Abstract
Assessing the accuracy of 3D models has become a keystone in the protein structure prediction field. ModFOLD7 is our leading resource for Estimates of Model Accuracy (EMA), which has been upgraded by integrating a number of the pioneering pure-single- and quasi-single-model approaches. Such an integration has given our latest version the strengths to accurately score and rank predicted models, with higher consistency compared to older EMA methods. Additionally, the server provides three options for producing global score estimates, depending on the requirements of the user: (1) ModFOLD7_rank, which is optimized for ranking/selection, (2) ModFOLD7_cor, which is optimized for correlations of predicted and observed scores, and (3) ModFOLD7 global for balanced performance. ModFOLD7 has been ranked among the top few EMA methods according to independent blind testing by the CASP13 assessors. Another evaluation resource for ModFOLD7 is the CAMEO project, where the method is continuously automatically evaluated, showing a significant improvement compared to our previous versions. The ModFOLD7 server is freely available at http://www.reading.ac.uk/bioinf/ModFOLD/.
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Maghrabi, A.H.A., McGuffin, L.J. (2020). Estimating the Quality of 3D Protein Models Using the ModFOLD7 Server. In: Kihara, D. (eds) Protein Structure Prediction. Methods in Molecular Biology, vol 2165. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-0708-4_4
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DOI: https://doi.org/10.1007/978-1-0716-0708-4_4
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