Rapid model quality assessment for protein structure predictions using the comparison of multiple models without structural alignments

By Liam J. McGuffin, Daniel B. Roche. 2010

107 citations recorded by the source corpus.

Subfields: Materials Chemistry, Molecular Biology

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In Materials Chemistry

In Molecular Biology

Citations in the corpus

Local Error Estimates Dramatically Improve the Utility of Homology Models for Solving Crystal Structures by Molecular Replacement (2015, background): “Predictions that did not obey this latter relationship were of lower completeness; e.g., side chains or whole loops were missing.”
Accurate template‐based modeling in CASP12 using the IntFOLD4‐TS, ModFOLD6, and ReFOLD methods (2018, methodology): “For our IntFOLD4 server TS predictions, the major improvement over the IntFOLD3-TS method (used in CASP11) was the integration of our new ModFOLD6_rank model quality assessment method for final model selection and ASE, instead of using ModFOLDclust2.”
ModFOLD6: an accurate web server for the global and local quality estimation of 3D protein models (2017, unknown): “We initially implemented a quasi-single model approach with our ModFOLD3 method (18), which generated reference sets of models from the target sequence, using IntFOLD-TS (19), for comparison with the submitted model using ModFOLDclust2 (16).”
The IntFOLD server: an integrated web resource for protein fold recognition, 3D model quality assessment, intrinsic disorder prediction, domain prediction and ligand binding site prediction (2011, methodology): “Each submitted model is compared against the models generated by nFOLD4 (and every other provided model) using the ModFOLDclust2 method (7).”
IntFOLD: an integrated server for modelling protein structures and functions from amino acid sequences (2015, methodology): “The DISOclust3 method analyses the ModFOLDclust2 (17) per-residue scores in order to identify the regions of high variability occurring in the multiple alternative 3D models that are generated by the IntFOLD3TS method.”
Large-scale model quality assessment for improving protein tertiary structure prediction (2015, methodology): “The multi-model QA methods include ModFOLDclust2 (McGuffin and Roche, 2010); Pcons (Wallner and Elofsson, 2006); APPOLLO (Wang et al. , 2011); QApro— a weighted combination of ModelEvaluator and APOLLO (Cao et al. , 2014a).”
FunFOLD: an improved automated method for the prediction of ligand binding residues using 3D models of proteins (2011, methodology): “According to the ModFOLDclust2 [28] predictions, the global model quality score for the model used in the prediction for target T0483 is ~0.”
Deep convolutional neural networks for predicting the quality of single protein structural models (2019, methodology): “One kind of QA methods require a pool of models as input, which are often called consensus (or multi-model) methods 16,24-26 .”