proteinflow 1.1.0

Versatile pipeline for processing protein structure data for deep learning applications.

ProteinFlow

A data processing pipeline for all your protein design needs.

Read the documentation.

Installation

Recommended: create a new conda environment and install proteinflow and mmseqs. Note that the python version has to be between 3.8 and 3.10.

conda create --name proteinflow -y python=3.9
conda activate proteinflow
conda install -y -c conda-forge -c bioconda mmseqs2
python -m pip install proteinflow

In addition, proteinflow depends on the rcsbsearch package and the latest release v0.2.3 is currently not functioning . Follow the recommended fix:

python -m pip install "rcsbsearch @ git+https://github.com/sbliven/rcsbsearch@dbdfe3880cc88b0ce57163987db613d579400c8e"

You do not need to install mmseqs or rcsbsearch if you are planning to

Usage

Downloading pre-computed datasets (stable)

Already precomputed datasets with consensus set of parameters and can be accessed and downloaded using the proteinflow. package. Check the output of proteinflow check_tags for a list of available tags.

proteinflow download --tag 20221110 

Running the pipeline

You can also run proteinflow with your own parameters. Check the output of proteinflow check_snapshots for a list of available PDB snapshots (naming rule: {year}{month}{day}).

For instance, let's generate a dataset with the following description:

• resolution threshold: 5 angstrom,
• PDB snapshot: 20190101,
• structure methods accepted: all (x-ray christolography, NRM, Cryo-EM),
• sequence identity threshold for clustering: 40% sequence similarity,
• maximum length per sequence: 1000 residues,
• minimum length per sequence: 5 residues,
• maximum fraction of missing values at the ends: 10%,
• size of validation subset: 10%.

proteinflow generate --tag new --resolution_thr 5 --pdb_snapshot 20190101 --not_filter_methods --min_seq_id 0.4 --max_length 1000 --min_length 5 --missing_ends_thr 0.1 --valid_split 0.1

See the docs (or proteinflow generate --help) for the full list of parameters and more information.

A registry of all the files that are removed during the filtering as well as description with the reason for their removal is created automatically for each generate command. The log files are save (at data/logs by default) and a summary can be accessed running proteinflow get_summary {log_path}.

Splitting

By default, both proteinflow generate and proteinflow download will also split your data into training, test and validation according to MMseqs2 clustering and homomer/heteromer/single chain proportions. However, you can skip this step with a --skip_splitting flag and then perform it separately with the proteinflow split command.

The following command will perform the splitting with a 10% validation set, a 5% test set and a 50% threshold for sequence identity clusters.

proteinflow split --tag new --valid_split 0.1 --test_split 0.5 --min_seq_id 0.5

Using the data

The output files are pickled nested dictionaries where first-level keys are chain Ids and second-level keys are the following:

• 'crd_bb': a numpy array of shape (L, 4, 3) with backbone atom coordinates (N, C, CA, O),
• 'crd_sc': a numpy array of shape (L, 10, 3) with sidechain atom coordinates (check proteinflow.sidechain_order() for the order of atoms),
• 'msk': a numpy array of shape (L,) where ones correspond to residues with known coordinates and zeros to missing values,
• 'seq': a string of length L with residue types.

Once your data is ready, you can open the files directly with pickle to access this data.

import pickle
import os

train_folder = "./data/proteinflow_new/training"
for filename in os.listdir(train_folder):
    with open(os.path.join(train_folder, filename), "rb") as f:
        data = pickle.load(f)
    crd_bb = data["crd_bb"]
    seq = data["seq"]
    ...

Alternatively, you can use our ProteinDataset or ProteinLoader classes for convenient processing. Among other things, they allow for feature extraction, single chain / homomer / heteromer filtering and randomized sampling from sequence identity clusters.

For example, here is how we can create a data loader that:

• samples a different cluster representative at every epoch,
• extracts dihedral angles, sidechain orientation and secondary structure features,
• only loads pairs of interacting proteins (larger biounits are broken up into pairs),
• has batch size 8.

from proteinflow import ProteinLoader
train_loader = ProteinLoader(
    "./data/proteinflow_new/training", 
    clustering_dict_path="./data/proteinflow_new/splits_dict/train.pickle",
    node_features_type="dihedral+sidechain_orientation+secondary_structure",
    entry_type="pair",
    batch_size=8,
)
for batch in train_loader:
    crd_bb = batch["X"] #(B, L, 4, 3)
    seq = batch["S"] #(B, L)
    sse = batch["secondary_structure"] #(B, L, 3)
    to_predict = batch["masked_res"] #(B, L), 1 where the residues should be masked, 0 otherwise
    ...

See more details on available parameters and the data format in the docs.

ProteinFlow Stable Releases

Tag	Date	Location (S3)	Size	Min res	Min len	Max len	MMseqs thr	Split (train/val/test)	Missing thr (ends/middle)
paper	10.11.22	data split	24G	3.5	30	10000	0.3	90/5/5	0.3/0.1

License

The proteinflow package and data are released and distributed under the BSD 3-Clause License

Contributions

This is an open source project supported by Adaptyv Bio. Contributions, suggestions and bug-fixes are welcomed.