Predicting backbone Cα angles and dihedrals from protein sequences by stacked sparse auto‐encoder deep neural network

By James G. Lyons, Abdollah Dehzangi, Rhys Heffernan, Alok Sharma, Kuldip K. Paliwal, Abdul Sattar, Yaoqi Zhou, Yuedong Yang. 2014

152 citations recorded by the source corpus.

Subfields: Materials Chemistry, Molecular Biology

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Citations in the corpus

End-to-end differentiable learning of protein structure (2018, background): “In contrast, use of deep learning for structure prediction has so far been restricted to individual components within a larger pipeline (Aydin et al., 2012; Gao et al., 2017; Li et al., 2017; Lyons et al., 2014), for example prediction of contact maps (Liu et al.”
Deep learning for computational biology (2016, positive): “For example, sparse autoencoders have been applied to classify cancer cases using gene expression profiles (Fakoor et al , 2013) or to predict protein backbones (Lyons et al , 2014).”
Highly accurate sequence-based prediction of half-sphere exposures of amino acid residues in proteins (2016, methodology): “Here, sine and cosine of angles were employed to remove the effect of angle periodicity (Lyons et al., 2014).”
RaptorX-Angle: real-value prediction of protein backbone dihedral angles through a hybrid method of clustering and deep learning (2018, methodology): “Zhou et al. had developed SPIDER2 [27] to predict real-valued angles and then separately SPIDER2-Delta [35] to predict error of those predicted structural properties.”
UniCon3D: de novo protein structure prediction using united-residue conformational search via stepwise, probabilistic sampling (2016, methodology): “Furthermore, we use SPIDER2 (Heffernan et al., 2015; Lyons et al., 2014) to predict the virtual backbone angles from sequence using machine learning in order to compare MAE of sampled decoy to sequence-based prediction.”
Deep learning in bioinformatics (2016, methodology): “Omics sequeuncing data (DNA-seq, RNA-seq, ChIP-seq) features from genomic sequence position specific scoring matrix (PSSM) physicochemical properties (steric parameter, volume) Atchley factors (FAC) 1-dimensional structural properties contact map (distance of amino acid pairs in 3D structure) microa”
A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists (2017, methodology): “Stacked autoencoders, DBNs, and LSTM could predict protein structures directly from their sequences with much better accuracy than conventional methods (Heffernan et al., 2015; Lyons et al., 2014; Sønderby et al., 2015; Spencer et al., 2015).”