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McGuffin Group Methods for Quality Assessment

McGuffin Group Methods for Quality Assessment. Three methods for different categories: ModFOLD v 1.1 – Server, QMODE1 ModFOLDclust – Server, QMODE2 ModFOLD v 2.0 – Human, QMODE1 (now a server, QMODE2). SS (new). SS-weighted (new). ModSSEA. TM-score. MODCHECK. ProQ-MX. ProQ-LG.

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McGuffin Group Methods for Quality Assessment

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  1. McGuffin Group Methods for Quality Assessment • Three methods for different categories: • ModFOLD v 1.1 – Server, QMODE1 • ModFOLDclust – Server, QMODE2 • ModFOLD v 2.0 – Human, QMODE1 • (now a server, QMODE2)

  2. SS (new) SS-weighted (new) ModSSEA TM-score MODCHECK ProQ-MX ProQ-LG ModFOLD v 1.1 (Server) • Combines 6 QA scores using a Neural Network (4 scores in CASP7) • Considers models individually • Trained using TM-scores and fold recognition models • Outputs a single score for each model (QMODE1) Inputs Hidden Layer Output

  3. 2. Per-residue accuracy - Mean S-score rearranged to give distance in Angstroms 1. Overall/global model quality - Mean TM-score between models (Similar to 3D-Jury) Si = S-score for residue i di = distance between aligned residues according to TM-score superposition d0 = distance threshold (3.9) Sr = predicted residue accuracy for the model N = number of models A = set of alignments Sia = Si score for a residue in a structural alignment (a) S = quality score for model N-1 = number of pairwise structural alignments carried out for model M = set of alignments Tm = TM-score for alignment of models ModFOLDclust (Server) • Simple clustering method - unsupervised • Compares all sever models against one another • Outputs overall score plus per-residue accuracy (QMODE2)

  4. Server rank (new) ModFOLDclust (new) SS (new) SS-weighted (new) TM-score ModSSEA MODCHECK ProQ-MX ProQ-LG ModFOLD v 2.0 (Manual) • Combines ModFOLD scores, ModFOLDclust score and initial server ranking using a NN • Considers models individually (sort of) • Compares each model against 30 nFOLD3 server models to get a ModFOLDclust score (server version) • Per-residue accuracy from ModFOLDclust method (server version)

  5. ModFOLD 2.0 - all TS1 models ModFOLDclust – all TS1 models Predicted quality Predicted quality Observed quality (GDT-TS) Observed quality (GDT-TS) ModFOLDclust – T0498 ModFOLDclust – T0499 Predicted quality Predicted quality Observed quality (GDT-TS) Observed quality (GDT-TS)

  6. Results continued… Conclusions • ModFOLD 1.1: • Increase in average per-target correlation since CASP7? • Decrease in global correlation? But diff. data sets. • ModFOLD 2.0: • Fewer outliers but no significant difference from ModFOLDclust • Benchmarking on CASP7 set showed an increase in Kendall’s Tau (not significant, training artefact?) • ModFOLDclust: • Most simple & effective method, but CPU intensive • Still room for improvement, doesn’t consistently recognise best model • Marginally better than Zhang-Server in terms of cumulative GDT-TS, but difference is not significant

  7. http://www.reading.ac.uk/bioinf/ModFOLD/ l.j.mcguffin@reading.ac.uk • References: • McGuffin, L. J. (2008) The ModFOLD Server for the Quality Assessment of Protein Structural Models. Bioinformatics, 24, 586-7. • McGuffin, L. J. (2007) Benchmarking consensus model quality assessment for protein fold recognition. BMC Bioinformatics, 8, 345. The ModFOLD server

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