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

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The SeRenDIP server offers a simple interface to our random-forest based methods for predicting protein-protein interaction (PPI) positions from a single input sequence (SeRenDIP). We have published predictors for heteromeric PPIs ('Heteromeric') and for generic homomeric/​heteromeric PPIs ('Combined').

We have also added two development predictors SeRendIP-CE for Conformational Epitope interactions: specific for epitopes ('Epitope'), or generic for either epitope or heteromeric interactions ('EpiComb'); the manuscript for the epitope interaction prediction is submitted.

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. help

       
 

Example output:

Showcases SeRenDIP PPI interface prediction for SARS-CoV-2 proteins:

Showcases SeRenDIP-CE conformational epitope prediction for SARS-CoV-2 proteins:

Please cite:
Qingzhen Hou1, Bas Stringer, Katharina Waury, Henriette Capel, Reza Haydarlou, Sanne Abeln, Jaap Heringa, K. Anton Feenstra. SeRenDIP-CE: Sequence-based Interface Prediction for Conformational Epitopes. Bioinformatics advance 11 May 2021, doi: 10.1093/bioinformatics/btab321
1Department of Biostatistics, School of Public Health Cheeloo College of Medicine & National institute of health data science of China; Shandong 250002, P. R. China

Qingzhen Hou1, Paul De Geest, Christian Griffioen, Sanne Abeln, Jaap Heringa, K. Anton Feenstra. 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 .
13BIO-BioInfo – BioModeling, BioInformatics & BioProcesses, Université Libre de Bruxelles

Qingzhen Hou, Paul De Geest, Wim F. Vranken1, Jaap Heringa, K. Anton Feenstra. 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
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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