1 / 9

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

A Multi-Template Multi-Model Combination Approach to Template-Based Modeling. Jianlin Cheng Computer Science Department & Informatics Institute University of Missouri, Columbia, MO, USA. 1. Template Ranking. 2. Multiple-Template Combination. Combination. Alignments. MAR-TCRK-EGAP-WY…

aysel
Télécharger la présentation

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

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  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

  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 T0390 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

More Related