EVA: evaluation of protein structure prediction servers

By Ingrid Y. Y. Koh, Volker A. Eyrich, Marc A. Martí-Renom, Dariusz Przybylski, Mallur Srivatsan Madhusudhan, Narayanan Eswar, O. Graña, Florencio Pazos, Alfonso Valencia, Andrej Sali and 1 more. 2003

186 citations recorded by the source corpus.

Subfields: Materials Chemistry, Molecular Biology

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Citations in the corpus

Toward the estimation of the absolute quality of individual protein structure models (2010, methodology): “Proteins from the thermophilic organism Thermatoga maritima received significantly higher QMEAN Z-scores in a pairwise comparison with their homologous mesophilic counterparts, underlining the significance of the QMEAN Z-score as an estimate of protein stability.”
The PredictProtein server (2003, background): “Protein structure [see the EVA server (26) for an up-to-date evaluation of structure prediction]: 3a. PROFsec secondary structure prediction: on average, 76% of all residues are correctly predicted by (only 71% by PHDsec); 3b. PROFacc accessibility prediction: almost 80% of all residues are…”
The SWISS-MODEL Repository and associated resources (2008, background): “during the bi-annual CASP (Critical Assessment of Techniques for Protein Structure Prediction) experiments (10,11) or the automated EVA project ( 12 ), has demonstrated that comparative protein structure modelling is currently the most accurate”
Outcome of a workshop on archiving structural models of biological macromolecules. (2006, methodology): “Finally, modeling is also supported by programs and web servers intended for assessing different prediction methods using the experimentally determined structures as the reference (Koh et al., 2003; Rychlewski and Fischer, 2005) and for predicting errors in a model when the actual structure is unkno”
Critical assessment of high-throughput standalone methods for secondary structure prediction (2011, methodology): “Later on, the evaluations were carried out using the EVA platform [11,22].”
Structural genomics is the largest contributor of novel structural leverage (2009, background): “Our restriction to E-values B 10 implies that the inferred structural models are of relatively high reliability and cover most of the aligned regions [16, 25]; higher leverage and coverage can be achieved at the expense of accuracy [3, 17, 27, 33].”
Natively Unstructured Loops Differ from Other Loops (2007, methodology): “Technically, this sequence-unique subset was taken from the EVA server [74,75].”
The SWISS-MODEL Repository: new features and functionalities (2005, methodology): “The accuracy and reliability of the SWISS-MODEL server are continuously monitored by the EVA-CM evaluation project (15).”
Protein Modelling: What Happened to the “Protein Structure Gap”? (2013, unknown): “The assessment of techniques for structure prediction in retrospective experiments such as CASP (Moult et al., 2011), EVA (Koh et al., 2003), LifeBench (Rychlewski and Fischer, 2005) or CAMEO (Haas et al., 2013) allowed comparing different methods on the same dataset, thereby establishing the curren”
ModBase, a database of annotated comparative protein structure models and associated resources (2013, methodology): “The accuracy of automated comparative protein structure modeling has been quantified by the CAFASP effort (8) as well as automated web servers EVA ( 9 ) and LIVEBENCH (10).”
Alignment of protein sequences by their profiles (2004, background): “First, there already is a published benchmark on the EVA Web site (http://cubic.bioc.columbia.edu/ eva; Eyrich et al. 2001; Koh et al. 2003) that compares several sequence-structure, SAM, and PSI-BLAST methods.”
Protein homology detection by HMM?CHMM comparison (2005, methodology): “These servers are among the best-performing present-day methods for fold recognition, as can be seen from the results of the blind, automated structure prediction contests CAFASP, LIVEBENCH and EVA (Fischer et al., 2003; Rychlewski et al., 2003; Koh et al., 2003).”
A simple and fast secondary structure prediction method using hidden neural networks (2005, methodology): “, 1999) and Matthew’s correlation coefficients (MCCs) (for a review see Simossis and Heringa, 2004) as given on the EVA server (Koh et al., 2003).”