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. 5. Combination & Refinement (2-3%)
1. Template Ranking
2. Multiple-Template Combination
Alignments
Combination
Query-Template 1
MAR-TCRK-EGAP-WY…
Y-R-MH-R-DGM-MWT…
TAKMTHK-DEGFG-YW…
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

6. Global-Local Model Combination
Globally similar models
Locally similar model fragments
Combination and iterative modeling by Modeller
Side chain rebuilt by SCWRL.

7. Some High-Quality Predictions
GDT=0.90
T0426
GDT=0.97
T0432
GDT=0.92
T0458
GDT=0.97
Orange: structure; Green: model
H-Bonds are well predicted.

8. 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