ModFOLD6: an accurate web server for the global and local quality estimation of 3D protein models

By Ali Hassan Ahmed Maghrabi, Liam J. McGuffin. 2017

100 citations recorded by the source corpus.

Abstract Methods that reliably estimate the likely similarity between the predicted and native structures of proteins have become essential for driving the acceptance and adoption of three-dimensional protein models by life scientists. ModFOLD6 is the latest version of our leading resource for Estimates of Model Accuracy (EMA), which uses a pioneering hybrid quasi-single model approach. The ModFOLD6 server integrates scores from three pure-single model methods and three quasi-single model methods using a neural network to estimate local quality scores. Additionally, the server provides three options for producing global score estimates, depending on the requirements of the user: (i) ModFOLD6_rank, which is optimized for ranking/selection, (ii) ModFOLD6_cor, which is optimized for correlations of predicted and observed scores and (iii) ModFOLD6 global for balanced performance. The ModFOLD6 methods rank among the top few for EMA, according to independent blind testing by the CASP12 assessors. The ModFOLD6 server is also continuously automatically evaluated as part of the CAMEO project, where significant performance gains have been observed compared to our previous server and other publicly available servers. The ModFOLD6 server is freely available at: http://www.reading.ac.uk/bioinf/ModFOLD/.

Subfields: Genetics, Molecular Biology

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Citations in the corpus

Introducing “best single template” models as reference baseline for the Continuous Automated Model Evaluation (CAMEO) (2019, neutral): “Within the top performing regime of this performance domain “ QMEANDisCo3 ” , 27 “ QMEANDisCo2, ” 29 “ ModFOLD6, ” 30 and “ ModFOLD7-lDDT ” 22 are clearly in the lead (Figure 3C), followed by “ QMEAN3, ” “ ModFOLD4, ” 31 and “ VoroMQA.”
IntFOLD: an integrated web resource for high performance protein structure and function prediction (2019, methodology): “The IntFOLD server provides a single point of access to an integrated suite of six component methods: IntFOLD-TS, for tertiary structure prediction (9–11,18,19); ModFOLD, for 3D model Accuracy Self-Estimate (ASE) scoring (9,20); ReFOLD, for 3D model refinement (9,21); DISOclust, for disorder predict”
QMEANDisCo—distance constraints applied on model quality estimation (2019, background): “An alternative is so called quasi-single model methods (Maghrabi and McGuffin, 2017), which try to combine the convenience of taking a single model as input with the predictive power of consensus methods by generating ensemble information themselves.”
Accurate template‐based modeling in CASP12 using the IntFOLD4‐TS, ModFOLD6, and ReFOLD methods (2018, methodology): “Our ModFOLD6_rank method significantly improves upon our previous approaches for quality assessment, particularly for top model selection (Figure 3) and for providing local error estimates.(11) On average, the ReFOLD method provided a further significant improvement in model quality of our submitted”
Methods for estimation of model accuracy in CASP12 (2017, methodology): “Our initial motivation in the development of ModFOLD6 was to increase the accuracy of local/per-residue assessments for single models.(16) For the local/per-residue error estimates, each model was considered individually using 2 new pure-single model methods, the Contact Distance Agreement and the S”
Smooth orientation-dependent scoring function for coarse-grained protein quality assessment (2018, methodology): “In addition to these two main types of QA methods, techniques combining both ideas have also been proposed (11, 18), referred to as quasi-single model QA methods.”