Package: metapredict
metapredict: A machine learning-based tool for predicting protein disorder.Last updated November 2023Current version: metapredict V2-FF (V2.6)The current recommended and default version of metapredict is metapredict V2-FF (version 2.6). Small increments (2.6.x) may be made as bug fixes or feature enhancements.For context, V2-FF provides identical predictions to metapredict V2, but viapredict_disorder_batch()provides 10-100x improvement in performance on CPUs and GPUs.To quantify this yourself, run:import metapredict
metapredict.print_performance(batch=True)
metapredict.print_performance(batch=False)To compare the number of residues-per-second metapredict V2-FF predicts in batch mode vs. non-batch mode. For CPUs this is typically a 10-20x improvement. If GPUs are available this value can be substantially higher.InstallationThe current stable version ofmetapredictis available through GitHub or the Python Package Index (PyPI).To install from PyPI, run:pip install metapredictYou can also install the current development version frompip install git+https://git@github.com/idptools/metapredictTo clone the GitHub repository and gain the ability to modify a local copy of the code, rungit clone https://github.com/idptools/metapredict.git
cd metapredict
pip install -e .Note you will need the -e flag to ensure thecythoncode compiles correctly, but this also means the installed version is linked to the local version of the code.This will installmetapredictlocally. If you modify the source code in the local repository, be sure to re-install withpip.DocumentationDocumentation for metapredict automatically builds from the/docdirectory in this repository and is hosted athttps://metapredict.readthedocs.io/.In brief, metapredict provides both command-line tools and a set of user-face functions from the metapredict python module. Both sets of tools are fully documented online.How can I use metapredict?Metapredict can be used in four different ways:As a stand-alone command-line tool (installable via pip - the code in this repository).As a Python library for integrating into your favorite bioinformatics pipeline (installable via pip - the code in this repository).As a web-server for examining disorder predictions on individual sequences found athttps://metapredict.net/.NEW as of August 2022:as a Google Colab notebook for batch-predicting disorder scores for larger numbers of sequences:LINK HERE. Performance-wise, batch mode can predict the entire yeast proteome in ~1.5 min.NEW as of May 2023:as part of theALBATROSS paper, we provide a colab notebook for predicting IDRs on a proteome-wide scaleLINK HERE.How to citeIf you use metapredict for your work, please cite the metapredict paper:Emenecker, R. J., Griffith, D. & Holehouse, A. S. Metapredict: a fast, accurate, and easy-to-use predictor of consensus disorder and structure. Biophys. J. 120, 4312–4319 (2021).Note that in addition to the original paper, there's a V2 preprint; HOWEVER, we ask you only cite the original paper and describe the version being used (V1, V2 or V2-FF).Emenecker, R. J., Griffith, D. & Holehouse, A. S. Metapredict V2: An update to metapredict, a fast, accurate, and easy-to-use predictor of consensus disorder and structure. bioRxiv 2022.06.06.494887 (2022). doi:10.1101/2022.06.06.494887## ChangesChangesFor changes see thechangelog.mdfile in this directory.AcknowledgementsPARROT, created by Dan Griffith, was used to generate the network used for metapredict. Seehttps://pypi.org/project/idptools-parrot/for some very cool machine learning stuff.In addition to using Dan Griffith's tool for creating metapredict, the original code forbrnn_architecture.pyandencode_sequence.pywas written by Dan.We would like to thank theDeepMindteam for developing AlphaFold and EBI/UniProt for making these data so readily available.We would also like to thank the team at MobiDB for creating the database that was used to train this predictor. Check out their awesome stuff athttps://mobidb.bio.unipd.itCopyrightCopyright (c) 2020-2023, Holehouse Lab - Washington University School of Medicine