rafm 1.1.4

Description:

rafm 1.1.4

rafm computes per-model measures such as expected global LDDT
associated with atomic-level accuracy for AlphaFold models from
pLDDT confidence scores.

Installation
You can install rafm via pip from PyPI:
$ pip install rafm


Usage
rafm –help lists all commands. Current commands are:


plddt-stats
Calculate stats on bounded pLDDTs from list of AlphaFold model files.
in either PDB or mmCIF format.
Options:



–criterion FLOAT
The cutoff value on truncated pLDDT for possible utility.
[default: 91.2]




–min-length INTEGER
The minimum sequence length for which to calculate truncated stats.
[default: 20]




–min-count INTEGER
The minimum number of truncated pLDDT values for which to
calculate stats [default: 20]




–lower-bound INTEGER
The pLDDT value below which stats will not be calculated.
[default: 80]




–upper-bound INTEGER
The pLDDT value above which stats will not be calculated.
[default: 100]




–file-stem TEXT
Output file name stem. [default: rafm]





Output columns (where NN is the bounds specifier, default: 80):



residues_in_pLDDT
The number of residues in the AlphaFold model.




pLDDT_mean
The mean value of pLDDT over all residues.




pLDDT_median
The median value of pLDDT over all residues.




pLDDTNN_count
The number of residues within bounds.




pLDDTNN_frac
The fraction of pLDDT values within bounds, if the
count is greater than the minimum.




pLDDTNNN_mean
The mean of pLDDT values within bounds, if the
count is greater than the minimum.




pLDDTNN_median
The median of pLDDT values within bounds, if the
count is greater than the minimum.




LDDT_expect
The expectation value of global LDDT over the
residues with LDDT within bounds. Only
produced if default bounds are used.




passing
True if the model passed the criterion, False
otherwise. Only produced if default bounds are
used.




file
The path to the model file.









plddt-select-residues
Writes a tab-separated file of residues from passing models,
using an input file of values selected by plddt-stats.
Input options are the same as plddt-stats.
Output columns:



file
Path to the model file.




residue
Residue number, starting from 0 and numbered
sequentially. Note that all residues will be
written, regardless of bounds set.




pLDDT
pLDDT value for that residue.









plddt-plot-dists
Plot the distributions on the bounded pLDDT and residues in
models that pass the selection criteria.

Input Options:


out-file-type
Plot file extension of a type that matplotlib understands,
(e.g., ‘jpg’, ‘pdf’) [default: png]




residue-criterion
Per-residue cutoff on usability (for plot only).





Outputs:
When applied to set of “dark” genomes with no previous PDB entries, the
distributions of median pLDDT scores with a lower bound of 80 and
per-residue pLDDT scores with a minimum of 80 looks like this:







stats
Produce a set of summary stats on results of runs. See also the global
stats file rafm_stats.json.






Statistical Basis
The default parameters were chosen to select for LDDT values of greater
than 80 on a set of crystal structures obtained since AlphaFold was trained.
The distributions of LDDT scores for the passing and non-passing sets, along
with an (overlapping) set of AlphaFold model files at 100% sequence identity over
at least 80% of the sequence looks like this:

The markers on the x-axis refer to the size of conformational changes
observed in conformational changes in various protein crystal structures:


CALM
Between calcium-bound and calcium-free calmodulin
(depicted in the logo image above).




ERK2
Between unphosphorylated and doubly-phosphorylated ERK2 kinase.




HB
Between R- and T-state hemoglobin




MB
Between carbonmonoxy- and deoxy-myoglobin




The value of LDDT >= 80 we selected as the minimum value that was likely to
prove useful for virtual screening. The per-residue value of pLDDT >= 80
was also chosen as the minimum likely to give the correct side-chain rotamers
for a surface defined by contacts between two residues. A choice of 91.2 as a
criterion leads to the following confusion matrix versus a set of post-training
crystal structures:

At a correlation coefficient of 0.71, this correlation isn’t great, but enough
to demonstrate a usable sensitivity. After we fix a few problems with the
alignments, it may go a bit higher but our feeling is probably not
more than about 0.8. The support will get better, but the criterion on this
metric seems unlikely to change.


Contributing
Contributions are very welcome.
To learn more, see the Contributor Guide.


License
Distributed under the terms of the MIT license,
rafm is free and open source software.


Issues
If you encounter any problems,
please file an issue along with a detailed description.


Credits
This project was generated from the
UNM Translational Informatics Python Cookiecutter template.
rafm was written by Joel Berendzen and Jessica Binder.