Accurate template‐based modeling in CASP12 using the IntFOLD4‐TS, ModFOLD6, and ReFOLD methods

McGuffin, Liam J.; Shuid, Ahmad Naqib; Kempster, Robert; Maghrabi, Ali H. A.; Nealon, John Oliver; Salehe, Bajuna R.; Atkins, Jennifer; Roche, Daniel B.

doi:10.1002/prot.25360

Cited by 50 publications

(55 citation statements)

References 27 publications

(69 reference statements)

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“…For reference, we have included the ROC AUC values in Table , where we corroborate a clear lead tied by IntFOLD4_TS, SWISS‐MODEL, and HHPredB. The 3 servers are performing significantly better than any other server listed in providing realistic local model confidence estimates.…”

Section: Resultsmentioning

confidence: 70%

“…The results over the CAMEO time frame 2016‐05‐01 to 2016‐07‐30 reveal large discrepancies between individual servers (see also Supporting Information Figure S2). IntFOLD4‐TS was most consistently assigning correct model confidence estimates, closely followed by HHPredB and SWISS‐MODEL. Scatter plots of the individual server data are given in the supplementary material.…”

Section: Resultsmentioning

confidence: 89%

“…Ligand binding site accuracy evaluations are grouped based on the PDB ligand classification in 4 classes: ionic (I), organic (O), short nucleotides (N), and short peptides (P) from 2 to 10 bases or residues, respectively. Note that this measure does not rely on ligands actually being present in the predictions, and currently only 2 structure prediction servers in CAMEO actually aim to model ligands in their predictions, notably SWISS‐MODEL and IntFOLD4‐TS . Table shows the ranking based on the ligand binding site quality, where the top 5 methods produce acceptable binding sites on average.…”

Section: Resultsmentioning

confidence: 99%

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Continuous Automated Model EvaluatiOn (CAMEO) complementing the critical assessment of structure prediction in CASP12

et al. 2017

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Every second year, the community experiment "Critical Assessment of Techniques for Structure Prediction" (CASP) is conducting an independent blind assessment of structure prediction methods, providing a framework for comparing the performance of different approaches and discussing the latest developments in the field. Yet, developers of automated computational modeling methods clearly benefit from more frequent evaluations based on larger sets of data. The "Continuous Automated Model EvaluatiOn (CAMEO)" platform complements the CASP experiment by conducting fully automated blind prediction assessments based on the weekly pre-release of sequences of those structures, which are going to be published in the next release of the PDB Protein Data Bank. CAMEO publishes weekly benchmarking results based on models collected during a 4-day prediction window, on average assessing ca. 100 targets during a time frame of 5 weeks. CAMEO benchmarking data is generated consistently for all participating methods at the same point in time, enabling developers to benchmark and cross-validate their method's performance, and directly refer to the benchmarking results in publications. In order to facilitate server development and promote shorter release cycles, CAMEO sends weekly email with submission statistics and low performance warnings. Many participants of CASP have successfully employed CAMEO when preparing their methods for upcoming community experiments. CAMEO offers a variety of scores to allow benchmarking diverse aspects of structure prediction methods. By introducing new scoring schemes, CAMEO facilitates new development in areas of active research, for example, modeling quaternary structure, complexes, or ligand binding sites.

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Section: Resultsmentioning

confidence: 70%

Section: Resultsmentioning

confidence: 89%

Section: Resultsmentioning

confidence: 99%

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Continuous Automated Model EvaluatiOn (CAMEO) complementing the critical assessment of structure prediction in CASP12

et al. 2017

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“…DOPE scores > −0.3 indicate levels of relatively high energy, pointing towards structural errors. Additionally, the model was applied to the ModFOLD6 server calculating a residue error plot, a global model quality score (0.5348), and a P ‐value ( P < .0001). Overall, the structure was of high quality, apart from the flexible loops which are also experimentally difficult to assess.…”

Section: Methodsmentioning

confidence: 99%

Ara h 7 isoforms share many linear epitopes: Are 3D epitopes crucial to elucidate divergent abilities?

