Intrinsically Disordered Protein: Difference between revisions
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* [http://bioinf.cs.ucl.ac.uk/disopred/ DISOPRED2] (Jones Group, University College London, UK). "DISOPRED2 was trained on a set of around 750 non-redundant sequences with high resolution X-ray structures. Disorder was identified with those residues that appear in the sequence records but with coordinates missing from the electron density map. This is an imperfect means for identifying disordered residues as missing co-ordinates can also arise as an artifact of the crystalization process. False assignment of order can also occur as a result of stabilizing interactions by ligands or other macromolecules in the complex. However, this is the simplest means for defining disorder in the absence of further experimental investigation of the protein." (Quoted from the DISOPRED2 website.) | * [http://bioinf.cs.ucl.ac.uk/disopred/ DISOPRED2] (Jones Group, University College London, UK). "DISOPRED2 was trained on a set of around 750 non-redundant sequences with high resolution X-ray structures. Disorder was identified with those residues that appear in the sequence records but with coordinates missing from the electron density map. This is an imperfect means for identifying disordered residues as missing co-ordinates can also arise as an artifact of the crystalization process. False assignment of order can also occur as a result of stabilizing interactions by ligands or other macromolecules in the complex. However, this is the simplest means for defining disorder in the absence of further experimental investigation of the protein." (Quoted from the DISOPRED2 website.) | ||
* [http://biomine.cs.vcu.edu/servers/ | * [http://biomine.cs.vcu.edu/servers/flDPnn2/ flDPnn2] (putative '''f'''unction- and '''l'''inker based '''D'''isorder '''P'''rediction using deep '''n'''eural '''n'''etwork)<ref name="fldpnn">PMID: 34290238</ref>. In 2021, flDPnn, was selected as the {{font color|#c000c0|'''best disorder predictor in the first Critical Assessment of Protein Intrinsic Disorder Prediction'''}} (CAID) <ref name="caid2021">PMID: 33875885</ref>. | ||
* [https://fold.proteopedia.org/ FoldIndex]<ref name="foldindex" /> (Sussman Group, Weizmann Institute, Rehovot, Israel). FoldIndex makes predictions based on the observation that IDPs occupy the low hydrophobicity/ high net-charge portion of charge-hydrophobicity phase space. (See Figure above.) | * [https://fold.proteopedia.org/ FoldIndex]<ref name="foldindex" /> (Sussman Group, Weizmann Institute, Rehovot, Israel). FoldIndex makes predictions based on the observation that IDPs occupy the low hydrophobicity/ high net-charge portion of charge-hydrophobicity phase space. (See Figure above.) | ||
* [ | * [https://iupred2a.elte.hu/ IUPred2a] (Dosztányi, Csizmók, Tompa and Simon: Budapest, Hungary). "IUPred recognized intrinsically unstructured regions from the amino acid sequence based on the estimated pairwise energy content. The underlying assumption is that globular proteins are composed of amino acids which have the potential to form a large number of favorable interactions, whereas intrinsically disorered proteins (IDPs) adopt no stable structure because their amino acid composition does not allow sufficient favorable interactions to form." (Quoted from the IUPred website.) | ||
* [http://www.pondr.com/ PONDR] (Dunker Group, Indiana University and Molecular Kinetics, Inc., Indianapolis IN USA; Obradovic Group, Temple Univ., Philadelphia PA USA). "PONDR® functions from primary sequence data alone. The predictors are feedforward neural networks that use sequence information from windows of generally 21 amino acids. Attributes, such as the fractional composition of particular amino acids or hydropathy, are calculated over this window, and these values are used as inputs for the predictor. The neural network, which has been trained on a specific set of ordered and disordered sequences, then outputs a value for the central amino acid in the window. The predictions are then smoothed over a sliding window of 9 amino acids. If a residue value exceeds a threshold of 0.5 (the threshold used for training) the residue is considered disordered." (Quoted from the PONDR website.) | * [http://www.pondr.com/ PONDR] (Dunker Group, Indiana University and Molecular Kinetics, Inc., Indianapolis IN USA; Obradovic Group, Temple Univ., Philadelphia PA USA). "PONDR® functions from primary sequence data alone. The predictors are feedforward neural networks that use sequence information from windows of generally 21 amino acids. Attributes, such as the fractional composition of particular amino acids or hydropathy, are calculated over this window, and these values are used as inputs for the predictor. The neural network, which has been trained on a specific set of ordered and disordered sequences, then outputs a value for the central amino acid in the window. The predictions are then smoothed over a sliding window of 9 amino acids. If a residue value exceeds a threshold of 0.5 (the threshold used for training) the residue is considered disordered." (Quoted from the PONDR website.) | ||