# © University of Reading 2007 Dr Liam J. McGuffin RCUK Academic Fellow 20 April 2014 McGuffin Group.

## Presentation on theme: "© University of Reading 2007 Dr Liam J. McGuffin RCUK Academic Fellow 20 April 2014 McGuffin Group."— Presentation transcript:

© University of Reading 2007 www.reading.ac.uk/bioinf Dr Liam J. McGuffin RCUK Academic Fellow l.j.mcguffin@reading.ac.uk 20 April 2014 McGuffin Group Methods for Prediction of Protein Disorder Two methods for different categories: DISOclust – Server version DISOclust – Manual version

To put your footer here go to View > Header and Footer2 DISOclust (Server) 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 P d = posterior probability of disorder M = the set of models S rm = S r score for a model (m). Disorder score 1-(mean residue accuracy) S-score (distance between residues) S i = S-score for residue i d i = distance between aligned residues d 0 = distance threshold (3.9) Residue accuracy (mean S-score) S r = predicted residue accuracy for model N = number of models A = set of alignments S ia = Si score for a residue in a structural alignment (a)

To put your footer here go to View > Header and Footer3 False positive rate 0-1 True positive rate False positive rate 0-0.1 True positive rate 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 SEAUC (0-0.1)AUC-SEAUC+SE DISOclust_server 0.8715 0.00520.05320.86630.8767 DISOclust_manual 0.8654 0.00530.05400.86020.8707 DISOPRED 0.8399 0.00560.05000.83430.8455