sidebar-main
title
griff
banner-button_0 banner-button_Layer-7 banner_button_03 banner-button_Layer-4 banner-button_05 banner-button_Layer-5 banner-button_07
banner-button_08 banner-button_09 banner-button_10
Bioinformatics Unit banner
   homeoffresearchoffconfoffprogoffmemoffpuboffvacofflinkoff
   tabfoot tabfoot-bgtabfoot-bgtabfoot-bgtabfoot-bgtabfoot-bgtabfoot-bgtabfoot-bgtabfoot-bgtabfoot-bgtabfoot-bgtabfoot-bgtabfoot-bgtabfoot-bgtabfoot-bgtabfoot-bg

Seeing the Trees through the Forest:
Sequence-based Homo- and Heteromeric
Protein-protein Interaction sites prediction
using Random Forest

This is the download page for our work described in the following papers. Please read them before using our tool, and please cite them afterwards :-)

Qingzhen Hou, Paul De Geest, Wim F. Vranken1, Jaap Heringa and K. Anton Feenstra (2017). Seeing the Trees through the Forest: Sequence-based Homo- and Heteromeric Protein-protein Interaction sites prediction using Random Forest. Bioinformatics 33, pp 1479–1487, 2017, doi: 10.1093/bioinformatics/btx005, 2017
1Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB & Structural Biology Brussels, VUB & Structural Biology Research Centre, VIB; Brussels 1050, Belgium.
Qingzhen Hou3, Paul De Geest, Christian Griffioen, Sanne Abeln, Jaap Heringa, K. Anton Feenstra (2019). SeRenDIP: SEquential REmasteriNg to DerIve profiles for fast and accurate predictions of PPI interface positions. Bioinformatics 35, pp 4794–4796, 2019, doi: 10.1093/bioinformatics/btz428
33BIO-BioInfo – BioModeling, BioInformatics & BioProcesses, Université Libre de Bruxelles
Qingzhen Hou2, Bas Stringer, Katharina Waury, Reza Haydarlou, Sanne Abeln, Jaap Heringa, K. Anton Feenstra (2020). SeRenDIP-CE: SEquence-based Random forestpredictor with lENgth and Dynamics for Epitope Prediction. (in preparation)
2Department of Biostatistics, School of Public Health & National institute of health data science of China; Shandong 250002, P. R. China

We have created a web-server "SeRenDIP — SEquence-based Random forest predictor with lENgth and Dynamics for Interacting Proteins" which allows you to try out your queries of interest. However, runtimes can be quite substantial on our server. If you need more performance, you may instead rather want to use the stand alone version supplied in the download link below.

As a note of caution, some of the R scripts included in the main archives turn out to be quite sensitive to the R version used. We know they should work with R 2.15.0 and R 3.2.1 (which is what the webserver runs on), and it doesn't work with at least one older version of R. We do not know if newer R versions work correctly or not. Critical point here, is that with the wrong R version, results are produced but they are incorrect. You're best off to check the prediction from your 'home installation' for one or a few proteins against our webserver.

Download Description MD5
Hou_etal_SERENDEP_predictors.tar.gz Archive containing RF predictors for Epitopes, based on Hou et al. 2020 (in prep). (256MB) e96b3cd80dfb775f91e0679c8f856368
Hou_etal_RF-PPI_predictors_datasets.tar.gz Archive containing README, test R script, predictors and test datasets, based on Hou et al., 2017. Bioinformatics 33 pp 1479–1487 and Hou et al., 2019. Bioinformatics 35, pp 4794–4796 (836MB) 694ea6804aebc2d5690b23ffb8c81098
Hou_etal_RF-PPI_combined.tar.gz Archive containing the combined training datasets (homodimer and heterodimer proteins), based on Hou, et al. 2015, BMC Bioinformatics 16:325. (321M) e9a7744ef195ba80c86f1a67afd2cf2b
Hou_etal_RF-PPI_hm_476_test_train_5fold.tar.gz Archive containing training datasets for the homodimer proteins, based on Hou, et al. 2015, BMC Bioinformatics 16:325. (434MB) 783d2b4ca7f305f3603beb90bfe24a33
Hou_etal_RF-PPI_dset_119_train.tar.gz Archive containing training datasets for the heteromeric proteins, based on Murakami and Mizuguchi, 2010 Bioinformatics, 26:1841–1848. (14.2MB) 7b708dcf7767492730e5ff9f17957aeb
README.md README file separately.
LICENSE LICENSE separately.
GNU General Public License v3.0 - GNU Project - Free Software Foundation (FSF).txt GNU GPL v3.0 - Free Software Foundation (FSF) separately.

    These scripts and datafiles are provided as-is, and come without 
    any warranty whatsoever. If it works for you, great, let me know!
    If it doesn't work for you, we'd be happy to try and help you fix it. 
    If it destroys your universe, too bad (you may still file a bug report).
 
    Copyright (c) 2016 Q. Hou q.hou@vu.nl, 
                       P. De Geest pauldegeest1@gmail.com, 
                       W.F. Vranken wvranken@vub.ac.be, 
                       J. Heringa j.heringa@vu.nl, 
                       K. Anton Feenstra k.a.feenstra@vu.nl.

    This program is free software: you can redistribute it and/or modify
    it under the terms of the GNU General Public License as published by
    the Free Software Foundation, either version 3 of the License, or
    (at your option) any later version.

    This program is distributed in the hope that it will be useful,
    but WITHOUT ANY WARRANTY; without even the implied warranty of
    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
    GNU General Public License for more details.

    You should have received a copy of the GNU General Public License
    along with this program.  If not, see www.gnu.org/licenses/.

(c) IBIVU 2021. If you are experiencing problems with the site, please contact the webmaster.