1 VLXT PONDR® VL-XT integrates three feedforward neural networks: the VL1 predictor (Romero 2001), the N-terminus predictor (XN), and the C-terminus predictor (XC) (both from Li et al. 1999). VL1 was trained using 8 long disordered regions identified from missing electron density in X-ray crystallographic studies, and 7 long disordered regions characterised by NMR. The XN and XC predictors, together called XT, were also trained using X-ray crystallographic data, where the terminal disordered regions were 5 or more amino acids in length. NULL Romero,P., Obradovic,Z., Li,X., Garner,E.C., Brown,C.J., Dunker,A.K. (2001) "Sequence complexity of disordered protein." Proteins, 42, 38-48.
Li,X., Romero,P., Rani,M., Dunker,A.K., Obradovic,Z. (1999) "Predicting Protein Disorder for N-, C-, and Internal Regions." Genome Inform. Ser. Workshop Genome Inform., 10, 30-40. http://www.pondr.com/pondr-tut2.html 42511B 0 0 disorder 2012-06-05 00:00:00 2012-10-08 08:18:50 1 1 9 Espritz-X Espritz predicts three variants of disorder using bi-directional recursive neural networks. Espritz-X was trained on PDB X-ray crystallography of short disorder. The method can be run with a fast or slower variant (requiring PSI-BLAST) of the algorithm (Walsh 2012). Due to the wide genomic scale of D²P² the fast variant was used. Additionally the following cut-offs were used to yield 5% false positive rate 0.1434 for this Espritz variant. Walsh,I., Martin,A.J., Di Domenico,T., and Tosatto, S.C. (2012) ESpritz: accurate and fast prediction of protein disorder. Bioinformatics., 28(4), 503-509. http://protein.bio.unipd.it/espritz/ 20E3C2 1 0 disorder 0000-00-00 00:00:00 2012-11-25 20:42:29 8 1 2 VSL2b PONDR® VSL2 predictor is a combination of neural network predictors for both short and long disordered regions (Peng 2006). A length limit of 30 residues divides short and long disordered regions. Each individual predictor is trained by the dataset containing sequences of that specific length. The final prediction is a weighted average determined by a second layer predictor (Peng et al. 2006). PONDR® VSL2 applies not only the sequence profile, but also the result of sequence alignments from PSI-BLAST and secondary structure prediction from PHD and PSIPRED. This predictor is so far the most accurate predictor in the PONDR® family. NULL Peng,K., Radivojac,P., Vucetic,S., Dunker,A.K. and Obradovic,Z. (2006) "Length-dependent prediction of protein intrinsic disorder." BMC Bioinformatics 7(1), 208. http://www.pondr.com/pondr-tut2.html 9E3797 0 0 disorder 2012-06-05 00:00:00 2012-10-07 20:16:53 2 1 3 PrDOS PrDOS is composed of two predictors. The first predictor is implemented using a support vector machine with a position-specific profile of local amino acid sequence. A similar concept to how PSIPRED predicts local secondary structure features. The second predictor assumes the conservation of intrinsic disorder in homologous protein domain families, and is implemented using PSI-BLAST and a novel measure of disorder (Ishida and Kinoshita 2007). The final prediction is taken as the combination of the results of the two predictors. Ishida,T. and Kinoshita,K. (2007) "PrDOS: prediction of disordered protein regions from amino acid sequence.", Nucleic Acids Res., 35, Web Server issue http://prdos.hgc.jp 2D3661 1 0 disorder 2012-06-05 00:00:00 2012-10-07 20:26:02 3 1 4 IUPred-A IUPred assumes that the core of a well-structured globular protein has amino acids that can make enough favourable contacts to form a stable 3D structure. A matrix of amino acid pairs holds estimates of their pairwise interaction energies which is then used with a position specific scoring method to predict when stretches of amino acids are not contributing to a stable structure. The underlying assumption is that globular proteins are composed of amino acids which have the potential to form a large number of favourable interactions, whereas intrinsically unstructured proteins (IUPs) adopt no stable structure because their amino acid composition does not allow sufficient favourable interactions to form. (Dosztányi 2005). The A variant of IUPred uses scores specifically trained for detection of ANCHOR binding regions. Bálint Mészáros, István Simon and Zsuzsanna Dosztányi "Prediction of Protein Binding Regions in Disordered Proteins." PLoS Comput. Biol. (2009) 5(5): e1000376. http://iupred.enzim.hu 690013 1 1 disorder 2012-06-05 00:00:00 2012-11-23 11:21:21 6 0 5 ANCHOR Many disordered proteins function via binding to a structured partner and undergo a disorder-to-order transition. In order to predict disordered binding regions, ANCHOR seeks to identify segments that reside in disordered regions, cannot form enough favorable intrachain interactions to fold on their own and are likely to gain stabilizing energy by interacting with a globular protein partner. The approach relies on the pairwise energy estimation approach that is the basis for IUPred, a general disorder prediction method. Bálint Mészáros, István Simon and