We combine IIF, in which the reaction is treated implicitly and the diffusion is treated explicitly and exactly, with various sparse grid techniques based on the finite element and finite difference methods and a multi-level combination approach.
To consider the overall quality of our multi-level combination approach, we used TM-score.
This demonstrates that the multi-level combination approach works competitively well on high accuracy easy targets.
Here we present a multi-level combination approach to improve protein structure prediction during all facets of TBM.
Here we describe MULTICOM, a multi-level combination approach to improve the various steps in protein structure prediction.
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Their performance in terms of average GDT-TS is close to the second best predictor, indicating the multi-level combination is competitive in this category.
The CASP8 results show that the multi-level combination approach is effective for the full spectrum of protein modeling, including high-accuracy TBM, hard TBM and FM.
Results: The multi-level combination approach was implemented via five automated protein structure prediction servers and one human predictor which participated in the eighth Critical Assessment of Techniques for Protein Structure Prediction (CASP8), 2008.
Our multi-level combination pipeline (Fig. 1) for protein structure prediction is generally comprised of five steps: (i) template identification and ranking, (ii) multi-template combination, (iii) model generation, (iv) model evaluation and (v) model combination and refinement.
The results show that the multi-level combination of complementary templates, alternative alignments and similar models aided by model quality assessment can systematically improve both template-based and template-free protein modeling.
