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Continued thread

Once I am happy with the current state of the atomic model, the next step depends on the global resolution of the map:
1. For low resolution (~5 to 2.5 Å), I run the model through #phenix.real_space_refine using the parameter file generated by #ISOLDE. This turns off most things, but refines the coordinates with reference-model restraints (so they won't move far from where I left them with my manual fitting) and the atomic b-factors.
13/19

Continued thread

Le réacteur# PBFR, réacteur rapide au #sodium (même technologie que #Phenix et #Superphenix construits par le passé en France), d’une puissance de 500MWe vient d’obtenir l’autorisation du chargement du combustible.

Avec 71% de #charbon dans son mix électrique, l’ajout de nouvelles capacités nucléaires permettra d’éviter directement l’utilisation du charbon et les émissions de #co2

Last century, I was young and already in love with my Mamiya C330 camera (loaded with an Ilford HP5+ film).

I will probably need to change my « artist name », since there is already a street artist in Switzerland named QueenKong.

I am sad about that and need to think to an alternative.

Maybe QueenKhat.

Still a Queen 👑

@structbio

If you ever #dock models into noisy #cryoem maps, please look at the papers we (Claudia Millán, Airlie McCoy,
@terwilltom) have just released through bioRxiv!

# 1, theory:

biorxiv.org/content/10.1101/20

# 2, implementation in a program called EM_placement.

biorxiv.org/content/10.1101/20

Find it in today's (dev-4821) build of #Phenix (phenix-online.org/download/nig), and coming soon elsewhere including a #ChimeraX plugin!

bioRxiv · Likelihood-based signal and noise analysis for docking of models into cryo-EM mapsFast, reliable docking of models into cryo-EM maps requires understanding of the errors in the maps and the models. Likelihood-based approaches to errors have proven to be powerful and adaptable in experimental structural biology, finding applications in both crystallography and cryo-EM. Indeed, previous crystallographic work on the errors in structural models is directly applicable to likelihood targets in cryo-EM. Likelihood targets in Fourier space are derived here to characterise, based on the comparison of half-maps, the direction- and resolution-dependent variation in the strength of both signal and noise in the data. Because the signal depends on local features, the signal and noise are analysed in local regions of the cryo-EM reconstruction. The likelihood analysis extends to prediction of the signal that will be achieved in any docking calculation for a model of specified quality and completeness. A related calculation generalises a previous measure of the information gained by making the cryo-EM reconstruction. Synopsis Likelihood-based rotation, translation and refinement targets have been derived for docking models into cryo-EM reconstructions. ### Competing Interest Statement The authors have declared no competing interest.

#AlphaFold #AI with #Phenix #PredictAndBuild makes #Crystallography easier: All you need is your #Xray data and a sequence file.

#AlphaFold2 generates a #hypothesis, #PredictAndBuild compares it to the data, improves it and feeds it back to #AlphaFold2 to create a better #hypothesis.

(Fully automatic. Requires current nightly build of #Phenix from phenix-online.org)

biorxiv.org/content/10.1101/20