# Model building with ModelAngelo

As of release 5.0, RELION comes with a machine-learning approach for automated atomic model building called ModelAngelo. ModelAngelo is a graph neural network that combines information from the cryo-EM map with sequence information of the proteins that are present in the map to build an atomic model.


## Download FASTA sequence


Because we know that the sample in this data set is beta-galactosidase from E.coli, we can provide the protein sequence as a FASTA file. Select `Fetch sequence` job from `Fetch` category and fill in following parameters:

```params
UNIPROT accession number: P00722
```



Press `RUN`. We don’t need to inspect the `RESULTS`  as it will only contain a `.fasta` text file containing the sequence string.


## Model building

Now select the `ModelAngelo` job from `Atomic Model Build` category and fill in following parameters:

```params
B-factor sharpened map:: PostProcess/jobXXX/postprocess_masked.mrc

FASTA sequence for proteins:: Fetch/jobXXX/P00722.fasta

Which GPU to use:: <leave blank>
```

Press `RUN`. The job should take about 10 minutes to run. Once it’s finished we can inspect the `RESULTS` tab.  Adjust the `Iso Value` and inspect the model. The model should fit nicely in the density.

```{image} ../_static/images/SPA/9_modelangelo_result.png
:align: center
:scale: 70%
```

Please note that this model isn’t finished. This should be considered as the first step towards model building - obtaining an initial model. It is crucial to complete a number of `Model Refinement` and `Model Validation` steps. While these are out of scope for this tutorial, a tutorial for Model Building can be found [HERE](https://ccpem-tutorials.readthedocs.io/en/latest/Doppio-Model-Building/index.html).