1. MULTICOM – A Combination Pipeline for Protein Structure Prediction
Jianlin Cheng
Computer Science Department & Informatics Institute
University of Missouri, Columbia, MO, USA

2. MULTICOM Structure Prediction Pipeline
Server Predictor
Query Sequence
1. Template Identification
2. Multi-Template Combination
Human Predictor
3. Model Generation
All CASP8 Server Models
4. Model Evaluation
5. Multi-Model Combination
Output

3. MULTICOM Structure Prediction Pipeline
Query Sequence
PSI-BLAST
HHSearch
COMPASS
FOLDpro + SPEM
1. Template Identification
2. Multi-Template Combination
Query-template alignments:
3. Model Generation
4. Model Evaluation
5. Multi-Model Combination
Find a set of good templates / fragments;
generate alternative query-template
alignments
Output

4. MULTICOM Structure Prediction Pipeline
Query Sequence
1. Template Identification
Combination
1. Combine top ranked query-
template alignment (QTA) with
other significant QTAs
2. Take fragments from less
significant QTA (Template-free)
2. Multi-Template Combination
3. Model Generation
4. Model Evaluation
Don’t try to find the best template;
Instead combine multiple good
templates / fragments.
5. Multi-Model Combination
Output

5. MULTICOM Structure Prediction Pipeline
Query Sequence
1. Template Identification
Integrative Model Generation
Modeller
Rosetta for template-free
small domains
2. Multi-Template Combination
3. Model Generation
4. Model Evaluation
Domain-level combination of
template-based and template-free approaches
5. Multi-Model Combination
Output

6. MULTICOM Structure Prediction Pipeline
Query Sequence
1. Template Identification
Model Ranking by ModelEvaluator
2. Multi-Template Combination
3. Model Generation
4. Model Evaluation
5. Multi-Model Combination
Output

7. ModelEvaluator Good models ranked at the top. Very effective for
Ab initio Sequence-Based Structural Feature Prediction
3D Model
Secondary Structure
Comparison
EEEECCEEEHHHHHHHHHHHHEEEECCEEEHHHH
Relative Solvent Accessibility
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Contact Map
Beta-Sheet Pairing
Good models ranked at the
top. Very effective for
template-free models.
Input Features
Predicted
GDT-TS score

8. MULTICOM Structure Prediction Pipeline
Query Sequence
Start from a top ranked model
Combine it with other models
having global similarity (80%, 4Å)
3. Combine it with the longest similar model fragments
1. Template Identification
Global-Local Model Combination
2. Multi-Template Combination
Modeller Iterative Modeling
3. Model Generation
Average Model
4. Model Evaluation
Don’t try to find the best model.
Instead combine multiple good
models / fragments (2-3% improvement).
5. Multi-Model Combination
Output

9. Good Template-Free Example: T0416_2
Structure
MULTICOM (GDT = 0.66, RMSD = 2.5)
Combination of 20 models:
Zhang-Server
Robetta
TASSER
MULTICOM
YASARA
forecast
Success: rank very good models at top.
Superposition (red: model)
(Courtesy by Prof. Joel Sussman)

10. Good Template-Free Example: T0513_2
Structure
MULTICOM (GDT = 0.73, RMSD=2.1)
Combine Robetta models
Better than each one of them
Success: rank very good models at top and
combination improves modeling.
Superposition (blue: model)

11. Not Good Template-Free Example: T0405_1
Structure
(Helix Bundle)
MULTICOM
GDT = 0.41
Superposition (by Prof. Sussman)
(Gray: structure, yellow: best model
green: MULTICOM model)
Failure: ModelEvaluator fails to identify correct helix orientations.

12. Concluding Remarks
CASP Community can sometime generate good template-free models (e.g. Rosetta-based tools)
ModelEvaluator can rank good template-free models at the top
Iterative global-local combination of models can improve template-free modeling
Blending of template-free and template-based modeling

13. Blending of Template-Free and Template-Based Modeling
100% TBM
50% TBM+50%FM
100% FM
Protein Modeling Spectrum

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