First, this assumes that I have a "final" map (no more image processing needed) and that its local resolution is better than ~5 Å in most places. Except otherwise noted, this is the "raw" (unfiltered and unsharpened) map.
2/19

Having identified the proteins, I fetch their #AlphaFold2 predictions from AlphaFold-DB, or compute them if not in the DB. #AF2 models have excellent geometry and complete sequence correctly numbered, so they are excellent starting models. I rarely use #PDB entries as starting models anymore. Rare exceptions: a PDB entry I deposited myself, or one containing a post-translational modification or non-natural amino acid I need (never present in #AF2 models, only the 20 standards amino acids).
4/19

Using #ChimeraX, I dock these #AF2 models into the map. I have seen a case with two different protein subunits of very similar sequences, which makes assigning each AF2 model to the density pretty difficult. It is however very easy to align (with the `matchmaker` command in ChimeraX) the AF2 models to the model produced by #model_angelo. This will unambiguously place similar AF2 models to where model_angelo detected their sequences.
5/19

If the map is symmetric, I generate the asymmetric unit (ASU) and store the symmetry info that regenerates the whole complex in my working CIF file containing what will eventually become the final atomic model.
6/19

I finally trim the termini and loops not supported by any density. At this stage, I have a starting atomic model: it mostly fits the density, and there should remain only local problems to fix.
7/19

At this point, I use #ISOLDE to do manual fitting. If the map has a low resolution, I apply secondary structure restraints to the model. I start with a global simulation to let the model settle, which I let run until the dots in the interactive Ramachandran plot have reached an equilibrium. With #AF2 models, there shouldn't be Ramachandran outliers in the first place, but this is a good overview of how many residues are moving. I want them to settle in the nearest density before I continue.
8/19

Then comes the fun part in #ISOLDE: tug the atoms and they respond in a physically realistic way, how cool is that?! 🤩 This step can also be very tedious if the model is large. 😵 Using the `isolde step next` command, I walk along each chain from its first to last residue (in a symmetric map, only for the ASU), fixing problems. This includes placing water molecules, ions and ligands, where there is density supporting their presence. This is super fun to do, and teaches a lot about chemistry.
9/19

During this stage I often get help from two additional sources of information:
1. The model from #model_angelo sometimes helps moving the #AF2 model to the correct location, in these cases where a segment of the AF2 model doesn't match the map. Very often these discrepancies between map and prediction point to regions of the protein involved in conformational change, so I make a note of the chain ID and residue range so I know to take a close look later and compare to related structures.
10/19

2. A post-processed map from #deepEMhancer used only as a visual guide (so not allowed to pull on the atoms in the iMDFF procedure in #ISOLDE) is often helpful in regions of lower resolution. I have seen cases where I couldn't find a stable conformation for a long side chain, until I placed it where the deepEMhancer map suggested: suddenly it was nicely stable in the combined pull of the raw map and MD force field.
11/19