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Struct2Net specifications


Unique identifier OMICS_02986
Name Struct2Net
Interface Web user interface, Application programming interface
Restrictions to use None
Input data One or two amino acid sequences in FASTA format.
Input format FASTA
Output data The output gives a list of interactors if one sequence is provided and an interaction prediction if two sequences are provided.
Computer skills Basic
Stability Stable
Maintained Yes

Publications for Struct2Net

Struct2Net citations


Determining the minimum number of protein protein interactions required to support known protein complexes

PLoS One
PMCID: 5919440
PMID: 29698482
DOI: 10.1371/journal.pone.0195545

[…] To investigate the accuracy of GreedyMinPPI, we compared our results with those from existing PPI prediction methods using four different weighted PPI datasets predicted from Struct2Net [], ENTS [], PIP [] and iWRAP []. Struct2Net is a web server for predicting PPIs based on the structural features using protein sequence data as input data. ENTS is a random forest based PP […]


Prediction of cassava protein interactome based on interolog method

Sci Rep
PMCID: 5722940
PMID: 29222529
DOI: 10.1038/s41598-017-17633-2

[…] h always lack data, are quite challenging. To closely investigate the interaction of a protein set, computational methods that include information on protein structure into prediction regime, such as Struct2Net and physical docking are proposed.In plants, earlier studies of PPI were limited to only a few species. The current PPI information of plants, especially cassava, has constrained choices of […]


Non interacting proteins may resemble interacting proteins: prevalence and implications

Sci Rep
PMCID: 5289270
PMID: 28084410
DOI: 10.1038/srep40419

[…] mate of the fraction of negative pairs with structural precedent that are potential in vitro interactors. To do so, we used two different approaches. One is based on the prediction of interactions by Struct2Net and the other one is based on the presence of interologues in the public interaction databases.The prediction of interactions provided by Struct2Net relies on interfacial energy and alignme […]


Tyrosine Kinase Ligand Receptor Pair Prediction by Using Support Vector Machine

Adv Bioinformatics
PMCID: 4548105
PMID: 26347773
DOI: 10.1155/2015/528097

[…] %, recall = 57.9%, and AUC = 0.858. These results show the practical utility of the composite vectors method.We also applied our test dataset to a general protein-protein interaction prediction tool, Struct2Net [], which performs structure-based computational prediction of protein-protein interactions. It successfully predicted 7 of the 95 positive pairs and wrongly predicted 57 of the 2183 negati […]


Drug Like Protein–Protein Interaction Modulators: Challenges and Opportunities for Drug Discovery and Chemical Biology

Mol Inform
PMCID: 4160817
PMID: 25254076
DOI: 10.1002/minf.201400040

[…] ein complexes known experimentally. A list of in silico methods has been recently reported by the literature[,] and include for instance TACOS (Template-based Assembly of Complex Structures)[] or the Struct2Net server.[] […]


PPIcons: identification of protein protein interaction sites in selected organisms

J Mol Model
PMCID: 3744667
PMID: 23729008
DOI: 10.1007/s00894-013-1886-9

[…] 6 % precision over 1494 protein-protein interfaces, of which 518 were homodimers, 114 were heterodimers and 862 were multimers. Singh et al. [] obtained 60 % sensitivity and 75 % specificity in their Struct2Net web server.In comparison, our results are prepared using 196 hetero-complexes (40 for E. coli, 123 for Yeast, 33 for Homo sapiens) and obtained up to 81.46 % AUC, 73.68 % sensitivity (or re […]


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Struct2Net institution(s)
Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA; Computational and Systems Biology Program, Massachusetts Institute of Technology, Cambridge, MA, USA; Toyota Technological Institute at Chicago, Chicago, IL, USA; Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA, USA

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