IUPred: web server for the prediction of intrinsically unstructured regions of proteins based on estimated energy content

Dosztányi, Zsuzsanna; Csizmók, Veronika; Tompa, Péter; Simon, István

doi:10.1093/bioinformatics/bti541

Cited by 1,870 publications

(1,885 citation statements)

References 12 publications

Supporting

Mentioning

1,835

Contrasting

Unclassified

Order By: Relevance

“…Intrinsic structural disorder of proteins was predicted using IUPRED, which predicts disorder on a per-residue basis [22,23]. The disorder score predicted by IUPRED was normalized by protein length to account for variations in different protein lengths when comparing predictions for various protein sets.…”

Section: Intrinsic Disorder Of Proteinsmentioning

confidence: 99%

“…RBPs being diverse structurally and functionally, are known to be highly disordered [43,44]. Intrinsic structural disorder of the RBPs was predicted using IUPRED, which predicts disorder on a per-residue basis [22,23] (Materials and Methods). The disorder score predicted by IUPRED was normalized by protein length to account for variations in different protein lengths when comparing predictions for entire dataset.…”

Section: Rbps Exhibit Significant Intrinsic Disorder and Are Enrichedmentioning

confidence: 99%

See 1 more Smart Citation

The human RBPome: From genes and proteins to human disease

Neelamraju

Hashemikhabir

Janga

2015

Journal of Proteomics

114

View full text Add to dashboard Cite

Section: Intrinsic Disorder Of Proteinsmentioning

confidence: 99%

Section: Rbps Exhibit Significant Intrinsic Disorder and Are Enrichedmentioning

confidence: 99%

The human RBPome: From genes and proteins to human disease

Neelamraju

Hashemikhabir

Janga

2015

Journal of Proteomics

114

View full text Add to dashboard Cite

“…22,28 The circular dichroism spectra are similar to those observed for coil-like natively unfolded polypeptides; 28 changes in circular dichroism as a function of temperature also resemble the response of intrinsically disordered proteins. 27 Analysis of the CTCF sequence with disorder prediction algorithms 29,30 even identified regions in the terminal domains as likely to be unstructured (data not shown). We cannot rule out the possible existence of isolated helices or strands, but these elements are neither abundant nor assemble into an ordered fold.…”

Section: Functional Implications Of Ctcf Molecular Architecturementioning

confidence: 99%

CTCF terminal segments are unstructured

Martinez

Miranda

2010

Protein Science

View full text Add to dashboard Cite

The human CCCTC-binding factor, CTCF, organizes and regulates transcription of the genome by colocalizing distant DNA elements on the same and even different chromosomes. This protein consists of 11 zinc fingers flanked by polypeptide segments of unknown structure and function. We purified recombinant terminal fragments and observed that both are extended, monomeric, and predominantly consist of unordered content. We thus speculate that the role of the terminal extensions, and perhaps all of CTCF, is to act as a scaffold for the assembly of other proteins on a specific binding site.

show abstract

“…For determining the structural disorder of a protein region we ran the IUPred algorithm over the full-length protein sequence to get a disorder score between 0 and 1 for each residue of a protein [43]. Finally, an average score for the last 5 residues (peptide sequence in our study) was obtained to determine putative candidate regions for interaction.…”

Section: Resultsmentioning

confidence: 99%

Cluster based prediction of PDZ-peptide interactions

Kundu

Backofen

2014

BMC Genomics

View full text Add to dashboard Cite

BackgroundPDZ domains are one of the most promiscuous protein recognition modules that bind with short linear peptides and play an important role in cellular signaling. Recently, few high-throughput techniques (e.g. protein microarray screen, phage display) have been applied to determine in-vitro binding specificity of PDZ domains. Currently, many computational methods are available to predict PDZ-peptide interactions but they often provide domain specific models and/or have a limited domain coverage.ResultsHere, we composed the largest set of PDZ domains derived from human, mouse, fly and worm proteomes and defined binding models for PDZ domain families to improve the domain coverage and prediction specificity. For that purpose, we first identified a novel set of 138 PDZ families, comprising of 548 PDZ domains from aforementioned organisms, based on efficient clustering according to their sequence identity. For 43 PDZ families, covering 226 PDZ domains with available interaction data, we built specialized models using a support vector machine approach. The advantage of family-wise models is that they can also be used to determine the binding specificity of a newly characterized PDZ domain with sufficient sequence identity to the known families. Since most current experimental approaches provide only positive data, we have to cope with the class imbalance problem. Thus, to enrich the negative class, we introduced a powerful semi-supervised technique to generate high confidence non-interaction data. We report competitive predictive performance with respect to state-of-the-art approaches.ConclusionsOur approach has several contributions. First, we show that domain coverage can be increased by applying accurate clustering technique. Second, we developed an approach based on a semi-supervised strategy to get high confidence negative data. Third, we allowed high order correlations between the amino acid positions in the binding peptides. Fourth, our method is general enough and will easily be applicable to other peptide recognition modules such as SH2 domains and finally, we performed a genome-wide prediction for 101 human and 102 mouse PDZ domains and uncovered novel interactions with biological relevance. We make all the predictive models and genome-wide predictions freely available to the scientific community.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2164-15-S1-S5) contains supplementary material, which is available to authorized users.

show abstract

IUPred: web server for the prediction of intrinsically unstructured regions of proteins based on estimated energy content

Cited by 1,870 publications

References 12 publications

The human RBPome: From genes and proteins to human disease

The human RBPome: From genes and proteins to human disease

CTCF terminal segments are unstructured

Cluster based prediction of PDZ-peptide interactions

Contact Info

Product

Resources

About