Abstract
Here we report on the assessment results of the third experiment to evaluate the state of the art in protein model refinement, where participants were invited to improve the accuracy of initial protein models for 27 targets. Using an array of complementary evaluation measures, we find that five groups performed better than the naïve (null) method—a marked improvement over CASP9, although only three were significantly better. The leading groups also demonstrated the ability to consistently improve both backbone and side chain positioning, while other groups reliably enhanced other aspects of protein physicality. The top-ranked group succeeded in improving the backbone conformation in almost 90% of targets, suggesting a strategy that for the first time in CASP refinement is successful in a clear majority of cases. A number of issues remain unsolved: the majority of groups still fail to improve the quality of the starting models; even successful groups are only able to make modest improvements; and no prediction is more similar to the native structure than to the starting model. Successful refinement attempts also often go unrecognized, as suggested by the relatively larger improvements when predictions not submitted as model 1 are also considered. Proteins 2014; 82(Suppl 2):98–111.
Highlights
The scope and accuracy of comparative modeling have been increasing steadily over time, but generating predictions that are consistently closer to the native structure than they are to the original templates still proves very challenging
Protein refinement methods developed since CASP9 have focused on a broad range of strategies including molecular dynamics (MD), fragmentbased approaches, knowledge-based approaches, elastic network models, and hydrogen bond network optimization.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]
We selected as refinement challenges those template-based modeling (TBM) targets or their structural domains (1) that were relatively small, with less than $250 amino acids and few missing residues; (2) that exhibited limited crystal contact distortions if they were solved by X-ray crystallography; (3) that had a tight conformational ensemble if they were studied by NMR spectroscopy
Summary
The scope and accuracy of comparative modeling have been increasing steadily over time, but generating predictions that are consistently closer to the native structure than they are to the original templates still proves very challenging. Protein refinement methods developed since CASP9 have focused on a broad range of strategies including molecular dynamics (MD), fragmentbased approaches, knowledge-based approaches, elastic network models, and hydrogen bond network optimization.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16] Perhaps the most promising advances have come in the design of novel parallel supercomputer architectures comprising substantial numbers of application-specific integrated circuits, which allow atomistic MD simulations to run for as long as 100 ls.[17,18] In combination with increasingly accurate physics-based force fields such as CHARMM, which have been used to successfully fold structurally diverse sets of fast-folding proteins,[19,20,21,22] the major problems limiting the application of MD to protein refinement can be mitigated to some extent. It is worth noting that distributed computing projects such as Folding@home have achieved similar aggregate ensemble simulation timescales,[23] while collaborative multiplayer online games such as Foldit have demonstrated that the integration of human visual problem-solving and strategy development capabilities are a powerful approach to tackle computationallylimited problems.[24]
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