GeneSilico MetaDisorder web service provides access to four metapredictors designed to predict disorder in proteins using existing bioinformatics tools and machine learning.
As an output you will get metapredictions based on optimized results from primary methods. Methods were carefully benchmarked and validated using our own dataset
and during blind tests CASP8 and CASP9.
This method uses 15 predictions from primary disorder predictors (for a complete list see table below) and than weights their output according method accuracy
(see Figure 1). Individual weights of methods correspond to 1+Sw score of the method for the first dataset. Although this method seems to be very simple, it has been
proven during CASP (the best method in 2008) that this approach can be very succesful (AUC for targets in CASP8 was over 0.91!!!). On the other hand, those
targets were very easy to predict (AUC for our own, more challenging dataset was ~0.83, which seems to be more realistic).
Uses alignments from 8 fold recognition method (for a complete list see table below). The idea behind it is that the gaps
in alignments can be used as an indicator of disorder (but please compare easy case in Example 1
with more blurred one in Example2). Resulting predictor was able to detect disorder with AUC 0.83 compared
with 0.91 for CASP8 targets and MetaDisorder). It must be stressed that each method can have different utility for disorder prediction and additionally
alignments produced by FR methods
are scored according their accuracy (this mean that the information from "good" alignments should be scored more than from worse). To address those questions
we optimized the weights for given method taking into account alignment quality using genetic algorithm implemented in
PyEvolve. CASP8 target were used for training and benchmarking. Additionally, the method uses smoothing filter (to avoid one or two letter
long miss predictions) and the correction for terminal amino acids (prone to be more disordered).
MetaDisorder3D takes at most 10 best alignments produced by FR method and divide them to "good", "medium" and "poor" alignments using methods native scores:
Merges above mentioned methods into one using genetic algorithm. Trained on CASP8 targets, validated during CASP9
Similar to previous method, but for the optimization, instead of using Sw score in fitness function of GA, we use so-called Sww score, which tries
to capture the best features of AUC and Sw score. Sww score is calculated exactly as classical Sw, but the disorder/order thresholds are moved like it is done in AUC.
Dataset used to train and benchmark methods
We used 3 separate datasets which were cross validated and/or bootstrapped when used.
1) 1147 proteins from PDB database filtered by resolution <2 Å, R-factor < 0.2, length 50-1000 amino acids
and sequence identity < 20%
2) 566 proteins composed from Disprot (version 3.6) and CASP7 targets
3) 122 targets from CASP8
How disorder is defined and calculated?
TP - both
FN - predicted as ordered, but is
TN - both ordered
FP - predicted as disordered, but is
The accuracy of methods was calculated using Sw score and AUC (area under curve):
Primary methods for disorder prediction
* - from November 2015, iPDA method has been replaced by SPINE-D in pipeline
R, Jensen L, Diella F, Bork P, Gibson T, Russell R. Protein disorder
prediction: implications for structural proteomics. Structure 2003; 11:
Z, Peng K, Vucetic S, Radivojac P, Dunker AK. Exploiting heterogeneous
sequence properties improves prediction of protein disorder. Proteins
2005; 61: 176-82.
|iPDA||Su CT, Chen CY, Hsu CM. iPDA:
integrated protein disorder analyzer. Nucleic Acids Res. 2007 Jul;35(Web Server issue):W465-72.
||remote (no longer available)*
Cheng J, Sweredoski M, Baldi P. "Accurate Prediction of Protein Disordered Regions by Mining Protein Structure Data" Data Mining and Knowledge Discovery. 2005; 11(3):213-222.
R, Russell R, Neduva V, Gibson T. GlobPlot: Exploring protein sequences
for globularity and disorder. Nucleic Acids Res. 2003; 31: 3701-8.
Z, Csizmok V, Tompa P, Simon I. IUPred: web server for the prediction
of intrinsically unstructured regions of proteins based on estimated
energy content. Bioinformatics 2005; 21: 3433-3434.
K, Hirose S, Noguchi T. POODLE-S: web application for predicting
protein disorder by using physicochemical features and reduced amino
acid set of a position-specific scoring matrix. Bioinformatics 2007;
S, Shimizu K, Kanai S, Kuroda Y, Noguchi T. POODLE-L: a two-level SVM
prediction system for reliably predicting long disordered regions.
Bioinformatics 2007; 23: 2046-53.
T, Kinoshita K. PrDOS: prediction of disordered protein regions from
amino acid sequence. Nucleic Acids Res. 2007; 35: W460-4.
Vullo A, Bortolami O, Pollastri G, Tosatto S. "Spritz: a server for the prediction of intrinsically disordered regions in protein sequences using kernel machines"
Nucleic Acids Res. 2006; 34(Webserver Issue): W164-W168
Z, Thomson R, McNeil P, Esnouf R. RONN: the bio-basis function neural
network technique applied to the detection of natively disordered
regions in proteins. Bioinformatics 2005; 21: 3369-76.
Fold recognition methods for disorder prediction
* for blast and hhsearch you should expect two results. First, blast is run with and without -F option (called
here pdbblast and blastp). Second, hhsearch is run over pdb70 and cdd databases.
||Altschul SF, Madden TL, Schaffer AA,
Zhang J, Zhang Z, Miller W, Lipman DJ. Gapped BLAST and PSI-BLAST: a new generation of protein
database search programs. Nucleic Acids Res. 1997; 25(17): 3389-402.
||Soding J. (2005) Protein homology
detection by HMM-HMM comparison. Bioinformatics 21, 951-960.
||Jaroszewski L, Rychlewski L, Li Z,
Li W, Godzik A. FFAS03: a server for profile--profile sequence alignments. Nucleic Acids Res.
2005; 33, W284-8.
||Lobley A, Sadowski MI, Jones DT.
pGenTHREADER and pDomTHREADER: new methods for improved protein fold recognition and superfamily
discrimination. Bioinformatics 2009; 25(14): 1761-7
||Wallner B, Elofsson A. Pcons5: combining consensus,
structural evaluation and fold recognition scores. Bioinformatics. 2005; 21(23): 4248-54.
||Kelley LA, Sternberg MJ. Protein structure prediction
on the Web: a case study using the Phyre server. Nat Protoc. 2009; 4(3): 363-71.
||remote (800 aa limit)
This site is powered by: