Intrinsically Disordered Protein: Difference between revisions
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IDPs play roles in '''processes''' such as: | IDPs play roles in '''processes''' such as: | ||
* Intrinsically disordered regions play a decisive role in ATP-mediated opening of bacterial potassium (K+) channels KtrAB<ref>PMID: 40335548</ref>. | |||
* Cell signaling and cell cycle regulation, e.g. cyclin dependent kinase inhibitor p21Waf1/Cip1/Sdi1<ref>PMID: 8876165</ref> | * Cell signaling and cell cycle regulation, e.g. cyclin dependent kinase inhibitor p21Waf1/Cip1/Sdi1<ref>PMID: 8876165</ref> | ||
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=== Prediction Servers === | === Prediction Servers === | ||
The sequence display for protein structure entries at [https://rcsb.org RCSB.org] includes a [http://firstglance.jmol.org/disopred.htm line graphically representing disorder predicted by IUPred] (see below). Enter the [[PDB ID]] into the search slot at the top of the page at [https://rcsb.org RCSB.org], then click on the ''Sequence'' tab. | |||
The quality of predictions by various algorithms have been evaluated beginning in CASP5 (2002). The assessment of disorder predictions for CASP8 (2008) has been published<ref>PMID: 19774619</ref>. | The quality of predictions by various algorithms have been evaluated beginning in CASP5 (2002). The assessment of disorder predictions for CASP8 (2008) has been published<ref>PMID: 19774619</ref>. | ||
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Meta-servers gather the predictions from other servers into a single report. | Meta-servers gather the predictions from other servers into a single report. | ||
* [http://mobidb.bio.unipd.it/ MobiDB]. The ''Structure'' section in entries at [http://uniprot.org UniProt.Org] offers MobiDB. MobiDB | * [http://biomine.cs.vcu.edu/servers/DEPICTER2/ DEPICTER2] (2023) predicts disorder and the '''functions''' of disordered regions<ref>PMID: 37140058</ref> <ref>PMID: 40728619</ref>. | ||
* [http://mobidb.bio.unipd.it/ MobiDB]. <!--The ''Structure'' section in entries at [http://uniprot.org UniProt.Org] offers MobiDB.--> MobiDB includes manually curated disorder data along with derived and predicted data. | |||
* [http://d2p2.pro/ D<sup>2</sup>P<sup>2</sup>]: "pre-computed disorder predictions on a large library of proteins from completely-sequenced genomes. ... statistical comparisons of the various prediction methods ...." | * [http://d2p2.pro/ D<sup>2</sup>P<sup>2</sup>]: "pre-computed disorder predictions on a large library of proteins from completely-sequenced genomes. ... statistical comparisons of the various prediction methods ...." | ||
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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.) | ||
