ESpritz: accurate and fast prediction of protein disorder

By Ian Walsh, Alberto J. M. Martin, Tomás Di Domenico, Silvio C. E. Tosatto. 2012

502 citations recorded by the source corpus.

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In Molecular Biology

Citations in the corpus

Critical assessment of protein intrinsic disorder prediction (CAID) ‐ Results of round 2 (2023, methodology): “For example, ESpritz 22 achieves an AUC that is approximately 3% lower than the top-performing method but requires significantly less computation time.”
SPOT-Disorder2: Improved Protein Intrinsic Disorder Prediction by Ensembled Deep Learning (2019, methodology): “The annotations in Mobi9414 ( i.e. , from MobiDB) contain direct labels from the DisProt database, inferred labels from the PDB, and predicted labels from a large ensemble of disorder predictors such as ESpritz [17].”
A Deep Learning Network Approach to ab initio Protein Secondary Structure Prediction (2014, background): “They have been used in other areas of protein prediction [27], [28], [29], but not secondary structure prediction, so we utilize this novel feature to investigate its potential to contribute to this field.”
InterPro in 2017—beyond protein family and domain annotations (2016, methodology): “Eight different algorithms (IUPredshort, IUPred-long (44), GlobPlot (45), DisEMBL-465, DisEMBL-HL (46), Espritz-DisProt, Espritz-NMR and Espritz-X-ray (47)) were chosen for their speed and orthogonality of approaches.”
Disorder Prediction Methods, Their Applicability to Different Protein Targets and Their Usefulness for Guiding Experimental Studies (2015, unknown): “it/ 10 servers; Espritz (all 3 flavours) [38], IUPred (2 flavours) [39], DisEMBL (2 flavours) [18], GlobPlot [28], VSL2B [31], JRONN [40].”
Phosphorylation of innate immune adaptor proteins MAVS, STING, and TRIF induces IRF3 activation (2015, methodology): “disordered regions using the EspritzWeb server (41) with parameters trained on disordered proteins in the Disprot database (42).”
The Role of Balanced Training and Testing Data Sets for Binary Classifiers in Bioinformatics (2013, methodology): “We calculated predicted disorder regions for the proteins in each of the data sets using the programs IUpred [10], Espritz [65], and VSL2 [66].”
Sequence-based determinants and prediction of fuzzy interactions in protein complexes. (2020, methodology): “The Espritz NMR method [57] with threshold ¼ 0.3089 resulted in the best agreement with the experimentally validated DDRs in FuzDB TOT (Fig.”
InterPro: the protein sequence classification resource in 2025 (2024, unknown): “MobiDB-lite generates a consensus by integrating several predictors, such as DisEMBL ( 52 ), ESpritz ( 53 ) and IUPred ( 54 ).”
Improving protein disorder prediction by deep bidirectional long short‐term memory recurrent neural networks (2016, unknown): “Examples are CSpritz (Walsh et al. , 2011), MD (Schlessinger et al. , 2009), metaPrDOS (Ishida and Kinoshita, 2008), MFDp2 (Mizianty et al. , 2013) and PONDR-FIT (Xue et al. , 2010).”
MoRFchibi SYSTEM: software tools for the identification of MoRFs in protein sequences (2016, methodology): “MoRFCHiBi Light utilizes Bayes rule to incorporate MoRFCHiBi scores with disorder scores generated by ESpritz (19).”
Comprehensive large-scale assessment of intrinsic protein disorder (2015, background): “Others attempting to move away from this disorder style measure some form of protein backbone flexibility.”
Genome‐scale prediction of proteins with long intrinsically disordered regions (2014, background): “A few fast predictors, such as ESpritz,28 IUPred,29,30 and VSL2B,31 are available but their predictive quality and computational costs require further improvements, which we demonstrate in our empirical analysis.”