Natively unstructured regions in proteins identified from contact predictions

By Avner Schlessinger, Marco Punta, Burkhard Rost. 2007

125 citations recorded by the source corpus.

MOTIVATION Natively unstructured (also dubbed intrinsically disordered) regions in proteins lack a defined 3D structure under physiological conditions and often adopt regular structures under particular conditions. Proteins with such regions are overly abundant in eukaryotes, they may increase functional complexity of organisms and they usually evade structure determination in the unbound form. Low propensity for the formation of internal residue contacts has been previously used to predict natively unstructured regions. RESULTS We combined PROFcon predictions for protein-specific contacts with a generic pairwise potential to predict unstructured regions. This novel method, Ucon, outperformed the best available methods in predicting proteins with long unstructured regions. Furthermore, Ucon correctly identified cases missed by other methods. By computing the difference between predictions based on specific contacts (approach introduced here) and those based on generic potentials (realized in other methods), we might identify unstructured regions that are involved in protein-protein binding. We discussed one example to illustrate this ambitious aim. Overall, Ucon added quality and an orthogonal aspect that may help in the experimental study of unstructured regions in network hubs. AVAILABILITY http://www.predictprotein.org/submit_ucon.html. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Subfields: Molecular Biology

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Citations in the corpus

Modeling aspects of the language of life through transfer-learning protein sequences (2019, methodology): “The idea was applied to other tasks, including the prediction of transmembrane regions [11 – 13], solvent accessibility [14], residue flexibility (B-values) [15, 16], inter-residue contacts [17] and protein disorder [15, 18 – 20].”
Improved Disorder Prediction by Combination of Orthogonal Approaches (2009, methodology): “We used DISOPRED2 for several reasons: it was one of the best methods according to the CASP6 disorder assessment [49], it installed easily locally, and DISOPRED2 is quite orthogonal to our in-house methods [11,59].”
Toward an accurate prediction of inter-residue distances in proteins using 2D recursive neural networks (2014, background): “structures similarity [23-25], in the prediction of protein folding rates [26], protein disorder [27] and inter-domain”
Comprehensive review of methods for prediction of intrinsic disorder and its molecular functions (2017, background): “These inputs are divided into two types: 1) the outputs from four disorder predictors including NORSnet [76], IUPred [46], DISOPRED2 [71], and UCon [77]; and 2) other sequence-derived information including flexibility predicted with PROFbval [56], predicted secondary structure and solvent accessibil”
Prediction of Protein Binding Regions in Disordered Proteins (2009, background): “In a previous work, the ability to predict specific contacts was emphasized in order to recognize disordered regions that are involved in binding externally rather than internally [46].”
Improving protein disorder prediction by deep bidirectional long short‐term memory recurrent neural networks (2016, methodology): “…in many accurate predictors, such as DisEMBL (Linding et al. , 2003 a ), DISOPRED (Jones and Ward, 2003), DISpro (Cheng et al. , 2005), NORSnet (Schlessinger et al. , 2007 a ), the PONDR series (Romero et al. , 2001), PROFbval (Schlessinger et al. , 2006), RONN (Yang et al. , 2005), SPINE-D…”
AUC-Maximized Deep Convolutional Neural Fields for Protein Sequence Labeling (2016, background): “As suggested in [41], the predicted residue-residue contact information could further contribute to disorder prediction under the MRF model.”
Prediction of protein disorder based on IUPred (2018, unknown): “43 A novel approach to estimate position specific estimated energy (PSEE) of a residue using contact energy and predicted relative solvent accessibility was also incorporated into the DisPredict2 method.”
PONDR-FIT: A Meta-Predictor of Intrinsically Disordered Amino Acids (2010, methodology): “Several physics-based methods have also been developed, including the charge-hydropathy plot [3], IUPred [31], Ucon[32], and FoldIndex[33].”
MFDp2 (2013, background): “…chains can be divided into four categories: (1) approaches that utilize the relative propensity of amino acids to form disorder/ordered regions, 18-21 (2) methods that are based on classifiers generated with the help of machine learning algorithms, 22-37 (3) meta-methods that are based on a…”
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]”
Modeling the language of life – Deep Learning Protein Sequences (2019, methodology): “The idea was 21 applied to other tasks, including the prediction of transmembrane regions [11-13], solvent 22 accessibility [14], residue flexibility (B-values) [15, 16], inter-residue contacts [17] and protein 23 disorder [15, 18-20].”
Predict impact of single amino acid change upon protein structure (2012, methodology): “In addition, we used the following predicted structural and functional features: secondary structure [32,33] and solvent accessibility [24,25,32], protein flexibility [34], protein disorder [35-38], protein-protein interaction hotspots [39-41] and DNA-binding residues [42].”