I guess that also means that AlphaFold has learnt the
crystal-structure-ness that older homology methods never achieved -
which is why (anecdotally?) a "better" homology model tended to give
worse MR performance than the "worse" template?
(Or something like that, I'm parrotting what I remember people (maybe
Randy?) saying long ago about the problems with homology models in MR.)
On 04/12/2020 11:57, Adam Simpkin wrote:
I thought I might be able to add a little to this conversation as I performed
some MR runs as part of the CASP14 High Accuracy analysis. There were 30
targets with reflection data. Of these, AlphaFold2 models could be used to
directly solve 24 structures after converting
RMS error predictions to simulated B-factors to aid the MR (10.1002/prot.25800).
Some of the models did contain sufficient local errors to impede MR. However,
we were able to obtain a further 3 solutions by using AMPLE to truncate the
models based on the per-residue RMS error predictions provided. In fact, a
moderate truncation in AMPLE improved the quality of the MR solution in ~78%
that succeeded by removing the few incorrectly models loops (typically at
lattice interfaces).
A final thing to note was that the 3 structures that didn’t work still provided high
quality model predictions (GDT_TS of 69, 84 & 83). These targets all contained
multiple chains in the ASU and one was fairly low resolution (>3 Angstroms).
Overall though I think the take home is clear, these models are really good and when
the method or something similar is more publicly available I think it will definitely
simplify MR for troublesome targets.
Best wishes,
Adam
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