MetaDisorder: a meta-server for the prediction of intrinsic disorder in proteins

By Lukasz Pawel Kozlowski, Janusz M. Bujnicki. 2012

334 citations recorded by the source corpus.

Intrinsically unstructured proteins (IUPs) lack a well-defined three-dimensional structure. Some of them may assume a locally stable structure under specific conditions, e.g. upon interaction with another molecule, while others function in a permanently unstructured state. The discovery of IUPs challenged the traditional protein structure paradigm, which stated that a specific well-defined structure defines the function of the protein. As of December 2011, approximately 60 methods for computational prediction of protein disorder from sequence have been made publicly available. They are based on different approaches, such as utilizing evolutionary information, energy functions, and various statistical and machine learning methods. Given the diversity of existing intrinsic disorder prediction methods, we decided to test whether it is possible to combine them into a more accurate meta-prediction method. We developed a method based on arbitrarily chosen 13 disorder predictors, in which the final consensus was weighted by the accuracy of the methods. We have also developed a disorder predictor GSmetaDisorder3D that used no third-party disorder predictors, but alignments to known protein structures, reported by the protein fold-recognition methods, to infer the potentially structured and unstructured regions. Following the success of our disorder predictors in the CASP8 benchmark, we combined them into a meta-meta predictor called GSmetaDisorderMD, which was the top scoring method in the subsequent CASP9 benchmark. A series of disorder predictors described in this article is available as a MetaDisorder web server at http://iimcb.genesilico.pl/metadisorder/. Results are presented both in an easily interpretable, interactive mode and in a simple text format suitable for machine processing.

Subfields: Materials Chemistry, Molecular Biology

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Citations in the corpus

A comprehensive assessment of long intrinsic protein disorder from the DisProt database (2018, background): “These are: AUCpred (Wang et al. , 2016), Disopred3 (Jones and Cozzetto, 2015), ESpritz ‘slow’ (X-ray, NMR and DisProt) (Walsh et al. , 2012), Spine-D (Zhang et al. , 2017), S2D (Sormanni et al. , 2015) MFDp (Mizianty et al. , 2010), MFDp2 (Mizianty et al. , 2013) and MetaDisorder (Kozlowski and Bujn”
AUCpreD: proteome-level protein disorder prediction by AUC-maximized deep convolutional neural fields (2016, methodology): “Consensus methods combine individual predictors (Ishida and Kinoshita, 2007; Hirose et al., 2007; Kozlowski and Bujnicki, 2012; Xue et al., 2010), either sequencebased or template-based, to yield better accuracy.”
DISOPRED3: precise disordered region predictions with annotated protein-binding activity (2014, methodology): “Finally, meta-pre-dictors integrate the output of independent tools through an array of algorithms (Ishida and Kinoshita 2008; Kozlowski and Bujnicki 2012; Schlessinger et al. , 2009).”
Disorder Prediction Methods, Their Applicability to Different Protein Targets and Their Usefulness for Guiding Experimental Studies (2015, background): “Today, over 60 protein disorder prediction servers exist, although not all are publicly available [32].”
Structural and biophysical properties of FopA, a major outer membrane protein of Francisella tularensis (2022, methodology): “(C) Prediction of disordered regions in FopA by two meta disorder prediction programs PONDR-FIT (63) and MetaDisorder (60).”
DeepCNF-D: Predicting Protein Order/Disorder Regions by Weighted Deep Convolutional Neural Fields (2015, methodology): “Examples include PrDOS [10], PONDR [11], POODLE [12], and MetaDisorder [13].”
D2P2: database of disordered protein predictions (2012, background): “Resources such as MetaDisorder (17) go some way to resolve this issue providing the naı̈ve user with reliable results from a similar spectrum of predictors as D2P2.”
Comprehensive review of methods for prediction of intrinsic disorder and its molecular functions (2017, methodology): “Examples meta-methods include CSpritz [62], MetaDisorder [63], MFDp [64], DisMeta [65], and MFDp2 [66].”