Assessment of the protein-structure refinement category in CASP8

By Justin L. MacCallum, Lan Hua, Michael J. Schnieders, Vijay S. Pande, Matthew P. Jacobson, Ken A. Dill. 2009

74 citations recorded by the source corpus.

Here, we summarize the assessment of protein structure refinement in CASP8. Twenty‐four groups refined a total of 12 target proteins. Averaging over all groups and all proteins, there was no net improvement over the original starting models. However, there are now some individual research groups who consistently do improve protein structures relative to a starting starting model. We compare various measures of quality assessment, including (i) standard backbone‐based methods, (ii) new methods from the Richardson group, and (iii) ensemble‐based methods for comparing experimental structures, such as NMR NOE violations and the suitability of the predicted models to serve as templates for molecular replacement. On the whole, there is a general correlation among various measures. However, there are interesting differences. Sometimes a structure that is in better agreement with the experimental data is judged to be slightly worse by GDT‐TS. This suggests that for comparing protein structures that are already quite close to the native, it may be preferable to use ensemble‐based experimentally derived measures of quality, in addition to single‐structure‐based methods such as GDT‐TS. Proteins 2009. © 2009 Wiley‐Liss, Inc.

Subfields: Materials Chemistry, Molecular Biology

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Citations in the corpus

Ab Initio Protein Structure Assembly Using Continuous Structure Fragments and Optimized Knowledge-based Force Field (2012, background): “(11) will help avoid the incorrect mirror image models, which have been most often encountered in ab initio structure folding.”
Experimental accuracy in protein structure refinement via molecular dynamics simulations (2018, methodology): “Model refinement methods have been evaluated in blind tests during CASP (Critical Assessment of protein Structure Prediction) since CASP8, held in 2008 (32).”
3Drefine: an interactive web server for efficient protein structure refinement (2016, methodology): “Performance of 3Drefine was benchmarked using refinement targets from CASP8 (12 targets) (19) and CASP9 (14 targets) refinement targets in a completely automated manner, without using the knowledge of additional information.”
Assessment of the model refinement category in CASP12 (2017, background): “1,7,31,32 Refinement targets are selected from amongst the main challenge targets.”
Critical assessment of methods of protein structure prediction—Round VIII (2009, unknown): “13 There are four papers assessing other aspects of structure modeling: domain and three-dimensional (3D) contact identification,14 assessment of disorder,15 prediction of model quality,16 and prediction of ligands binding sites.”
GalaxyRefine2: simultaneous refinement of inaccurate local regions and overall protein structure (2019, methodology): “In recent CASP (Critical Assessment of techniques for protein Structure Prediction) blind prediction experiments, a model refinement category was introduced to evaluate existing model refinement meth-ods and stimulate advances in the field (7).”
Computational protein structure refinement: Almost there, yet still so far to go (2017, methodology): “Up until CASP8, the overall most consistent structure refinement method was essentially not to attempt refinement at all.(65) In CASP9, refinement methods started to eke out, on average, very slight improvements.”
Atomic-level protein structure refinement using fragment guided molecular dynamics conformation sampling (2011, methodology): “Performance of FG-MD on CASP8 Refinement Targets The structure refinement category was a new addition to CASP since CASP8 (MacCallum et al., 2009).”
GalaxyRefine: protein structure refinement driven by side-chain repacking (2013, methodology): “The GalaxyRefine method has been extensively tested on (i) the refinement category targets of CASP8 (5), CASP9 (6) and CASP10 (53 proteins), (ii) Zhang-server (ITASSER) models (84 proteins) (11) and (iii) ROSETTA server models (69 proteins) (17) for CASP10 templatebased modeling targets and (iv) FG-”