Natively Unstructured Loops Differ from Other Loops

Schlessinger, Avner; Liu, Jinfeng; Rost, Burkhard

doi:10.1371/journal.pcbi.0030140

Cited by 89 publications

(75 citation statements)

References 82 publications

(148 reference statements)

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“…158 NORSnet was trained to distinguish between very long contiguous segments with non-regular secondary structure (NORS regions) and well-folded proteins. Since NORSnet was trained on predicted information rather than on experimental data, it was optimized on a large data set, thus overcoming the biases related to the small size of experimental data sets.…”

Section: Metapredictorsmentioning

confidence: 99%

How disordered is my protein and what is its disorder for? A guide through the “dark side” of the protein universe

Lieutaud

Ferrón

Uversky

et al. 2016

Intrinsically Disordered Proteins

104

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In the last 2 decades it has become increasingly evident that a large number of proteins are either fully or partially disordered. Intrinsically disordered proteins lack a stable 3D structure, are ubiquitous and fulfill essential biological functions. Their conformational heterogeneity is encoded in their amino acid sequences, thereby allowing intrinsically disordered proteins or regions to be recognized based on properties of these sequences. The identification of disordered regions facilitates the functional annotation of proteins and is instrumental for delineating boundaries of protein domains amenable to structural determination with X-ray crystallization. This article discusses a comprehensive selection of databases and methods currently employed to disseminate experimental and putative annotations of disorder, predict disorder and identify regions involved in induced folding. It also provides a set of detailed instructions that should be followed to perform computational analysis of disorder.KEYWORDS disorder databases and metaservers; induced folding; intrinsic disorder; intrinsically disordered proteins; intrinsically disordered regions; prediction methods

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Section: Metapredictorsmentioning

confidence: 99%

How disordered is my protein and what is its disorder for? A guide through the “dark side” of the protein universe

Lieutaud

Ferrón

Uversky

et al. 2016

Intrinsically Disordered Proteins

104

View full text Add to dashboard Cite

show abstract

“…These efforts intensified after the disorder prediction was introduced into the biannual CASP experiments in 2002. [14][15][16] The disorder predictors are categorized into four types: 17 1. propensity-based methods based on relative propensity of amino acids to form disorder/ordered regions: GlobPlot, 18 FoldIndex, 19 IUPred, 20 and Ucon; 21 2. machine learning-based predictors: DISOPRED2, 22 DISpro, 23 RONN, 24 ProfBval, 25,26 PONDR predictors, 9,14,[27][28][29][30] PreDisorder, 23,32 NORSnet, 21 DisEMBL, 18 and Spritz; 31 3. consensus-based methods that combine predictions from multiple base predictors: metaPrDOS, 33 GS-MetaServer, 34 MD, 35 PONDR-FIT, 36 and MFDp; 17 4. structural models-based approaches that make use of predicted tertiary structure models: PrDOS 37 and DISOCLUST. 38 The results from a recent comparative review 39 and the CASP8 competition, 16 demonstrate that the consensus-based methods, such as the GS-MetaServer, 34 MD, 35 and MFDp, 17 generally outperform other methods.…”

Section: Introductionmentioning

confidence: 99%

On the Complementarity of the Consensus-Based Disorder Prediction

Peng

Kurgan

2011

Biocomputing 2012

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Intrinsic disorder in proteins plays important roles in transcriptional regulation, translation, and cellular signal transduction. The experimental annotation of the disorder lags behind the rapidly accumulating number of known protein chains, which motivates the development of computational predictors of disorder. Some of these methods address predictions of certain types/flavors of the disorder and recent years show that consensusbased predictors provide a viable way to improve predictive performance. However, the selection of the base predictors in a given consensus is usually performed in an ad-hock manner, based on their availability and with a premise that more is better. We perform first-of-its-kind investigation that analyzes complementarity among a dozen recent predictors to identify characteristics of (future) predictors that would lead to further consensus-based improvements in the predictive quality. The complementarity of a given set of three base predictors is expressed by the differences in their predictions when compared with each other and with their majority vote consensus. We propose a regression-based model that quantifies/predicts quality of the majority-vote consensus of a given triplet of predictors based on their individual predictive performance and their complementarity measured at the residue and the disorder segment levels. Our model shows that improved performance is associated with higher (lower) similarity between the three base predictors at the residue (segment) level and to their consensus prediction at the segment (residue) level. We also show that better consensuses utilize higher quality base methods. We use our model to predict the best-performing consensus on an independent test dataset and our empirical evaluation shows that this consensus outperforms individual methods and other consensus-based predictors based on the area under the ROC curve measure. Our study provides insights that could lead to the development of a new generation of the consensus-based disorder predictors.

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“…X-ray crystallography can identify disordered residues with missing coordinates in structure and NMR can show disordered residues with highly variable coordinates within ensemble. The annotation of residues should be done in consistent way for better evaluation of a predictor's performance [20], [21]. We selected two datasets which combine sequences from PDB having disordered residues without coordinates (recorded in REMARK 465) and sequences from DisProt to separately train our predictor.…”

Section: A Data Sources and Collectionmentioning

confidence: 99%

Improved protein disorder predictor by smoothing output

Iqbal¹,

Islam²,

Hoque³

2014

2014 17th International Conference on Computer and Information Technology (ICCIT)

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Abstract-Intrinsically disorder regions (IDRs) or, proteins (IDPs) are associated with important biological functions, while lacking stable structure in their native state. The phenomena of disordered proteins or residues are abundant in nature and are extensively involved in critical human diseases and hence impacting drug discovery. Thus, the study using disorder prediction is becoming crucial in the proteomic research. The large scale growth of genome database demands high performance computational methods for identification of protein disorder. We developed a canonical support vector machine based disorder predictor, DisPredict by integrating RBF kernel. It employs novel feature set for accurate characterization of disorder which outperformed two leading predictors: the neural network based SPINE-D and Meta predictor MFDp based on ten-fold cross validation. We propose a post processing of probabilities to further improve the accuracy, named DisPredict1.1 which yields outstanding performance further both in binary annotation and real valued probability prediction per residue in both short and long disordered regions. It provides highest Mathews Correlation Coefficient (MCC), competitive Area Under receiver operating characteristic Curve (AUC) and lowest Mean Absolute Error (MAE) when compared with twenty existing predictors of several kinds on independent benchmark dataset. DisPredict is available online.

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Natively Unstructured Loops Differ from Other Loops

Cited by 89 publications

References 82 publications

How disordered is my protein and what is its disorder for? A guide through the “dark side” of the protein universe

How disordered is my protein and what is its disorder for? A guide through the “dark side” of the protein universe

On the Complementarity of the Consensus-Based Disorder Prediction

Improved protein disorder predictor by smoothing output

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