Dr Liam J. McGuffin
RCUK Academic Fellow
[email protected]
McGuffin Group Methods
for Prediction of Protein Disorder
Two methods for different categories:
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DISOclust – Server version
•
DISOclust – Manual version
28 July 2017
© University of Reading 2007
www.reading.ac.uk/bioinf
DISOclust (Server)
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•
•
•
•
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Simple clustering method – unsupervised
Compares multiple models from nFOLD3 server
Calculates per-residue accuracy for each model using ModFOLDclust
Outputs probability of disorder (1 minus the mean per-residue accuracy)
Combines score with the scaled DISOPRED score
Manual method – same protocol but using all server models
S-score
(distance between
residues)
Residue accuracy
(mean S-score)
Disorder score
1-(mean residue
accuracy)
Si 
Sr 
1
 di 
1   
 d0 
2
1
Sia

N  1 aA
1

Pd  1    Srm 
 N mM 
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Si = S-score for residue i
di = distance between aligned residues
d0 = distance threshold (3.9)
Sr = predicted residue accuracy for model
N = number of models
A = set of alignments
Sia = Si score for a residue in a structural
alignment (a)
Pd = posterior probability of disorder
M = the set of models
Srm = Sr score for a model (m).
2
True positive rate
True positive rate
False positive rate 0-0.1
False positive rate 0-1
AUC, Area Under Curve (see ROC plots below); SE, Standard Error in AUC score; AUC(0-0.1),
partial area under curve between 0-0.1 false positives.
Method
AUC
SE
AUC (0-0.1)
AUC-SE
AUC+SE
DISOclust_server
0.8715
0.0052
0.0532
0.8663
0.8767
DISOclust_manual
0.8654
0.0053
0.0540
0.8602
0.8707
DISOPRED
0.8399
0.0056
0.0500
0.8343
0.8455
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Answers to specific questions…
• In your analysis of disorder do you treat short disordered regions, e.g. a missing loop
in a crystal structure, differently than a disordered domain or an entirely disordered
protein?
No, all regions are treated the same. No specific methods for long or short
regions.
• Can you briefly describe your disorder analysis, i.e. is it based on physical principals,
machine learning or a combination of both.
Results from structure based method (DISOclust) are combined with
results from a sequenced based machine learning method (DISOPRED).
DISOclust significantly improved all CASP7 methods (see paper).
• Does your analysis of disorder prediction affect your template free modeling, i.e.
does the disorder prediction aid your free model prediction? If so, in what way, in
practice, did you use your disorder prediction for free modeling?
Did not carry out FM, although the method does work for FM targets
• Can your disorder prediction distinguish between regions predicted to be fully
disordered, i.e. 'cooked spaghetti', or alternatively an ensemble of a few alternative
conformations?
Correctly identified T0484 and T0500 as fully disordered. Works equally
well on long/short regions of disorder. The DISOclust server provides
visualisation of multiple alternative conformations.
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The DISOclust server
http://www.reading.ac.uk/bioinf/DISOclust/
McGuffin, L. J. (2008) Intrinsic disorder prediction from the analysis of
multiple protein fold recognition models. Bioinformatics, 24,1798-804.
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