DTU Health Tech

Department of Health Technology

VirtualPulldown - 1.1b

Create protein physical interaction networks using the InWeb 3.0/5.0 inferred human interactome

VirtualPulldown takes a list of HUMAN proteins (or the corresponding genes) and searches the InWeb 3.0/5.0 inferred human interactome for other proteins physically interacting with the input set.

The proteins in the input set thus functions as "bait" in a virtual pulldown / co-purification experiment.

4 node network

Paste in protein (or corresponding gene) IDs

Upload file containing identifiers

View example Protein IDs


InWeb version

Physical interaction confidence score threshold

Topology based scoring

  • [Type 0] Edges to input / Edges to total
  • [Type 1] Edges to 1st order / Edges to total

Interaction type(s) to use

Valid IDs: UniProt (P01584, IL1B_HUMAN), RefSeq (NP_000567), Ensembl (ENSG00000125538) - for full list see Instructions

Instructions: text text text


For publication of results, please cite:

  • METHOD (virtual pulldown concept): Lage et al Nature Biotechnology 25, 309 - 316 (2007) ; Lage et al PNAS, 52, 20870-20875 (2008)
  • DATA (InWeb 3.0 inferred human interactome): Lage et al Molecular Systems Biology 6: 381 (2010)

View the Abstract page for more details.

Terms of use

As per the licenses from the underlying databases, which can be summarized as: Free for academic use - not available for commercial use at this point.

Usage instructions

Quick start

Paste in or upload a list of identifiers for HUMAN proteins (e.g UniProt IDs) - gene IDs for protein coding genes will work as well (e.g. ENSEMBL IDs). Hit "Submit query" and wait a few minutes for the server to work it's magic.

The VirtualPulldown server works by searching the InWeb database for proteins that PHYSICAL interacts with the input proteins (hence the "pulldown" metaphor). Proteins fulfilling the quality filtering criteria (by default rather stringent) will be included in the final network. Summary statistics will be shown and network(s) and meta-data will be offered as an easy download for further analysis (e.g. Using Cytoscape)

Detailed instructions

Protein identifiers supported

IMPORTANT: the input list MUST contain only HUMAN proteins

Gene identifiers supported

IMPORTANT: the input list MUST contain only HUMAN genes

Submit query

Click on the "Submit query" button. If the processing of the query takes more than a few seconds you'll will get the option of supplying your email address and be notified when the job is done.

Advanced options

The VirtualPulldown server has support for a number of advanced options. Typically it is not necessary to set these manually and most users can safely skip this section and proceed to submitting the query.

  • Protein-Protein interaction confidence cut-off
    By default the most optimal cut-off found during the benchmark process of InWeb will be selected. It's possible to completely disable this filter by setting the value to "0.0".

  • Option XXX:
    text text text.

  • Option YYY:
    text text text.

Example data

Sample protein list

Sample list of 20 human protein involved in apoptosis:

Sample gene list

Sample list of 25 human genes involve in the G2/M transition in cell division (data from cyclebase.org).

Output format

The networks generated by the VirtualPulldown server (via the use of InWeb) is a number of protein-protein interaction networks and meta-data files.

"Easy to use" selected network

By default the most stringently filtered network is selected and combined with a following meta-data to a SINGLE xgmml file for easy use in Cytoscape (the file include some basic visual styles as well).

(TODO: "Big" Figure showing this)

Meta-data included:

  • Node: UniProt ID
  • Node: UniProt description
  • Node: Ensembl ID
  • Node: Protein on input list: yes/no + visual style
  • Edge: Confidence score (0.0 - 1.0) + visual style

(TODO: "Small" Figure showing this)

VirtualPulldown - Background

What is a "virtual pulldown"?

text text text....

InWeb: a high quality inferred human interactome?

text text text ... physical interactions... explain how the orthology transfer works.... Mosaic figure .... Benchmark...

Virtual Pulldown and InWeb related article abstracts


METHOD (Virtual Pulldown concept)

Kasper Lage, E Olof Karlberg, Zenia M Størling, Pall I Olason, Anders G Pedersen, Olga Rigina, Anders M Hinsby, Zeynep Tümer, Flemming Pociot, Niels Tommerup, Yves Moreau and Søren Brunak
A human phenome-interactome network of protein complexes implicated in genetic disorders
Nature Biotechnology 25, 309 - 316 (2007)

Kasper Lage, Niclas Tue Hansen, E. Olof Karlberg, Aron C. Eklund, Francisco S. Roque, Patricia K. Donahoe, Zoltan Szallasi, Thomas Skøt Jensen, Søren Brunak
A large-scale analysis of tissue-specific pathology and gene expression of human disease genes and complexes
PNAS, 52, 20870-20875 (2008)

DATA ("InWeb 2.9/3.0" inferred human interactome)

Kasper Lage, Kjeld Møllgård, Steven Greenway, Hiroko Wakimoto, Joshua M Gorham, Christopher T Workman, Eske Bendsen, Niclas T Hansen, Olga Rigina, Francisco S Roque, Cornelia Wiese, Vincent M Christoffels, Amy E Roberts, Leslie B Smoot, William T Pu, Patricia K Donahoe, Niels Tommerup, Søren Brunak, Christine E Seidman, Jonathan G Seidman, and Lars A Larsen
Dissecting spatio-temporal protein networks driving human heart development and related disorders
Molecular Systems Biology 6: 381 (2010)


Lage et al, 2007

We performed a systematic, large-scale analysis of human protein complexes comprising gene products implicated in many different categories of human disease to create a phenome-interactome network. This was done by integrating quality-controlled interactions of human proteins with a validated, computationally derived phenotype similarity score, permitting identification of previously unknown complexes likely to be associated with disease. Using a phenomic ranking of protein complexes linked to human disease, we developed a Bayesian predictor that in 298 of 669 linkage intervals correctly ranks the known disease-causing protein as the top candidate, and in 870 intervals with no identified disease-causing gene, provides novel candidates implicated in disorders such as retinitis pigmentosa, epithelial ovarian cancer, inflammatory bowel disease, amyotrophic lateral sclerosis, Alzheimer disease, type 2 diabetes and coronary heart disease. Our publicly available draft of protein complexes associated with pathology comprises 506 complexes, which reveal functional relationships between disease-promoting genes that will inform future experimentation.

Lage et al, 2008

Heritable diseases are caused by germ-line mutations that, despite tissuewide presence, often lead to tissue-specific pathology. Here, we make a systematic analysis of the link between tissue-specific gene expression and pathological manifestations in many human diseases and cancers. Diseases were systematically mapped to tissues they affect from disease-relevant literature in PubMed to create a disease-tissue covariation matrix of high-confidence associations of >1,000 diseases to 73 tissues. By retrieving >2,000 known disease genes, and generating 1,500 disease-associated protein complexes, we analyzed the differential expression of a gene or complex involved in a particular disease in the tissues affected by the disease, compared with nonaffected tissues. When this analysis is scaled to all diseases in our dataset, there is a significant tendency for disease genes and complexes to be overexpressed in the normal tissues where defects cause pathology. In contrast, cancer genes and complexes were not overexpressed in the tissues from which the tumors emanate. We specifically identified a complex involved in XY sex reversal that is testis-specific and down-regulated in ovaries. We also identified complexes in Parkinson disease, cardiomyopathies, and muscular dystrophy syndromes that are similarly tissue specific. Our method represents a conceptual scaffold for organism-spanning analyses and reveals an extensive list of tissue-specific draft molecular pathways, both known and unexpected, that might be disrupted in disease.

Lage et al, 2010

Aberrant organ development is associated with a wide spectrum of disorders, from schizophrenia to congenital heart disease, but systems-level insight into the underlying processes is very limited. Using heart morphogenesis as general model for dissecting the functional architecture of organ development, we combined detailed phenotype information from deleterious mutations in 255 genes with high-confidence experimental interactome data, and coupled the results to thorough experimental validation. Hereby, we made the first systematic analysis of spatio-temporal protein networks driving many stages of a developing organ identifying several novel signaling modules. Our results show that organ development relies on surprisingly few, extensively recycled, protein modules that integrate into complex higher-order networks. This design allows the formation of a complicated organ using simple building blocks, and suggests how mutations in the same genes can lead to diverse phenotypes. We observe a striking temporal correlation between organ complexity and the number of discrete functional modules coordinating morphogenesis. Our analysis elucidates the organization and composition of spatio-temporal protein networks that drive the formation of organs, which in the future may lay the foundation of novel approaches in treatments, diagnostics, and regenerative medicine.


If you need help regarding technical issues (e.g. errors or missing results) contact Technical Support. Please include the name of the service and version (e.g. NetPhos-4.0) and the options you have selected. If the error occurs after the job has started running, please include the JOB ID (the long code that you see while the job is running).

If you have scientific questions (e.g. how the method works or how to interpret results), contact Correspondence.

Correspondence: Technical Support: