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

1
A Multi-Template Multi-Model Combination Approach
to Template-Based Modeling

• Jianlin Cheng
• Computer Science Department Informatics
  Institute
• University of Missouri, Columbia, MO, USA

2
1. Template Ranking
2. Multiple-Template Combination
Combination
Alignments
MAR-TCRK-EGAP-WY Y-R-MH-R-DGM-MWT TAKMTHK-DEGFG-
YW
Query-Template 1
MARTCRKEGAP-WY Y-RMH-RDGM-MWT
Input Query
. . .
MARTCRKE
Query-Template 2
MAR-TCRK-EGAPWY TAKMTHK-DEGFGYW
. . .
. . .
4. Evaluation 5. Combination Refinement (2-3)
3. Model Generation
Models
Generator
Output
CASP8 Server Models
3
Traditional Model Selection

• Single-Model Evaluation
• Clustering / Consensus Approach

4
Global-Local Model Combination
CASP8 Models
Rank models by GDT-TS scores predicted by
ModelEvaluator

. . .
Put relatively good, but not the best models at
the top
5
Global-Local Model Combination
Structure comparison by TM-Score
. . .
. . .
Select top 5 models as seed models
Identify similar models or fragments
Retain top 50 models
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Global-Local Model Combination

• Globally similar models
• Locally similar model fragments
• Combination and iterative modeling by Modeller
• Side chain rebuilt by SCWRL.

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Some High-Quality Predictions
T0390 GDT0.90
T0426 GDT0.97
T0432 GDT0.92
T0458 GDT0.97
Orange structure Green model
H-Bonds are well predicted.
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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

9
Acknowledgements

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