Hi Randy (and others who replied privately)

First off, thanks for taking the time to reply - much appreciated!

This all correlates with what I would have thought. 

One of the off-board replies mentioned that they remove unstructured/unreliable 
conformations - I’d guess that these have low confidence or can be pruned out 
with tools like  process_predicted_model and SliceNDice, or even using DSSP to 
only include elements with identifiable secondary structure.

It occurs to me that the problem with misinterpretation of the confidence in 
the B-factor column could be avoided if there was a field in the mmCIF format 
to replace 
“_atom_site.B_iso_or_equiv” with something like 
“_atom_site.predicted_confidence"; after all, the mmCIF format is ~30 years old 
so isn’t everyone using it by default (no, don’t bother answering that; 
learning chemistry in the 1970s I learnt that we should be talking about 
“ethanal”, “propan-2-ol” etc (“when you work for a chemical company like ICI 
that’s what you’ll have to call them”), but 50 years later we still buy 
acetaldehyde and IPA…)?

best wishes

Harry

> On 24 Jan 2026, at 17:35, Randy John Read <[email protected]> wrote:
> 
> Hi Harry,
> 
> Yes, this is easy to answer! There are various papers, some from my group, 
> about the utility of AlphaFold models in MR. For the specific point of how to 
> treat the models, you can look at the paper on process_predicted_model 
> (https://doi.org/10.1107/s2059798322010026 
> <https://doi.org/10.1107/s2059798322010026>), which is available in both 
> Phenix and CCP4. In CCP4, there’s also the SliceNDice tool. 
> 
> The side chains are considerably better than random, so they’re left in for 
> any residues included in the model. But yes, it’s good to prune out low 
> confidence residues, with 70 being the most common cutoff.
> 
> The trickiest bit is deciding where there might be inter-domain flexibility. 
> Domain parsing can be done in a variety of ways in process_predicted_model 
> and SliceNDice, but the human eye is also very good at the required pattern 
> recognition!
> 
> There’s something I like to reiterate whenever possible, because we keep 
> seeing people making this mistake. Whatever you do, don’t use the model 
> straight out of AlphaFold! These have the pLDDT in the B-factor column, on a 
> scale of 0-100 where 100 is nearly perfect. If you leave them as B-factors, 
> you will be down weighting the very best parts of the model in the MR 
> calculation! If you process the model, the pLDDT values will be turned into 
> B-factors that are low for high pLDDT and vice versa.
> 
> Best wishes,
> 
> Randy
> 
> From: CCP4 bulletin board <[email protected]> on behalf of Harry Powell 
> <[email protected]>
> Date: Saturday, 24 January 2026 at 15:56
> To: [email protected] <[email protected]>
> Subject: [ccp4bb] pruning & confidence of AlphaFold models for MR
> 
> Hi folks
> 
> I’m sure that people have thought about this, done this, and maybe even 
> published on the topic, so this should be an easy one to answer :-)
> 
> I was wondering how people treat AlphaFold models when using them in 
> molecular replacement? Do they prune them down to poly Ala, do they include 
> all modelled residues or remove those with low confidence (if so, how low - 
> pLDDT 70?)?
> 
> Harry
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