1. Hybrid Protein Model Quality Assessment Jianlin Cheng Computer Science Department & Informatics Institute University of Missouri, Columbia, MO, USA

2. MULTICOM-CLUSTER: Automated Hybrid Quality Assessment 1. Predict the quality of each single CASP8 model 2. Select top 5 ranked models as references 3. Compare each model with reference models –> average global quality 4. Superimpose each model with reference models -> local quality Sequence-Based Prediction: Secondary Structure Solvent Accessibility Contact Map Model Matching Scores Support Vector Machine Predicted GDT-TS score ModelEvaluator

3. MULTICOM-CLUSTER: Automated Hybrid Quality Assessment 1. Predict the quality of each single CASP8 model 2. Select top 5 ranked models as references 3. Compare each model with reference models –> average global quality 4. Superpose each model with reference models -> local quality......

4. MULTICOM-CLUSTER: Automated Hybrid Quality Assessment 1. Predict the quality of each single CASP8 model 2. Select top 5 ranked models as references 3. Compare each model with reference models –> average global quality 4. Superpose each model with reference models -> local quality...... TM-Score AVERAGEAVERAGE

5. MULTICOM-CLUSTER: Automated Hybrid Quality Assessment 1. Predict the quality of each single CASP8 model 2. Select top 5 models as references 3. Compare each model with reference models –> average global quality 4. Superpose each model with reference models -> local quality Calculate Distance

6. MULTICOM: Human Hybrid Quality Assessment 1. Predict the quality of each single CASP8 model 2. Select top 5 models as references 3. Compare each model with reference models –> average global quality 4. Superpose each model with reference models -> local quality Download CASP8 QA predictions Calculate average predicted quality score of each model Rank models by average scores Meta Analysis

7. Server Results of Global Quality PredictorAverage Per- Target Correlation Overall Correlation Average Loss (GDT-TS) Initial-Ranking0.730.767.3 Hybrid- Refinement 0.880.896.2 Improvement of correlation and loss is about 15% PredictorAverage Per- Target Correlation Overall Correlation Average Loss (GDT-TS) Initial-Ranking0.790.845.2 Hybrid- Refinement 0.890.914.7 Human Results of Global Quality Improvement of correlation and loss is about 10%

8. Conclusions Single-model approach can put good, but not always the best models at the top Score refinement by structure comparison can improve both ranking and correlation Better initial ranking leads to better final ranking A simple average is a very effective meta QA method Structure comparison with reference models is a hybrid, semi-clustering approach

9. Acknowledgements CASP8 organizers and assessors CASP8 participants MU colleagues: Dong Xu, Toni Kazic My group: Zheng Wang Allison Tegge Xin Deng