© University of Reading Dr Liam J. McGuffin RCUK Academic Fellow 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 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 false positives. Method AUC SEAUC (0-0.1)AUC-SEAUC+SE DISOclust_server DISOclust_manual DISOPRED

To put your footer here go to View > Header and Footer4 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 s tructure 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.

To put your footer here go to View > Header and Footer5 The DISOclust server McGuffin, L. J. (2008) Intrinsic disorder prediction from the analysis of multiple protein fold recognition models. Bioinformatics, 24,