Assessment of Refinement of Template-Based Models in CASP11

By Vivek Modi, Roland L. Dunbrack. 2016

37 citations recorded by the source corpus.

CASP11 (the 11th Meeting on the Critical Assessment of Protein Structure Prediction) ran a blind experiment in the refinement of protein structure predictions, the fourth such experiment since CASP8. As with the previous experiments, the predictors were provided with one starting structure from the server models of each of a selected set of template‐based modeling targets and asked to refine the coordinates of the starting structure toward native. We assessed the refined structures with the Z‐scores of the standard CASP measures, which compare the model‐target similarities of the models from all the predictors. Furthermore, we assessed the refined structures with “relative measures,” which compare the improvement in accuracy of each model with respect to the starting structure. The latter provides an assessment of the extent to which each predictor group is able to improve the starting structures toward native. We utilized heat maps to display improvements in the Calpha–Calpha distance matrix for each model. The heat maps labeled with each element of secondary structure helped us to identify regions of refinement toward native in each model. Most positively scoring models show modest improvements in multiple regions of the structure, while in some models we were able to identify significant repositioning of N/C‐terminal segments and internal elements of secondary structure. The best groups were able to improve more than 70% of the targets from the starting models, and by an average of 3–5% in the standard CASP measures. Proteins 2016; 84(Suppl 1):260–281. © 2016 Wiley Periodicals, Inc.

Subfields: Computational Theory and Mathematics, Materials Chemistry, Molecular Biology

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Citations in the corpus

Critical Assessment of Methods of Protein Structure Prediction (CASP) – Round XII (2017, methodology): “27,28 From a practical point of view, it is important to also have methods that perform consistently.”
Assessment of the model refinement category in CASP12 (2017, methodology): “1,7,31,32 Refinement targets are selected from amongst the main challenge targets.”
Experimental accuracy in protein structure refinement via molecular dynamics simulations (2018, methodology): “Since CASP10, MD-based refinement protocols have started to achieve more consistent structural improvements (33, 34), and such methods have become a key element in structure refinement protocols (20, 21, 23, 28, 35 – 41).”
Computational protein structure refinement: Almost there, yet still so far to go (2017, background): “As template-based models often come within 2 – 5 Å C α RMSD from an experimentally determined native structure, 46 the goal of structure re ﬁ nement is a seemingly moderate degree of improvement in accuracy by just a few Å. 48 – 50 Template-based models already incorporate knowledge from existing…”
ReFOLD: a server for the refinement of 3D protein models guided by accurate quality estimates (2017, background): “The most successful human refinement group in both CASP10 and CASP11 was the Feig group (1,11).”
Protein homology model refinement by large-scale energy optimization (2018, methodology): “In contrast, coarse-grained conformational search and unrestrained simulations can sample more extensively but suffer from inaccuracy in energy functions and thus more often degrade than improve model quality (3, 8, 11).”