Hi,

> Any time you do a thought experiment you make a fake-data data set, the
> "true" phases and "true" amplitudes become the ones you put into the
> simulation process.  This is by definition.  Is there potential for
> circular reasoning?  Of course!  But you can do controls:
>

this is so much true! This is what I've been doing for development and
testing all the time since started working for Phenix! Fully controlled
thought/numerical experiments done this way are super-helpful (but
obviously have limitations!).


>    If you start with an ordinary single-conformer coordinate model and
> flat bulk solvent from refmac to make your Ftrue, then what you will
> find is that even after adding all plausible experimental errors to the
> data the final Rwork/Rfree invariably drop to small-molecule levels of
> 3-4%.  This is true even if you prune the structure back, shake it, and
> rebuild it in various ways.  The difference features always guide you
> back to Rwork/Rfree = 3/4%. However, if you refine with phenix.refine,
> you will find Rwork/Rfree stall at around 10-11%.  This is because Ftrue
> came from refmac and refmac and phenix.refine have somewhat different
> bulk solvent models.  If Ftrue comes from phenix and you refine with
> refmac you get similar "high" R values.  High for a small molecule
> anyway. And, of course, if you get Ftrue from phenix and refine with
> phenix you also get final Rwork/Rfree = 3/4%. If you do more things that
> automated building doesn't do, like multi-headed side chains, or get the
> bulk solvent from an MD simulation, then you can get "realistic"
> Rwork/Rfree in the 20%s.  All of this is the main conclusion from this
> paper: https://dx.doi.org/10.1111/febs.12922


Even within Phenix alone this is true if you switch between different
scaling/bulk-solvent models or play with automation levels (such as
ignoring reflection otliers, etc).

Pavel

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