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

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 predictors for heteromeric PPIs ('Heteromeric') and for generic homomeric/​heteromeric PPIs ('Combined') (Hou et al., 2017, 2019). We also have two predictors SeRenDIP-CE for Conformational Epitope interactions: specific for epitopes ('Epitope'), or generic for either epitope or heteromeric interactions ('EpiComb') (Hou et al., 2021). 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 use the RF models supplied on the download page. You may also be interested in our PIPENN: Protein Interface Prediction from sequence with an Ensemble of Neural Nets web-server which allows you to try out your queries of interest. Typical runtimes per protein for PIPENN are on the order of 10 minutes.	Paste in your input sequence:   Showcase 2FHZ:A or  upload your alignment: Choose between Homo/Heteromeric (combined) PPI, Heteromeric specific PPI, Epitope/Heteromeric combined, or Epitope specific prediction: select random forest model trained on Combined Heteromeric EpiComb Epitope dataset.
Example output: pdb 1YVB chain A Plasmodium falciparum Cysteine Protease pdb 2FHZ chain A Escherichia coli Colicin-E5 immunity protein Showcases SeRenDIP PPI interface prediction for SARS-CoV-2 proteins: P0DTC1 — Name: SARS-CoV-2 Replicase polyprotein 1a — UniProtKB: R1A_SARS2 P0DTC2 — Name: SARS-CoV-2 Spike glycoprotein — UniProtKB: SPIKE_SARS2 P0DTC3 — Name: SARS-CoV-2 Protein 3a — UniProtKB: AP3A_SARS2 P0DTC4 — Name: SARS-CoV-2 Envelope small membrane protein — UniProtKB: VEMP_SARS2 P0DTC5 — Name: SARS-CoV-2 Membrane protein — UniProtKB: VME1_SARS2 P0DTC6 — Name: SARS-CoV-2 Non-structural protein 6 — UniProtKB: NS6_SARS2 P0DTC7 — Name: SARS-CoV-2 Protein 7a — UniProtKB: NS7A_SARS2 P0DTC9 — Name: SARS-CoV-2 Nucleoprotein — UniProtKB: NCAP_SARS2 P0DTD1 — Name: SARS-CoV-2 Replicase polyprotein 1ab — UniProtKB: R1AB_SARS2 P0DTD2 — Name: SARS-CoV-2 Protein 9b — UniProtKB: ORF9B_SARS2 P0DTD3 — Name: SARS-CoV-2 Uncharacterized protein 14 — UniProtKB: Y14_SARS2 Showcases SeRenDIP-CE conformational epitope prediction for SARS-CoV-2 proteins: P0DTC1 — Name: SARS-CoV-2 Replicase polyprotein 1a — UniProtKB: R1A_SARS2 P0DTC2 — Name: SARS-CoV-2 Spike glycoprotein — UniProtKB: SPIKE_SARS2 P0DTC3 — Name: SARS-CoV-2 Protein 3a — UniProtKB: AP3A_SARS2 P0DTC4 — Name: SARS-CoV-2 Envelope small membrane protein — UniProtKB: VEMP_SARS2 P0DTC5 — Name: SARS-CoV-2 Membrane protein — UniProtKB: VME1_SARS2 P0DTC6 — Name: SARS-CoV-2 Non-structural protein 6 — UniProtKB: NS6_SARS2 P0DTC7 — Name: SARS-CoV-2 Protein 7a — UniProtKB: NS7A_SARS2 P0DTC9 — Name: SARS-CoV-2 Nucleoprotein — UniProtKB: NCAP_SARS2 P0DTD1 — Name: SARS-CoV-2 Replicase polyprotein 1ab — UniProtKB: R1AB_SARS2 P0DTD2 — Name: SARS-CoV-2 Protein 9b — UniProtKB: ORF9B_SARS2 P0DTD3 — Name: SARS-CoV-2 Uncharacterized protein 14 — UniProtKB: Y14_SARS2 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 37, pp 3421–3427, 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: DynaMine: Cilia, E., Pancsa, R., Tompa, P., Lenaerts, T., and Vranken, W. (2013). From protein sequence to dynamics and disorder with DynaMine. Nature communications, 4:2741. Cilia, E., Pancsa, R., Tompa, P., Lenaerts, T., and Vranken, W. F. (2014). The DynaMine web-server: predicting protein dynamics from sequence. Nucleic acids research, 42(W1):W264—W270. NetSurfP: Petersen, B., Petersen, T. N., Andersen, P., Nielsen, M., and Lundegaard, C. (2009). A generic method for assignment of reliability scores applied to solvent accessibility predictions. BMC structural biology, 9(1):1.
The SeRenDIP web server developed and Copyright (c) by K. Anton Feenstra, Paul de Geest and Qingzhen Hou.