Improving sequence-based fold recognition by using 3D model quality assessment

By Chris S. Pettitt, Liam J. McGuffin, David T. Jones. 2005

66 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition

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In Computational Mechanics

In Computer Vision and Pattern Recognition

Citations in the corpus

Toward the estimation of the absolute quality of individual protein structure models (2010, background): “In a comprehensive QMEAN Z-score analysis of all experimental structures in the PDB, membrane proteins accumulate on one side of the score spectrum and thermostable proteins on the other.”
Outcome of a Workshop on Applications of Protein Models in Biomedical Research (2009, background): “probabilities, and solvation properties (Benkert et al., 2008; Bhattacharya et al., 2008; Pettitt et al., 2005; Shen and Sali, 2006; Sippl, 1993; Wallner and Elofsson, 2003).”
Automated tertiary structure prediction with accurate local model quality assessment using the intfold‐ts method (2011, methodology): “13 An early prototype version of the nFOLD fold recognition method14 was first tested in CASP6, which aimed to integrate these new model quality assessment (QA) scores directly with the GenTHREADER scores.”
The ModFOLD server for the quality assessment of protein structural models (2008, methodology): “The original ModFOLD protocol combined scores obtained from the ModSSEA method (McGuffin, 2007), the MODCHECK method (Pettitt et al., 2005) and the two ProQ methods (Wallner and Elofsson, 2003) using a neural network trained with the TM-score (Zhang and Skolnick, 2004).”
Deep learning extends de novo protein modelling coverage of genomes using iteratively predicted structural constraints (2018, methodology): “The representative structure of the largest cluster is taken, selected by estimated model accuracy using a combination of MODCHECK [38] Cβ potentials of mean force and all atom MODELLER [39] DOPE-HR scores, and this model used to seed the next iteration.”
I-TASSER server for protein 3D structure prediction (2008, unknown): “The general idea of the modeling quality estimation of 3D models has been pursued by a number of authors [8-10], which merges as a new research topic of "model quality assessment programs" (MQAP) [11] and is assessed in the recent CASP7 experiment in the category of QA [12].”
QMEAN: A comprehensive scoring function for model quality assessment (2008, methodology): “One common use of MQAP methods is to rerank the output of single methods,51 for example, by deriving models from alternative alignments.”
How well can the accuracy of comparative protein structure models be predicted? (2008, methodology): “ModFOLD (McGuffin 2007) is a neural network that combines data from ModSSEA (Pettitt et al. 2005), MODCHECK (McGuffin and Jones 2003), and ProQ to predict the accuracy of an input model.”
ProQ3: Improved model quality assessments using Rosetta energy terms (2016, background): “Availability: ProQ3 is freely available on BitBucket at https://bitbucket.org/ElofssonLab/proq3 Contact: arne@bioinfo.se”
Outcome of a workshop on archiving structural models of biological macromolecules. (2006, methodology): “…based on statistical treatments wherein models are evaluated on the basis of expected structural parameters, such as main chain stereochemistry, long-range protein contacts and solvation properties (e.g., PROSA II [Sippl, 1993], ProQ [Wallner and Elofsson, 2003], MODCHECK [Pettitt et al., 2005]).”
Evaluating the absolute quality of a single protein model using structural features and support vector machines (2009, background): “1,5–23 Most of these methods are relative scoring methods designed to discriminate near-native structures from decoys.”