Predicting accurate contacts in thousands of Pfam domain families using PconsC3

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DEEPCON: Protein Contact Prediction using Dilated Convolutional Neural Networks with Dropout (2019, methodology): “For instance, methods such as RaptorX [3], MetaPSICOV [11], DNCON2 [9], PconsC3 [12], and PconsC4 [2] are either available as a downloadable tool or a web server.”
Assessing the accuracy of contact predictions in CASP13 (2019, background): “After 2010, when first papers on correlated mutations were published, a number of refinements took place, such as combining a set of nonoverlapping contact predictions in a consensus approach and especially the application of various supervised deep neural network based approaches,(42) further impro”
PconsC4: fast, free, easy, and accurate contact predictions (2018, methodology): “One validation set is identical to the benchmark set in PconsC3 (Michel et al. , 2017b).”
Large-scale structure prediction by improved contact predictions and model quality assessment (2017, methodology): “However, by the inclusion of additional information and improved machine learning methods it is now often possible to obtain accurate contact maps for families as small as a few hundred effective sequences (Michel et al., 2017; Jones et al., 2015; Wang et al., 2017).”