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Hybrid Protein Model Quality Assessment

Hybrid Protein Model Quality Assessment. Jianlin Cheng Computer Science Department & Informatics Institute University of Missouri, Columbia, MO, USA. MULTICOM-CLUSTER: Automated Hybrid Quality Assessment. 1. Predict the quality of each single CASP8 model. ModelEvaluator. Model.

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Hybrid Protein Model Quality Assessment

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  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 ModelEvaluator Model • Sequence-Based Prediction: • Secondary Structure • Solvent Accessibility • Contact Map 2. Select top 5 ranked models as references Matching Scores 3. Compare each model with reference models –> average global quality Support Vector Machine 4. Superimpose each model with reference models -> local quality Predicted GDT-TS score

  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 TM-Score 1. Predict the quality of each single CASP8 model A V E R A G E 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 . . .

  5. MULTICOM-CLUSTER: Automated Hybrid Quality Assessment 1. Predict the quality of each single CASP8 model Calculate Distance 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

  6. MULTICOM: Human Hybrid Quality Assessment 1. Predict the quality of each single CASP8 model Meta Analysis • Download CASP8 QA predictions • Calculate average predicted quality score of each model • Rank models by average scores 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

  7. Server Results of Global Quality Improvement of correlation and loss is about 15% 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

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