Package: thoipapy
THOIPApyThe Transmembrane HOmodimer Interface Prediction Algorithm (THOIPA) is a machine learning method for the analysis of protein-protein-interactions.THOIPA predicts transmembrane homodimer interface residues from evolutionary sequence information.THOIPA helps predict potential homotypic transmembrane interface residues, which can then be verified experimentally.
THOIPA also aids in the energy-based modelling of transmembrane homodimers.Important links:THOIPA webserverTHOIPA FAQTHOIPA wiki main pageHow does thoipapy work?downloads protein homologues with BLASTextracts residue properties (e.g. residue conservation and polarity)trains a machine learning classifiervalidates the prediction performancecreates heatmaps of residue properties and THOIPA predictionInstallationpip install thoipapyTHOIPA has only been tested on Linux, due to reliance on external dependencies such as FreeContact, Phobius, CD-HIT and rate4site.
For predictions only, a dockerised version is available that runs on Windows or MacOS.
Please see theTHOIPA webserverfor the latest information.DependenciesWe recommend theAnaconda python distribution, which contains all the required python modules
(numpy, scipy, pandas,biopython and matplotlib). THOIPApy is currently tested for python 3.8.5. The requirements.txt contains a snapshot of compatible
dependencies.Development statusThe code has been extensively updated and annotated for public release. However is released “as is” with some known issues, limitations and legacy code.Usage as a standalone predictorfirst check if your needs are met by theTHOIPA webserveror the latest version of dockerised softwarefor local predictions on linux, first install phobius, NCBI_BLAST, biopython, freecontact, CD-HIT, and rate4siteplease seethoipapy/test/functional/test_standalone_prediction.pyfor the latest run syntax, typicallyfromthoipapy.thoipaimportget_md5_checksum,run_THOIPA_predictionfromthoipapy.utilsimportmake_sure_path_existsprotein_name="ERBB3"TMD_seq="MALTVIAGLVVIFMMLGGTFL"full_seq="MVQNECRPCHENCTQGCKGPELQDCLGQTLVLIGKTHLTMALTVIAGLVVIFMMLGGTFLYWRGRRIQNKRAMRRYLERGESIEPLDPSEKANKVLA"out_dir="/path/to/your/desired/output/folder"make_sure_path_exists(out_dir)md5=get_md5_checksum(TMD_seq,full_seq)run_THOIPA_prediction(protein_name,md5,TMD_seq,full_seq,out_dir)Example Outputthe output includes a csv showing the THOIPA prediction for each residue, as well as a heatmap figure as a summarybelow is a heatmap showing the THOIPA prediction, and underlying conservation, relative polarity, and coevolutionCreate your own machine learning predictorTHOIPA can be retrained to any dataset of your choicethe original set of training sequences and other resources are available via theOpen Science Foundationthe THOIPA feature extraction, feature selection, and training pipeline is fully automatedcontact us for an introduction to the THOIPA software pipeline and settingspythonpath/to/thoipapy/run.py-shome/user/thoipa/THOIPA_settings.xlsxLicenseTHOIPApy is free software distributed under the permissive MIT License.ContributeContributors are welcome.For feedback or troubleshooting, please email us directly and initiate an issue in Github.ContactMark Teese,TNG Technology Consulting GmbH, formerly of theLangosch Labat theTechnical University of MunichBo Zeng,Chinese Academy of Sciences, Beijingformerly of theFrishman Labat theTechnical University of MunichCitationYao Xiao, Bo Zeng, Nicola Berner, Dmitrij Frishman, Dieter Langosch, and Mark George Teese (2020)
Experimental determination and data-driven prediction of homotypic transmembrane domain interfaces,
Computational and Structural Biotechnology Journal