Ehlers

Klinge

Suer

et al. 2019

Clin Experimental Allergy

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Background The peanut allergens Ara h 2, h 6, and h 7 are potent allergens and can trigger severe reactions. Ara h 7 consists of three isoforms differing in their ability to induce basophil degranulation, whereas the ability of Ara h 7.0201 is comparable to Ara h 2 and 6 as shown in previous literature. Objective To identify linear epitopes of Ara h 7.0101, Ara h 7.0201 and Ara h 7.0301 recognized by IgE and IgG4 from patients sensitized to Ara h 7 and to investigate their potential to elucidate divergent abilities of the Ara h 7 isoforms in inducing basophil activation. Methods Linear epitopes recognized by IgE and IgG4 were mapped by peptide microarray analysis containing 15‐mer peptides of Ara h 2.0201, 6, 7.0101, 7.0201 and 7.0301 and 39 peanut allergic patients sensitized to Ara h 7 (discovery). For validation, 20‐mer peptides containing the minimal epitope and surrounding amino acids were incubated with 25 sensitized patients and 10 controls (validation). Results Three out of 14 linear epitopes were unique for each isoform (Ara h 7.0101: aa 97‐109; Ara h 7.0201: aa 122‐133; Ara h 7.0301: aa 65‐74) but scarcely recognized by IgE. The main linear IgE epitope (aa 51‐57) located in the long flexible loop of all Ara h 7 isoforms was bound by antibodies from 31% of the patients (discovery and validation cohort). Regarding IgG4, 55% of the patients recognized an epitope present on all isoforms (aa 55‐65), whereas epitope aa 129‐137, only present on Ara h 7.0101/0.0301, was recognized by 38% of the patients. Recognition was highly individual, although 20% of the patients recognized any linear epitope neither by IgE nor by IgG4 despite a low mean z‐score of ≥ 1.7. Remarkably, only 50% of the patients recognized one or more epitopes by IgE. Conclusion & Clinical Relevance Ara h 7 isoforms share many linear epitopes being easily accessible for antibody binding. Unique epitopes, essential to elucidate divergent potencies, were scarcely recognized, suggesting a crucial involvement of conformational epitopes.

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“…Therefore it could be claimed that ProQ is laying the foundation for the improvement in model QA apparent in CASP12. ProQ2 is also included in the top‐ranked structure prediction methods BAKERROSETTA‐SERVER and the IntFOLD4 server for TS prediction …”

Section: Methodsmentioning

confidence: 99%

Methods for estimation of model accuracy in CASP12

et al. 2017

Self Cite

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Methods to reliably estimate the quality of 3D models of proteins are essential drivers for the wide adoption and serious acceptance of protein structure predictions by life scientists. In this article, the most successful groups in CASP12 describe their latest methods for estimates of model accuracy (EMA). We show that pure single model accuracy estimation methods have shown clear progress since CASP11; the 3 top methods (MESHI, ProQ3, SVMQA) all perform better than the top method of CASP11 (ProQ2). Although the pure single model accuracy estimation methods outperform quasi-single (ModFOLD6 variations) and consensus methods (Pcons, ModFOLDclust2, Pcomb-domain, and Wallner) in model selection, they are still not as good as those methods in absolute model quality estimation and predictions of local quality. Finally, we show that when using contact-based model quality measures (CAD, lDDT) the single model quality methods perform relatively better.

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Accurate template‐based modeling in CASP12 using the IntFOLD4‐TS, ModFOLD6, and ReFOLD methods

Cited by 50 publications

References 27 publications

Continuous Automated Model EvaluatiOn (CAMEO) complementing the critical assessment of structure prediction in CASP12

Continuous Automated Model EvaluatiOn (CAMEO) complementing the critical assessment of structure prediction in CASP12

Ara h 7 isoforms share many linear epitopes: Are 3D epitopes crucial to elucidate divergent abilities?

Methods for estimation of model accuracy in CASP12

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