Analysis and prediction of DNA-binding proteins and their binding residues based on composition, sequence and structural information

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Performance of mutation pathogenicity prediction methods on missense variants (2011, background): “Function-related attributes include predictions of DNA-binding residues [Ahmad et al., 2004], catalytic residues, calmodulin-binding targets [Radivojac et al., 2006], and posttranslational modification sites [Daily et al., 2005; Iakoucheva et al., 2004; Radivojac et al., 2010].”
BindN: a web-based tool for efficient prediction of DNA and RNA binding sites in amino acid sequences (2006, methodology): “Recently, artificial neural networks have been trained with sequence information and residue solvent accessibility for prediction of DNA-binding residues (8).”
A Unified Multitask Architecture for Predicting Local Protein Properties (2012, methodology): “[31] described predictors based on sequence composition and predicted solvent accessibility.”
RNA-protein binding motifs mining with a new hybrid deep learning based cross-domain knowledge integration approach (2016, neutral): “Similar strategy is also applied in DNA-protein binding sites prediction [13].”