Hybrid Protein Model Quality Assessment

Jianlin Cheng

Computer Science Department & Informatics InstituteUniversity of Missouri, Columbia, MO, USA

MULTICOM-CLUSTER: Automated Hybrid Quality Assessment

Sequence-Based Prediction:•Secondary Structure•Solvent Accessibility•Contact Map

Model

Matching Scores

Support VectorMachine

Predicted GDT-TS score

ModelEvaluator

MULTICOM-CLUSTER: Automated Hybrid Quality Assessment

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MULTICOM-CLUSTER: Automated Hybrid Quality Assessment

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TM-Score

AVERAGE

MULTICOM-CLUSTER: Automated Hybrid Quality Assessment

Calculate Distance

MULTICOM: Human Hybrid Quality Assessment

• Download CASP8 QA predictions

• Calculate average predicted quality score of each model

• Rank models by average scores

Meta Analysis

Server Results of Global QualityPredictor Average Per-

Target Correlation

Overall Correlation

Average Loss (GDT-TS)

Initial-Ranking 0.73 0.76 7.3

Hybrid-Refinement

0.88 0.89 6.2

Improvement of correlation and loss is about 15%

Predictor Average Per-Target Correlation

Overall Correlation

Average Loss (GDT-TS)

Initial-Ranking 0.79 0.84 5.2

Hybrid-Refinement

0.89 0.91 4.7

Human Results of Global Quality

Improvement of correlation and loss is about 10%

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

Acknowledgements

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