A Multi-Template Multi-Model Combination Approach to Template-

Based Modeling

Jianlin Cheng

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

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MARTCRKE…

Input Query

1. Template Ranking

Alignments

MARTCRKEGAP-WY…Y-RMH-RDGM-MWT…

MAR-TCRK-EGAPWY…TAKMTHK-DEGFGYW…

Query-Template 1

Query-Template 2

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Combination

MAR-TCRK-EGAP-WY…Y-R-MH-R-DGM-MWT…TAKMTHK-DEGFG-YW…

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3. Model Generation

CASP8 Server Models

Generator

2. Multiple-Template Combination

4. Evaluation5. Combination & Refinement(2-3%)

Output

Models

Traditional Model Selection

• Single-Model Evaluation • Clustering / Consensus Approach

Global-Local Model Combination

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CASP8 Models

Rank models by GDT-TS scores

predicted by ModelEvaluator

Put relatively good, but not the best models at the top

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Global-Local Model Combination

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Retain top 50% models

Select top 5 modelsas seed models

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Structure comparisonby TM-Score

Identify similar modelsor fragments

Global-Local Model Combination

• Globally similar models• Locally similar model fragments• Combination and iterative modeling by

Modeller• Side chain rebuilt by SCWRL.

Some High-Quality Predictions

T0390GDT=0.90

T0426GDT=0.97

T0432GDT=0.92

T0458GDT=0.97

Orange: structure; Green: model

H-Bonds are well predicted.

Conclusions• Iterative modeling and averaging improve

side-chain placement, geometry, and H-Bonds• Combining multiple good similar models can

produce a model better than the top ranked model

• Combined models are at least as good as centroids and have no steric clashes

Acknowledgements

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