aposteriori is a library for the voxelization of protein structures for protein design. It uses conventional PDB files to create fixed discretized areas of space called "frames". The atoms belonging to the side-chain of the residues are removed so to allow a Deep Learning classifier to determine the identity of the frames based solely on the protein backbone structure.
Coming soon...
pip install aposterioriChange directory to the aposteriori folder if you have not done so already:
git clone https://github.com/wells-wood-research/aposteriori/tree/master
cd aposteriori/Install aposteriori
pip install .There are two ways to create a dataset using aposteriori: through the Python API in
aposteriori.make_frame_dataset or using the command line tool make-frame-dataset that
installs along side the module:
make-frame-dataset /path/to/folderIf you want to try out an example, run:
make-frame-dataset tests/testing_files/pdb_files/Check the make-frame-dataset help page for more details on its usage:
Usage: make-frame-dataset [OPTIONS] STRUCTURE_FILE_FOLDER
Creates a dataset of voxelized amino acid frames.
A frame refers to a region of space around an amino acid. For every
residue in the input structure(s), a cube of space around the region (with
an edge length equal to `--frame_edge_length`, default 12 Å), will be
mapped to discrete space, with a defined number of voxels per edge (equal
to `--voxels-per-side`, default = 21).
Basic Usage:
`make-frame-dataset $path_to_folder_with_pdb/`
eg. `make-frame-dataset tests/testing_files/pdb_files/`
This command will make a tiny dataset in the current directory
`test_dataset.hdf5`, containing all residues of the structures in the
folder.
Globs can be used to define the structure files to be processed. `make-
frame-dataset pdb_files/**/*.pdb` would include all `.pdb` files in all
subdirectories of the `pdb_files` directory.
You can process gzipped pdb files, but the program assumes that the format
of the file name is similar to `1mkk.pdb.gz`. If you have more complex
requirements than this, we recommend using this library directly from
Python rather than through this CLI.
The hdf5 object itself is like a Python dict. The structure is simple:
└─[pdb_code] Contains a number of subgroups, one for each chain.
└─[chain_id] Contains a number of subgroups, one for each residue.
└─[residue_id] voxels_per_side^3 array of ints, representing element number.
└─.attrs['label'] Three-letter code for the residue.
└─.attrs['encoded_residue'] One-hot encoding of the residue.
└─.attrs['make_frame_dataset_ver']: str - Version used to produce the dataset.
└─.attrs['frame_dims']: t.Tuple[int, int, int, int] - Dimentsions of the frame.
└─.attrs['atom_encoder']: t.List[str] - Lables used for the encoding (eg, ["C", "N", "O"]).
└─.attrs['encode_cb']: bool - Whether a Cb atom was added at the avg position of (-0.741287356, -0.53937931, -1.224287356).
└─.attrs['atom_filter_fn']: str - Function used to filter the atoms in the frame.
└─.attrs['residue_encoder']: t.List[str] - Ordered list of residues corresponding to the encoding used.
└─.attrs['frame_edge_length']: float - Length of the frame in Angstroms (A)
└─.attrs['voxels_as_gaussian']: bool - Whether the voxels are encoded as a floating point of a gaussian (True) or boolean (False)
So hdf5['1ctf']['A']['58'] would be an array for the voxelized.
Options:
Options:
-o, --output-folder PATH Path to folder where output will be written.
Default = `.`
-n, --name TEXT Name used for the dataset file, the `.hdf5`
extension does not need to be included as it
will be appended. Default = `frame_dataset`
-e, --extension TEXT Extension of structure files to be included.
Default = `.pdb`.
--pieces-filter-file PATH Path to a Pieces format file used to filter
the dataset to specific chains inspecific
files. All other PDB files included in the
input will be ignored.
--frame-edge-length FLOAT Edge length of the cube of space around each
residue that will be voxelized. Default =
12.0 Angstroms.
--voxels-per-side INTEGER The number of voxels per side of the frame.
This will give a final cube of `voxels-per-
side`^3. Default = 21.
