error: The PAE value of the residue pair, rounded
to the closest integer. For PAE value on position (i, j), i is the
residue on which the structure is aligned for the predicted error, j
is the residue on which the error is predicted.
max_predicted_aligned_error: A number that denotes the largest
possible unrounded value of PAE that could occur in the PAE array.
The smallest possible value of PAE is 0.
The updated PAE format is only available from the AlphaFold Protein
Structure Database. The PAE format from the AlphaFold Colab notebook
is not updated.
If you require support with this change, please email
alphaf...@deepmind.com and they may
be able to assist.
Best Wishes,
Sameer Velankar
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--Gerard
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