Struct2Net PPI Prediction Server

The Struct2Net server makes structure-based computational predictions of protein-protein interactions (PPIs). The input to Struct2Net is either one or two amino acid sequences in FASTA format. The output gives a list of interactors if one sequence is provided and an interaction prediction if two sequences are provided.

The predictions are made by a structure-based threading approach. Given two protein sequences (or one sequence against all sequences of a species), the structure-based interaction prediction technique threads the sequence to all the protein complexes in the PDB and then chooses the best potential match. Based on this match, it uses machine learning techniques to predict whether the two proteins interact. This approach currently does not rely on any other functional genomic information (e.g. co-expression or cellular localization) and is hence independent of these. It may serve as an additional input into an integrative computational framework for predicting novel PPIs based on information from multiple sources.

See our publication in PSB 2006 for more information about the approach.

Web services provided by Struct2Net are available on the Download page.

UPDATES:

[2011]: Improved predictions using the iWRAP algorithm. Select "iWRAP" as your prediction algorithm when making your query selection.

[2012]: We are currently incorporating Coev2Net into Struct2Net. For the time being, please refer to Coev2Net.

For predictions in H. sapiens, S. cerevisiae and D. melanogaster

Enter query gene names/ids or keyword

Load sample data Clear
(e.g. Gene symbol, Ensembl, EntrezGene, RefSeq, UniProtKB, FlyBase, SGD)

Enter subject gene name/id (optional)

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Organism

Prediction algorithm

Enter your query protein sequence here in FASTA format

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Enter your subject protein sequence here FASTA format (optional)

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OR batch query:
Upload a file containing multiple pairs of sequences in FASTA format (max 4)

Sample file Clear

Select query method

Query PPIs to best-hit homolog in yeast, fly, or human (fast)

Organism:

Thread sequences onto all templates (slow)

Email address:

Recommended when choosing to thread onto templates.

Prediction algorithm

Please cite the following references:

R. Hosur, J. Peng, A. Vinayagam, U. Stelzl, J. Xu, N. Perrimon, J. Beinkowska, and B. Berger. 2012. "Coev2Net: a computational framework for boosting confidence in high-throughput protein-protein interaction datasets." Genome Biology, doi:10.1186/gb-2012-13-8-r76. [PDF]

R. Hosur, J. Xu, J. Beinkowska, and B. Berger. 2011. "iWRAP: An Interface Threading Approach with Application to Prediction of Cancer-Related Protein-Protein Interactions." Journal of Molecular Biology, doi:10.1016/j.jmb.2010.11.025. [Abstract]

R. Singh, D. Park, J. Xu, R. Hosur, and B. Berger. 2010. "Struct2Net: a Web-Service to Predict Protein-Protein Interactions Using a Structure-based Approach." Nucleic Acids Research, doi:10.1093/nar/gkq481. [PDF]

R. Singh, J. Xu, and B. Berger. 2006. "Struct2Net: Integrating Structure into Protein-Protein Interaction Prediction." Proc 11th Pacific Symposium on Biocomputing 11: 403-414. [PDF] [Supplementary Info]

Questions or comments? Please contact struct2net@csail.mit.edu

MIT | CSAIL | Berger Lab