Package: picnic-bio
PICNICPICNIC (Proteins Involved in CoNdensates In Cells) is a machine learning-based model that predicts proteins involved in biomolecular condensates. The first model (PICNIC) is based on sequence-based features and structure-based features derived from Alphafold2 models. Another model includes extended set of features based on Gene Ontology terms (PICNIC-GO). Although this model is biased by the already available annotations on proteins, it provides useful insights about specific protein properties that are enriched in proteins of biomolecular condensate. Overall, we recommend using PICNIC that is an unbiased predictor, and using PICNIC-GO for specific cases, for example for experimental hypothesis generation.External softwareIUPred2AIUPred2A is a tool that predicts disordered protein regions. It is available for download via the linkhttps://iupred2a.elte.hu/download_newThe downloaded archive should be unpacked into the "src/files/" directory.STRIDESTRIDE is a software for protein secondary structure assignment
Installation guide can be found herehttps://webclu.bio.wzw.tum.de/stride/Installation instructionsA binary installer for the latest released version is available at the Python Package Index (PyPI).RequirementsPython version 3.10+Download and unpack IUPred2AAdd IUPred2A to PYTHONPATHDownload and unpack STRIDEAdd STRIDE binary to your system PATHInstall external requirementsHow to install STRIDE?A complete installation guide can be foundhereor simply
run the following commands:$mkdirstride
$cdstride
$curl-OLhttps://webclu.bio.wzw.tum.de/stride/stride.tar.gz
$tar-zxfstride.tar.gz
$make
$exportPATH="$PATH:$PWD"How to install IUPred2A?IUPred2A software is available for free only for academic users and it cannot be used for commercial purpose.
If you are an academic user, then you can download IUPred2A by filling out the following formhere.# Step 1: Fill out the form above and download the IUPred2A tar ball$tar-zxfiupred2a.tar.gz
$cdiupred2a
$exportPYTHONPATH="$PWD"PICNIC is available on PyPI$python-mpipinstallpicnic_bioPICNIC officially supports Python 3.10+.PICNIC is also installable from source$gitclonegit@git.mpi-cbg.de:atplab/picnic.gitOnce you have a copy of the source, you can embed it in your own Python package, or install it into your site-packages easily$cdpicnic
$python3-mvenvpicnic-env$sourcepicnic-env/bin/activate(venv)$python-mpipinstall.How to install PICNIC using Conda?There isn't any binary installer available on Conda yet. Though it is possible to install PICNIC within a virtual Conda environment.Please note that in a conda environment you have to pre-install catboost, before installing picnic-bio itself, otherwise the installation will fail when compiling the catboost package from source code. Also it is recommended to use and set upconda-forgeto fetch pre-compiled versions of catboost.Please also note that catboost=1.2.2 is incompatible with Python 3.12. The maintainers of catboost are working towards a fix right now.We have documented how to get around the catboost installation issue.$condaconfig--addchannelsconda-forge
$condaconfig--setchannel_prioritystrict
$condacreate-nmyenvpython=[3.10,3.11]catboost=1.2.2
$condaactivatemyenv(myenv)$python-mpipinstallpicnic_bioHow to use?Usage - Using PICNIC from command line$ picnic <is_automated> <path_af> <uniprot_id> <is_go> --path_fasta_file <file>

usage: PICNIC [-h] [--path_fasta_file PATH_FASTA_FILE]
              is_automated path_af uniprot_id is_go

PICNIC (Proteins Involved in CoNdensates In Cells) is a machine learning-based
model that predicts proteins involved in biomolecular condensates.

positional arguments:
  is_automated          True if automated pipeline (works for proteins with
                        length < 1400 aa, with precalculated Alphafold2 model,
                        deposited to UniprotKB), else manual pipeline
                        (uniprot_id, Alphafold2 model(s) and fasta file are
                        needed to be provided as input)
  path_af               directory with pdb files, created by Alphafold2 for
                        the protein in the format. For smaller proteins ( <
                        1400 aa length) AlphaFold2 provides one model, that
                        should be named: AF-uniprot_id-F1-v{j}.pdb, where j is
                        a version number. In case of large proteins Alphafold2
                        provides more than one file, and all of them should be
                        stored in one directory and named: 'AF-
                        uniprot_id-F{i}-v{j}.pdb', where i is a number of
                        model, j is a version number.
  uniprot_id            protein identifier in UniprotKB (should correspond to
                        the name 'uniprot_id' for Alphafold2 models, stored in
                        directory_af_models)
  is_go                 boolean flag; if 'True', picnic_go score (picnic
                        version with Gene Ontology features) will be
                        calculated, Gene Ontology terms are retrieved in this
                        case from UniprotKB by uniprot_id identifier;
                        otherwise default picnic score will be printed
                        (without Gene Ontology annotation)

options:
  -h, --help            show this help message and exit
  --path_fasta_file PATH_FASTA_FILE
                        directory with sequence file in fasta formatExamplesRun automated pipeline:$picnicTruenotebooks/test_files/Q99720/Q99720TrueRun manual pipeline:$picnicFalse'notebooks/test_files/O95613/''O95613'False--path_fasta_file'notebooks/test_files/O95613/O95613.fasta.txt'Examples of using PICNIC are shown in a jupyter-notebook in notebooks folder.Link to paperDOI: 10.1101/2023.06.01.543229