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SeRenDIP — SEquence-based Random forest predictor with lENgth and Dynamics for Interacting Proteins

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For stable links, please refer to www.ibi.vu.nl/programs/ or www.ibi.vu.nl/programs/serendipwww/index.php.
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The SeRenDIP server offers a simple interface to our random-forest based method for predicting protein-protein interface positions from a single input sequence. You can input your sequence of interest, and obtain a table of predicted interface positions.

Please note that runtimes are typically around or below three hours, but may be up to 15 hours, depending on the number of blast hits for your query. If you need more performance, you may instead rather want to use the stand alone version supplied on the download page.

help Paste in your input sequence:   Showcase 1YVB:A

or  upload your alignment:

Select random forest model trained on dataset.

The 'Combined' model is a good overall choice, both for heteromeric and homomeric interactions. The 'Heteromeric' model performs slightly better on predicting heteromeric interactions, so this may be the better option if you know or suspect you are looking for heteromeric interactions.

       
 

Example output:

Please cite:

Qingzhen Hou1, Paul De Geest, Christian Griffioen, Sanne Abeln, Jaap Heringa, K. Anton Feenstra. SeRenDIP: remastered alignment profiles for fast and accurate predictions of PPI interface positions. (in preparation).
13BIO-BioInfo – BioModeling, BioInformatics & BioProcesses, Université Libre de Bruxelles

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 2017, doi: 10.1093/bioinformatics/btx005
1Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB & Structural Biology Brussels, VUB & Structural Biology Research Centre, VIB; Brussels.

External prediction methods used:

The SeRenDIP web server developed and Copyright (c) by K. Anton Feenstra, Paul de Geest and Qingzhen Hou.

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