-p, --processes INTEGER Number of processes to be used to create the
dataset. Default = 1.
-z, --is_pdb_gzipped If True, this flag indicates that the
structure files are gzipped. Default =
False.
-r, --recursive If True, all files in all subfolders will be
processed.
-v, --verbose Sets the verbosity of the output, use `-v`
for low level output or `-vv` for even more
information.
-cb, --encode_cb BOOLEAN Encode the Cb at an average position
(-0.741287356, -0.53937931, -1.224287356) in
the aligned frame, even for Glycine
residues. Default = True
-ae, --atom_encoder [CNO|CNOCB|CNOCBCA]
Encodes atoms in different channels,
depending on atom types. Default is CNO,
other options are ´CNOCB´ and `CNOCBCA` to
encode the Cb or Cb and Ca in different
channels respectively. [required]
-d, --download_file PATH Path to csv file with PDB codes to be
voxelised. The biological assembly will be
used for download. PDB codes will be
downloaded the /pdb/ folder.
-g, --voxels_as_gaussian BOOLEAN
Boolean - whether to encode voxels as
gaussians (True) or voxels (False). The
gaussian representation uses the
wanderwaal's radius of each atom using the
formula e^(-x^2) where x is Vx - x)^2 + (Vy
- y)^2) + (Vz - z)^2)/ r^2 and (Vx, Vy, Vz)
is the position of the voxel in space. (x,
y, z) is the position of the atom in space,
r is the Van der Waal’s radius of the atom.
They are then normalized to add up to 1.
-b, --blacklist_csv PATH Path to csv file with structures to be
removed.
-comp, --compression_gzip BOOLEAN
Whether to comrpess the dataset with gzip
compression.
-vas, --voxelise_all_states BOOLEAN
Whether to voxelise only the first state of
the NMR structure (False) or all of them
(True).
-rot, --tag_rotamers BOOLEAN Whether to tag rotamer information to the
frame (True) or not (False).
--help Show this message and exit.
Ideally, if you are trying to solve the Inverse Protein Folding Problem , you should use Biological Units as they are the minimal functional part of a protein. This prevents having solvent-exposed hydrophobic residues as training data.
Download the dataset:
- ftp://ftp.ebi.ac.uk/pub/databases/pdb/data/biounit/PDB (European Server) Alternative servers are available here (https://www.wwpdb.org/ftp/pdb-ftp-sites)
To read more about biological units: https://pdbj.org/help/about-aubu and https://pdb101.rcsb.org/learn/guide-to-understanding-pdb-data/biological-assemblies
Once the dataset is downloaded, you will have a directory with sub-directory containig the gzipped PDB structures (ie. your Protein Data Bank Files).
To voxelize the structures into frames, run:
make-frame-dataset /path/to/biounits/ -e .pdb1.gz If everything went well, you should be seeing the number of structures that will be voxelised and a list of default parameters, to which you will press "y " to proceed.
PISCES (Protein Sequence Culling Server) is a curated subset of protein structures. Each file contains a list of structures with parameters such as resolution, percentage identity and R-Values.
Aposteriori supports filtering with a PISCES file as such:
make-frame-dataset /path/to/biounits/ -e .pdb1.gz --pieces-filter-file
path/to/pisces/cullpdb_pc90_res1.6_R0.25_d190114_chains8082If everything went well, you should be seeing the number of structures that will be voxelised and a list of default parameters, to which you will press "y " to proceed.
The easiest way to install a development version of aposteriori is using Conda:
Create the environment:
conda create -n aposteriori python=3.8Activate it and clone the repository:
conda activate aposteriori
git clone https://github.com/wells-wood-research/aposteriori.git
cd aposteriori/Install dependencies:
pip install -r dev-requirements.txtInstall aposteriori:
pip install .Check that aposteriori works
make-frame-dataset --helpMake sure you test your install:
pytest tests/Alternatively you can install the repository with pip:
git clone https://github.com/wells-wood-research/aposteriori.git
cd aposteriori/
pip install -r dev-requirements.txtInstall aposteriori:
pip install .