AlphaFold-disorder

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AlphaFold-disorder

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Predict disorder and disorder binding from AlphaFold structures

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# AlphaFold-disorder
#### Disorder and binding region detection from AlphaFold predicted structures

The script parses and processes PDB files generated by AlphaFold. It expects the pLDDT score in the B-factor column. As intermediate (mandatory) step it calculates the Relative Solvent Accessibility (RSA) as provided by DSSP and BioPython.

#### Dependencies
- Python3
- NumPy
- Pandas
- BioPython
- DSSP 3.x ("mkdssp" executable)

#### Usage

The script takes in input a folder with PDB files and writes two TSV files.

python3 alphafold_disorder.py -i pdbs/ -o out.tsv

##### Additional parameters

- ***rsa_window*** (default 25) - RSA values are smoothed over a window centered on the residue to predict
- ***rsa_threshold*** (default 0.581) - Binding predictions are overweighted when disorder prediction is above this threshold

Both parameters take a space separated list of values (floats). The program generates an output for each possible combination of the provided lists.

##### Output format

###### TSV
By default, the program uses 
the TSV format and generates two files ***out_data.tsv*** and ***out_pred.tsv***, representing intermediate calculation 
(DSSP output) and the final prediction, respectively. 
The last two columns (**disorder-*****<rsa_window>***, **binding-*****<rsa_window>-<rsa_threshold>***) 
are the relevant ones representing the disorder and binding propensities.
```
name    pos     aa      lddt    disorder        rsa     disorder-25     binding-25-0.581
P47710  1       M       0.688   0.312   1.000   0.680   0.869
P47710  2       R       0.832   0.168   0.879   0.691   0.929
P47710  3       L       0.850   0.150   0.854   0.696   0.937
P47710  4       L       0.863   0.137   0.756   0.705   0.943
...
Q5RJL0  67      V       0.502   0.498   0.951   0.896   0.791
Q5RJL0  68      L       0.511   0.489   1.000   0.881   0.795
Q5RJL0  69      P       0.449   0.551   0.787   0.866   0.769
Q5RJL0  70      R       0.514   0.486   1.000   0.864   0.796
...
```

###### CAID
The CAID format can be generated with the command below.

python3 alphafold_disorder.py -i pdbs/ -o out.tsv -f caid

The program will generate different files for different types of prediction and different combination of parameters: 
- out_disorder.dat, disorder based on pLDDT
- out_disorder-<rsa_window>.dat, disorder based on RSA and smoothed over a window
- out_binding-<rsa_window>-<rsa_threshold>.dat, binding prediction wighted based on a threshold on the smoothed RSA

```
>P47710
1       M       0.68
2       R       0.691
3       L       0.696
4       L       0.705
...
67      V       0.896
68      L       0.881
69      P       0.866
70      R       0.864
...
```

#### How to cite

Piovesan D, Monzon AM, Tosatto SCE.<br />
Intrinsic protein disorder and conditional folding in AlphaFoldDB. 
Protein Sci. 2022 Nov;31(11):e4466.<br />
PMID: [36210722](https://pubmed.ncbi.nlm.nih.gov/36210722/)
PMCID: [PMC9601767](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9601767/).

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