Zsuzsanna Dosztányi "Prediction of Protein Binding Regions in Disordered Proteins." PLoS Comput. Biol. (2009) 5(5): e1000376. http://anchor.enzim.hu FF9284 1 1 binding 2012-06-05 00:00:00 2012-10-07 19:38:41 0 1 0 SUPERFAMILY Library of HMMs for each SCOP domain, release 1.75. NULL Gough, J., Karplus, K., Hughey, R. and Chothia, C. (2001)"Assignment of Homology to Genome Sequences using a Library of Hidden Markov Models that Represent all Proteins of Known Structure." J. Mol. Biol., 313(4), 903-919. http://supfam.org 0 0 structure 0000-00-00 00:00:00 2012-10-07 20:45:04 0 1 6 PV2 PV2 is a meta-predictor that was built upon five prediction methodologies trained on different disordered protein datasets: logistic regression, a neural network, a support vector machine, a conditional random field, and finally PONDR® VSL2b to capture the correlation between the neighboring residues. The PV2 meta-prediction reports a residue as disordered if any two of the underlying methods agree on a disordered state (Ghalwash 2012). NULL Mohamed F. Ghalwash, A. Keith Dunker and Zoran Obradovic (2012) "Uncertainty analysis in protein disorder prediction", Mol Biosyst. 8(1):381-91. NULL E6A115 0 0 disorder 0000-00-00 00:00:00 2012-10-07 20:26:25 4 1 8 IUPred-L IUPred assumes that the core of a well-structured globular protein has amino acids that can make enough favourable contacts to form a stable 3D structure. A matrix of amino acid pairs holds estimates of their pairwise interaction energies which is then used with a position specific scoring method to predict when stretches of amino acids are not contributing to a stable structure. The underlying assumption is that globular proteins are composed of amino acids which have the potential to form a large number of favourable interactions, whereas intrinsically unstructured proteins (IUPs) adopt no stable structure because their amino acid composition does not allow sufficient favourable interactions to form. (Dosztányi 2005). The L variant of IUPred uses scores specifically trained on long form disorder, and as such performs better on these regions over the short type. Dosztányi,Z., Csizmók,V., Tompa,P and Simon,I. (2005) "The Pairwise Energy Content Estimated from Amino Acid Composition Discriminates between Folded and Intrinsically Unstructured Proteins" J. Mol. Biol. 347, 827-839. http://iupred.enzim.hu B80424 1 1 disorder 0000-00-00 00:00:00 2012-11-25 20:42:23 6 1 10 Espritz-N Espritz predicts three variants of disorder using bi-directional recursive neural networks. Espritz-N was trained on NMR mobility data. The method can be run with a fast or slower variant (requiring PSI-BLAST) of the algorithm (Walsh 2012). Due to the wide genomic scale of D²P² the fast variant was used. Additionally the following cut-offs were used to yield 5% false positive rate 0.3089 with this Espritz variant. Walsh,I., Martin,A.J., Di Domenico,T., and Tosatto, S.C. (2012) ESpritz: accurate and fast prediction of protein disorder. Bioinformatics., 28(4), 503-509. http://protein.bio.unipd.it/espritz/ 26ACE0 1 0 disorder 0000-00-00 00:00:00 2012-11-25 20:42:26 7 1 11 Espritz-D Espritz predicts three variants of disorder using bi-directional recursive neural networks. Espritz-D was trained on DisProt data for long disorder. The method can be run with a fast or slower variant (requiring PSI-BLAST) of the algorithm (Walsh 2012). Due to the wide genomic scale of D²P² the fast variant was used. Additionally the following cut-offs were used to yield 5% false positive rate 0.5072 with this Espritz variant. Walsh,I., Martin,A.J., Di Domenico,T., and Tosatto, S.C. (2012) ESpritz: accurate and fast prediction of protein disorder. Bioinformatics., 28(4), 503-509. http://protein.bio.unipd.it/espritz/ 1C90A3 1 0 disorder 0000-00-00 00:00:00 2012-11-25 20:42:31 9 1 12 IUPred-S IUPred assumes that the core of a well-structured globular protein has amino acids that can make enough favourable contacts to form a stable 3D structure. A matrix of amino acid pairs holds estimates of their pairwise interaction energies which is then used with a position specific scoring method to predict when stretches of amino acids are not contributing to a stable structure. The underlying assumption is that globular proteins are composed of amino acids which have the potential to form a large number of favourable interactions, whereas intrinsically unstructured proteins (IUPs) adopt no stable structure because their amino acid composition does not allow sufficient favourable interactions to form. (Dosztányi 2005). The S variant of IUPred uses scores specifically trained on short form disorder, and as such performs better on these regions over the long type. NULL Dosztányi,Z., Csizmók,V., Tompa,P and Simon,I. (2005) "The Pairwise Energy Content Estimated from Amino Acid Composition Discriminates between Folded and Intrinsically Unstructured Proteins" J. Mol. Biol. 347, 827-839. http://iupred.enzim.hu 690013 0 0 disorder 0000-00-00 00:00:00 2012-11-21 13:18:38 5 1