IUPred2A: context-dependent prediction of protein disorder as a function of redox state and protein binding

By Bálint Mészáros, Gábor Erdös, Zsuzsanna Dosztányi. 2018

1,323 citations recorded by the source corpus.

Abstract The structural states of proteins include ordered globular domains as well as intrinsically disordered protein regions that exist as highly flexible conformational ensembles in isolation. Various computational tools have been developed to discriminate ordered and disordered segments based on the amino acid sequence. However, properties of IDRs can also depend on various conditions, including binding to globular protein partners or environmental factors, such as redox potential. These cases provide further challenges for the computational characterization of disordered segments. In this work we present IUPred2A, a combined web interface that allows to generate energy estimation based predictions for ordered and disordered residues by IUPred2 and for disordered binding regions by ANCHOR2. The updated web server retains the robustness of the original programs but offers several new features. While only minor bug fixes are implemented for IUPred, the next version of ANCHOR is significantly improved through a new architecture and parameters optimized on novel datasets. In addition, redox-sensitive regions can also be highlighted through a novel experimental feature. The web server offers graphical and text outputs, a RESTful interface, access to software download and extensive help, and can be accessed at a new location: http://iupred2a.elte.hu.

Subfields: Materials Chemistry, Molecular Biology, Spectroscopy, Computational Biology & Bioinformatics

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Citations in the corpus

SPOT-Disorder2: Improved Protein Intrinsic Disorder Prediction by Ensembled Deep Learning (2019, neutral): “edu/#webservers), we also used ANCHOR2 [60] (https://iupred2a.”
Sequence-based determinants and prediction of fuzzy interactions in protein complexes. (2020, methodology): “Therefore, it is not straightforward to compare its performance on disorder-to-order regions to other predictors (Anchor2 [52], MoRFchibi [25] and Dis-opred3 [26]), which aim to identify only such binding elements in disoredered proteins.”
Insights into SARS-CoV-2 genome, structure, evolution, pathogenesis and therapies: Structural genomics approach (2020, neutral): “We have predicted the intrinsically unstructured regions in SARS-CoV-2 polyprotein through multiple predictors such as PONDR® (Predictor of Natural Disordered Regions), VLXT, VL3, VLS2 [55] and IUPred2A web servers [56].”
IUPred3: prediction of protein disorder enhanced with unambiguous experimental annotation and visualization of evolutionary conservation (2021, background): “In addition, the same approach can also be used to highlight context dependent disordered regions,which can undergo a disorder-to-order transition as a result of binding (i.e. AN-CHOR) or changes in redox conditions (23).”
Analyzing Protein Disorder with IUPred2A (2020, methodology): “The actual region is calculated based on a heuristic segmentation approach (Mészáros et al., 2018).”
A structural biology community assessment of AlphaFold2 applications (2021, methodology): “22 ) and were filtered for proteins for which AF2-predicted structures are available in the Alpha-Fold database.”
InterPro: the protein sequence classification resource in 2025 (2024, neutral): “MobiDB-lite generates a consensus by integrating several predictors, such as DisEMBL ( 52 ), ESpritz ( 53 ) and IUPred ( 54 ).”
Mutation severity spectrum of rare alleles in the human genome is predictive of disease type (2019, result): “Similarly, results of three disorder prediction programs (DISOPRED3 [33], SPOT-Disorder [34], and IUPred2A [35]) consistently show that ordered regions are enriched and disorder regions are depleted in pathogenic SAVs.”