BIOINFORMATICS ORIGINAL PAPER doi:10.1093/bioinformatics/btn069 Structural bioinformatics A comprehensive assessment of sequence-based and template-based methods for protein contact prediction

2007

Citations in the corpus

• Protein 3D Structure Computed from Evolutionary Sequence Variation (2011, methodology): “, improved contact energy in the I-Tasser simulation method [76] [19] or addition of EIC distance restraints into the Rosetta server.”
• Folding membrane proteins by deep transfer learning (2017, background): “Supervised learning predicts contacts from a variety of information, e.g., SVMSEQ(Wu and Zhang, 2008), CMAPpro (Di Lena et al., 2012), PconsC2 (Skwark et al., 2014) and PconsC3 (Skwark et al., 2016), MetaPSICOV (Jones et al., 2015), PhyCMAP (Wang and Xu, 2013) and CoinDCA-NN (Ma et al., 2015).”
• A position-specific distance-dependent statistical potential for protein structure and functional study (2012, methodology): “The window size that we used is consistent with what was used by the Zhang group (Wu and Zhang, 2008a), and the numbers of hidden neurons are not very different from what was used by the Zhou group (Xue et al., 2009).”
• Protein-protein interaction prediction via Collective Matrix Factorization (2010, background): “Feature vectors may be extracted based on protein sequences directly or may involve indirect evidences, including domain compositions, motif pairs and related mRNA expression [5], [6], [7], [8], [9], [10], among others.”
• Machine Learning Methods for Protein Structure Prediction (2008, background): “long-range contacts can effectively help build a protein topology [76].”
• Predicting protein residue-residue contacts using deep networks and boosting (2012, methodology): “Once templates have been found and aligned, residue-residue contacts are predicted using the contacts found in the structures (Wu and Zhang, 2008).”
• I-TASSER: a unified platform for automated protein structure and function prediction (2010, background): “; and (3) sequence-based contact predictions from SVMSE”
• Predicting membrane protein contacts from non-membrane proteins by deep transfer learning (2017, unknown): “Supervised learning predicts contacts from a variety of information, e.g., SVMSEQ[13], CMAPpro [14], PconsC2 [11] and PconsC3 [15], MetaPSICOV [16], PhyCMAP [17] and CoinDCA-NN [12].”
• Template-based and free modeling of I-TASSER and QUARK pipelines using predicted contact maps in CASP12 (2017, background): “23”