PONDR-FIT: A Meta-Predictor of Intrinsically Disordered Amino Acids

By Bin Xue, Roland L. Dunbrack, Robert W. Williams, A. Keith Dunker, Vladimir N Uversky. Biochimica et Biophysica Acta, 2010

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Protein intrinsic disorder is becoming increasingly recognized in proteomics research. While lacking structure, many regions of disorder have been associated with biological function. There are many different experimental methods for characterizing intrinsically disordered proteins and regions; nevertheless, the prediction of intrinsic disorder from amino acid sequence remains a useful strategy especially for many large-scale proteomics investigations. Here we introduced a consensus artificial neural network (ANN) prediction method, which was developed by combining the outputs of several individual disorder predictors. By eight-fold cross-validation, this meta-predictor, called PONDR-FIT, was found to improve the prediction accuracy over a range of 3 to 20% with an average of 11% compared to the single predictors, depending on the datasets being used. Analysis of the errors shows that the worst accuracy still occurs for short disordered regions with less than ten residues, as well as for the residues close to order/disorder boundaries. Increased understanding of the underlying mechanism by which such meta-predictors give improved predictions will likely promote the further development of protein disorder predictors. The access to PONDR-FIT is available at www.disprot.org.

Subfields: Materials Chemistry, Molecular Biology, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/8dc055f6ff3b89803f2c5ea434cfebb43e95c6ab

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Citations in the corpus

How Common Is Disorder? Occurrence of Disordered Residues in Four Domains of Life (2015, result): “It is important that the results of our method (IsUnstruct) are better than the results of the meta-predictor PONDR-FIT [24] and are comparable to the results of the neural-network-based technique CSpritz [25] (see Table 3).”
The s2D method: simultaneous sequence-based prediction of the statistical populations of ordered and disordered regions in proteins. (2015, methodology): “(a) Results obtained using three different disorder predictors: PONDR-FITTM [25] (magenta), DISOPRED 3 [18,73] (lilac) and DisEMBLTM [19] (purple).”
Comprehensive comparative assessment of in-silico predictors of disordered regions. (2012, methodology): “Our assessment considers sixteen disorder predictors that cover the fours types of the methods, including four relative propensity-based methods including GlobPlot [[20]], IUPred [[21]], FoldIndex [[22]], and Ucon [[23]]; eight machine learning-based solutions such as DisEMBL [[25]], DISOPRED2 [[26]”
MetaDisorder: a meta-server for the prediction of intrinsic disorder in proteins (2012, methodology): “This approach has been used to develop many successful prediction meth-ods, e.g. in protein fold recognition [12], protein function prediction [13], prediction of protein domains [14], prediction of protein model quality [15], and recently also in protein disorder prediction [16-18].”
Intrinsically Disordered Proteins Studied by NMR Spectroscopy (2015, background): “1021/bi8006803 Garner E, Romero P, Dunker A et al (1999) Predicting binding regions within disordered proteins. Genome Inform Ser Workshop Genome Inform 10:41–50 Gierasch LM, King J (1990) Protein folding: deciphering the second half of the genetic code. American Association for the Advancement of S”
Comprehensive review of methods for prediction of intrinsic disorder and its molecular functions (2017, methodology): “URL : http://biomine.cs.vcu.edu/servers/MFDp PONDR-FIT (2010) PONDR-FIT [83] is a meta predictor that was authored by Xue et al. at the Indiana University.”
The disordered P granule protein LAF-1 drives phase separation into droplets with tunable viscosity and dynamics (2015, background): “Both the N and C termini of LAF-1 are predicted to be highly disordered, using the predictor of naturally disordered regions (PONDR) algorithm (31) (Fig.”
Orderly order in protein intrinsic disorder distribution: disorder in 3500 proteomes from viruses and the three domains of life (2012, background): “Up to now, the members of PONDR® VSL2 series are still among the most accurate predictors of intrinsic disorder (Mizianty et al, 2011 ; Xue, Dunbrack, Williams, Dunker, & Uversky, 2010 ).”
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): “This predictor is so far the most accurate predictor in the PONDR family (25).”